From f3d853b15f1bf28102a623f145846cb2df638d9c Mon Sep 17 00:00:00 2001 From: jennyyzxu Date: Thu, 5 Dec 2024 13:16:52 -0800 Subject: [PATCH 1/4] quarto demo notebook --- .gitignore | 3 + imfp_demo.qmd | 211 ++++++++++++++++++++++++++++++++++++ imfp_demo.quarto_ipynb | 183 +++++++++++++++++++++++++++++++ index.qmd | 1 + poetry.lock | 238 ++++++++++++++++++++++++++++++++++++++++- pyproject.toml | 4 + 6 files changed, 638 insertions(+), 2 deletions(-) create mode 100644 imfp_demo.qmd create mode 100644 imfp_demo.quarto_ipynb diff --git a/.gitignore b/.gitignore index d39267b..8d518f4 100644 --- a/.gitignore +++ b/.gitignore @@ -4,6 +4,9 @@ docs # VSCode files .vscode +# demo files +# data/ + # Byte-compiled / optimized / DLL files *__pycache__/ *.py[cod] diff --git a/imfp_demo.qmd b/imfp_demo.qmd new file mode 100644 index 0000000..dc284ea --- /dev/null +++ b/imfp_demo.qmd @@ -0,0 +1,211 @@ +--- +title: "imfp demo" +--- + +# imfp demo +This data analysis project aims to explore the relationship between economic growth and gender equality using `imfp`, which allows us to download data from IMF (International Monetary Fund). + +In this project, we explored the following: + +1. **Data Fetching** +* Make API call to fetch 4 datasets: GII (Gender Inequality Index), Nominal GDP, GDP Deflator Index, Population series + +2. **Feature Engineering** +* Cleaning: Convert GDP Deflator Index to a yearly basis and variables to numeric +* Dependent Variable: Percent Change of Gender Inequality Index +* Independent Variable: Percent Change of Real GDP per Capita +* Transform variables to display magnitude of change +* Merge the datasets + +3. **Data Visualization** +* Scatterplot +* Time Series Line Plots +* Barplot +* Boxplot +* Heatmap + +4. **Statistical Analysis** +* Descriptive Statistics +* Regression Analysis +* Time Series Analysis + +Ready for some insights about if a stronger economy mean more equal opportunities for all genders? Let’s dive in! + +## Suggested packages +`imfp` can be integrated with other python tools to streamline the computational process. + +`pandas`: view and manipulate data frame +`matplotlib.pyplot`: make plots +`seaborn`: make plots +`numpy`: computation +`LinearRegression`: implement linear regression +`tabulate`: format data into tables + +utils is a a custom module that contains helper functions, reusable code, or general-purpose tools that simplify certain tasks. `load_or_fetch_databases`, `load_or_fetch_parameters` `load_or_fetch_dataset` are used to load and retreive database, parameters, and dataset from a local or remote source. `view_dataframe_in_browser` displays dataframe in a web browser. + +`statsmodels.api`, `adfuller`, `ARIMA`,`VAR`,`plot_acf`,`plot_pacf`,`mean_absolute_error`,`mean_squared_error`, and`grangercausalitytests` are specifically used for time series analysis. + +```{python} +#| echo: false +import os +import pickle +from tempfile import NamedTemporaryFile +import pandas as pd +import imfp +import webbrowser + + +# Function to display a DataFrame in a web browser +def view_dataframe_in_browser(df): + html = df.to_html() + with NamedTemporaryFile(delete=False, mode="w", suffix=".html") as f: + url = "file://" + f.name + f.write(html) + webbrowser.open(url) + + +# Function to load databases from CSV or fetch from API +def load_or_fetch_databases(): + csv_path = os.path.join("data", "databases.csv") + + # Try to load from CSV + if os.path.exists(csv_path): + try: + return pd.read_csv(csv_path) + except Exception as e: + print(f"Error loading CSV: {e}") + + # If CSV doesn't exist or couldn't be loaded, fetch from API + print("Fetching databases from IMF API...") + databases = imfp.imf_databases() + + # Save to CSV for future use + databases.to_csv(csv_path, index=False) + print(f"Databases saved to {csv_path}") + + return databases + + +def load_or_fetch_parameters(database_name): + pickle_path = os.path.join("data", f"{database_name}.pickle") + + # Try to load from pickle file + if os.path.exists(pickle_path): + try: + with open(pickle_path, "rb") as f: + return pickle.load(f) + except Exception as e: + print(f"Error loading pickle file: {e}") + + # If pickle doesn't exist or couldn't be loaded, fetch from API + print(f"Fetching parameters for {database_name} from IMF API...") + parameters = imfp.imf_parameters(database_name) + + # Save to pickle file for future use + os.makedirs("data", exist_ok=True) # Ensure the data directory exists + with open(pickle_path, "wb") as f: + pickle.dump(parameters, f) + print(f"Parameters saved to {pickle_path}") + + return parameters + + +def load_or_fetch_dataset(database_id, indicator): + file_name = f"{database_id}.{indicator}.csv" + csv_path = os.path.join("data", file_name) + + # Try to load from CSV file + if os.path.exists(csv_path): + try: + return pd.read_csv(csv_path) + except Exception as e: + print(f"Error loading CSV file: {e}") + + # If CSV doesn't exist or couldn't be loaded, fetch from API + print(f"Fetching dataset for {database_id}.{indicator} from IMF API...") + dataset = imfp.imf_dataset(database_id=database_id, indicator=[indicator]) + + # Save to CSV file for future use + os.makedirs("data", exist_ok=True) # Ensure the data directory exists + dataset.to_csv(csv_path, index=False) + print(f"Dataset saved to {csv_path}") + + return dataset + +``` + +```{python} +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns +import numpy as np +from sklearn.linear_model import LinearRegression +from tabulate import tabulate +import statsmodels.api as sm +from statsmodels.tsa.stattools import adfuller +from statsmodels.tsa.arima.model import ARIMA +from statsmodels.tsa.vector_ar.var_model import VAR +from statsmodels.graphics.tsaplots import plot_acf, plot_pacf +from sklearn.metrics import mean_absolute_error +from sklearn.metrics import mean_squared_error +from statsmodels.tsa.stattools import grangercausalitytests +``` + +## Data Fetching + +```{python} +# Load or fetch databases +databases = load_or_fetch_databases() + +# Filter out databases that contain a year in the description +databases[ + ~databases['description'].str.contains(r"[\d]{4}", regex=True) +] + +# view_dataframe_in_browser(databases) +``` + + +```{python} +datasets = ["GENDER_EQUALITY", "IFS"] +params = {} + +# Fetch valid parameters for two datasets +for dataset in datasets: + params[dataset] = load_or_fetch_parameters(dataset) + + valid_keys = list(params[dataset].keys()) + print(f"Parameters for {dataset}: ", valid_keys) + +# view_dataframe_in_browser(params.get("IFS").get("indicator")) +``` + + +```{python} +datasets = {} +dsets = [("GENDER_EQUALITY", "GE_GII"), ("IFS", "NGDP_D_SA_IX"), ("IFS", "NGDP_XDC"), ("IFS", "LP_PE_NUM")] + +for dset in dsets: + datasets[dset[0] + "." + dset[1]] = load_or_fetch_dataset(dset[0], dset[1]) +``` + + +```{python} +# "Gender Inequality Index" +GII = "GENDER_EQUALITY.GE_GII" + +# "Gross Domestic Product, Deflator, Seasonally Adjusted, Index" +GDP_deflator = "IFS.NGDP_D_SA_IX" + +# "Gross Domestic Product, Nominal, Domestic Currency" +GDP_nominal = "IFS.NGDP_XDC" + +# "Population, Persons, Number of" +GDP_population = "IFS.LP_PE_NUM" + +# Assign the datasets to new variables so we don't change the originals +GII_data = datasets[GII] +GDP_deflator_data = datasets[GDP_deflator] +GDP_nominal_data = datasets[GDP_nominal] +GDP_population_data = datasets[GDP_population] +``` \ No newline at end of file diff --git a/imfp_demo.quarto_ipynb b/imfp_demo.quarto_ipynb new file mode 100644 index 0000000..98ff525 --- /dev/null +++ b/imfp_demo.quarto_ipynb @@ -0,0 +1,183 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "title: \"imfp demo\"\n", + "---\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "# imfp demo\n", + "This data analysis project aims to explore the relationship between economic growth and gender equality using `imfp`, which allows us to download data from IMF (International Monetary Fund).\n", + "\n", + "In this project, we explored the following:\n", + "\n", + "1. **Data Fetching**\n", + "* Make API call to fetch 4 datasets: GII (Gender Inequality Index), Nominal GDP, GDP Deflator Index, Population series\n", + "\n", + "2. **Feature Engineering**\n", + "* Cleaning: Convert GDP Deflator Index to a yearly basis and variables to numeric\n", + "* Dependent Variable: Percent Change of Gender Inequality Index\n", + "* Independent Variable: Percent Change of Real GDP per Capita \n", + "* Transform variables to display magnitude of change \n", + "* Merge the datasets\n", + "\n", + "3. **Data Visualization**\n", + "* Scatterplot\n", + "* Time Series Line Plots\n", + "* Barplot\n", + "* Boxplot\n", + "* Heatmap\n", + "\n", + "4. **Statistical Analysis**\n", + "* Descriptive Statistics\n", + "* Regression Analysis\n", + "* Time Series Analysis\n", + "\n", + "Ready for some insights about if a stronger economy mean more equal opportunities for all genders? Let’s dive in!\n", + "\n", + "## Suggested packages\n", + "`imfp` can be integrated with other python tools to streamline the computational process.\n", + "\n", + "`pandas`: view and manipulate data frame\n", + "`matplotlib.pyplot`: make plots\n", + "`seaborn`: make plots\n", + "`numpy`: computation\n", + "`LinearRegression`: implement linear regression\n", + "`tabulate`: format data into tables\n", + "\n", + "utils is a a custom module that contains helper functions, reusable code, or general-purpose tools that simplify certain tasks. `load_or_fetch_databases`, `load_or_fetch_parameters` `load_or_fetch_dataset` are used to load and retreive database, parameters, and dataset from a local or remote source. `view_dataframe_in_browser` displays dataframe in a web browser.\n", + "\n", + "`statsmodels.api`, `adfuller`, `ARIMA`,`VAR`,`plot_acf`,`plot_pacf`,`mean_absolute_error`,`mean_squared_error`, and`grangercausalitytests` are specifically used for time series analysis. \n" + ], + "id": "6c95637f" + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "import pandas as pd\n", + "from utils import load_or_fetch_databases, view_dataframe_in_browser\n", + "from utils import load_or_fetch_parameters\n", + "from utils import load_or_fetch_dataset\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import numpy as np\n", + "from sklearn.linear_model import LinearRegression\n", + "from tabulate import tabulate\n", + "import statsmodels.api as sm\n", + "from statsmodels.tsa.stattools import adfuller\n", + "from statsmodels.tsa.arima.model import ARIMA\n", + "from statsmodels.tsa.vector_ar.var_model import VAR\n", + "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n", + "from sklearn.metrics import mean_absolute_error\n", + "from sklearn.metrics import mean_squared_error\n", + "from statsmodels.tsa.stattools import grangercausalitytests" + ], + "id": "53b15ef5", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Fetching\n" + ], + "id": "fb369804" + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "# Load or fetch databases\n", + "databases = load_or_fetch_databases()\n", + "\n", + "# Filter out databases that contain a year in the description\n", + "databases[\n", + " ~databases['description'].str.contains(r\"[\\d]{4}\", regex=True)\n", + "]\n", + "\n", + "# view_dataframe_in_browser(databases)" + ], + "id": "b837d8e2", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "datasets = [\"GENDER_EQUALITY\", \"IFS\"]\n", + "params = {}\n", + "\n", + "# Fetch valid parameters for two datasets\n", + "for dataset in datasets:\n", + " params[dataset] = load_or_fetch_parameters(dataset)\n", + "\n", + " valid_keys = list(params[dataset].keys())\n", + " print(f\"Parameters for {dataset}: \", valid_keys)\n", + "\n", + "# view_dataframe_in_browser(params.get(\"IFS\").get(\"indicator\"))" + ], + "id": "1b358f02", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "datasets = {}\n", + "dsets = [(\"GENDER_EQUALITY\", \"GE_GII\"), (\"IFS\", \"NGDP_D_SA_IX\"), (\"IFS\", \"NGDP_XDC\"), (\"IFS\", \"LP_PE_NUM\")]\n", + "\n", + "for dset in dsets:\n", + " datasets[dset[0] + \".\" + dset[1]] = load_or_fetch_dataset(dset[0], dset[1])" + ], + "id": "59b2dd78", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "# \"Gender Inequality Index\"\n", + "GII = \"GENDER_EQUALITY.GE_GII\"\n", + "\n", + "# \"Gross Domestic Product, Deflator, Seasonally Adjusted, Index\"\n", + "GDP_deflator = \"IFS.NGDP_D_SA_IX\"\n", + "\n", + "# \"Gross Domestic Product, Nominal, Domestic Currency\"\n", + "GDP_nominal = \"IFS.NGDP_XDC\"\n", + "\n", + "# \"Population, Persons, Number of\"\n", + "GDP_population = \"IFS.LP_PE_NUM\"\n", + "\n", + "# Assign the datasets to new variables so we don't change the originals\n", + "GII_data = datasets[GII]\n", + "GDP_deflator_data = datasets[GDP_deflator]\n", + "GDP_nominal_data = datasets[GDP_nominal]\n", + "GDP_population_data = datasets[GDP_population]" + ], + "id": "f7eaee43", + "execution_count": null, + "outputs": [] + } + ], + "metadata": { + "kernelspec": { + "name": "python3", + "language": "python", + "display_name": "Python 3 (ipykernel)", + "path": "/Users/sammi/Library/Caches/pypoetry/virtualenvs/imfp-0AlIckOY-py3.13/share/jupyter/kernels/python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/index.qmd b/index.qmd index bb9ec7b..3019f9e 100644 --- a/index.qmd +++ b/index.qmd @@ -2,6 +2,7 @@ title: "imfp" --- + # imfp [![Tests](https://github.com/chriscarrollsmith/imfp/actions/workflows/main.yml/badge.svg)](https://github.com/chriscarrollsmith/imfp/actions/workflows/main.yml) diff --git a/poetry.lock b/poetry.lock index cee7d4d..c17112e 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. 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"^24.3.0" pytest-mock = "^3.10.0" jupyter = "^1.1.1" seaborn = "^0.13.2" +quarto = "^0.1.0" +scikit-learn = "^1.5.2" [build-system] requires = ["poetry-core"] From fef154b8754ab180aeb56618f8330fd3dc4191d9 Mon Sep 17 00:00:00 2001 From: jennyyzxu Date: Thu, 5 Dec 2024 13:18:56 -0800 Subject: [PATCH 2/4] quarto demo notebook --- .gitignore | 2 +- demo/imfp_demo.qmd | 710 +++++++++++++++++++++++++++++++++++++++++ imfp_demo.qmd | 211 ------------ imfp_demo.quarto_ipynb | 183 ----------- 4 files changed, 711 insertions(+), 395 deletions(-) create mode 100644 demo/imfp_demo.qmd delete mode 100644 imfp_demo.qmd delete mode 100644 imfp_demo.quarto_ipynb diff --git a/.gitignore b/.gitignore index 8d518f4..6edcba1 100644 --- a/.gitignore +++ b/.gitignore @@ -5,7 +5,7 @@ docs .vscode # demo files -# data/ +data/ # Byte-compiled / optimized / DLL files *__pycache__/ diff --git a/demo/imfp_demo.qmd b/demo/imfp_demo.qmd new file mode 100644 index 0000000..5c5d460 --- /dev/null +++ b/demo/imfp_demo.qmd @@ -0,0 +1,710 @@ +--- +title: "imfp demo" +--- + +This data analysis project aims to explore the relationship between economic growth and gender equality using `imfp`, which allows us to download data from IMF (International Monetary Fund). `imfp` can be integrated with other python tools to streamline the computational process. To demonstrate its functionality, the project experimented with a variety of visualization and analysis methods. + +Ready for some insights about if a stronger economy mean more equal opportunities for all genders? Let’s dive in! + +## Executive Summary + +In this project, we explored the following: + +1. **Data Fetching** +* Make API call to fetch 4 datasets: GII (Gender Inequality Index), Nominal GDP, GDP Deflator Index, Population series + +2. **Feature Engineering** +* Cleaning: Convert GDP Deflator Index to a yearly basis and variables to numeric +* Dependent Variable: Percent Change of Gender Inequality Index +* Independent Variable: Percent Change of Real GDP per Capita +* Transform variables to display magnitude of change +* Merge the datasets + +3. **Data Visualization** +* Scatterplot +* Time Series Line Plots +* Barplot +* Boxplot +* Heatmap + +4. **Statistical Analysis** +* Descriptive Statistics +* Regression Analysis +* Time Series Analysis + +## Suggested packages +`pandas`: view and manipulate data frame; +`matplotlib.pyplot`: make plots; +`seaborn`: make plots; +`numpy`: computation; +`LinearRegression`: implement linear regression; +`tabulate`: format data into tables; + +A custom module is written to simplify the process of making API calls and fetching information with imfp library. `load_or_fetch_databases`, `load_or_fetch_parameters` `load_or_fetch_dataset` load and retreive database, parameters, and dataset from a local or remote source. `view_dataframe_in_browser` displays dataframe in a web browser. + +`statsmodels.api`, `adfuller`, `ARIMA`,`VAR`,`plot_acf`,`plot_pacf`,`mean_absolute_error`,`mean_squared_error`, and`grangercausalitytests` are specifically used for time series analysis. + +```{python} +import os +import pickle +from tempfile import NamedTemporaryFile +import pandas as pd +import imfp +import webbrowser + + +# Function to display a DataFrame in a web browser +def view_dataframe_in_browser(df): + html = df.to_html() + with NamedTemporaryFile(delete=False, mode="w", suffix=".html") as f: + url = "file://" + f.name + f.write(html) + webbrowser.open(url) + + +# Function to load databases from CSV or fetch from API +def load_or_fetch_databases(): + csv_path = os.path.join("data", "databases.csv") + + # Try to load from CSV + if os.path.exists(csv_path): + try: + return pd.read_csv(csv_path) + except Exception as e: + print(f"Error loading CSV: {e}") + + # If CSV doesn't exist or couldn't be loaded, fetch from API + print("Fetching databases from IMF API...") + databases = imfp.imf_databases() + + # Save to CSV for future use + databases.to_csv(csv_path, index=False) + print(f"Databases saved to {csv_path}") + + return databases + + +def load_or_fetch_parameters(database_name): + pickle_path = os.path.join("data", f"{database_name}.pickle") + + # Try to load from pickle file + if os.path.exists(pickle_path): + try: + with open(pickle_path, "rb") as f: + return pickle.load(f) + except Exception as e: + print(f"Error loading pickle file: {e}") + + # If pickle doesn't exist or couldn't be loaded, fetch from API + print(f"Fetching parameters for {database_name} from IMF API...") + parameters = imfp.imf_parameters(database_name) + + # Save to pickle file for future use + os.makedirs("data", exist_ok=True) # Ensure the data directory exists + with open(pickle_path, "wb") as f: + pickle.dump(parameters, f) + print(f"Parameters saved to {pickle_path}") + + return parameters + + +def load_or_fetch_dataset(database_id, indicator): + file_name = f"{database_id}.{indicator}.csv" + csv_path = os.path.join("data", file_name) + + # Try to load from CSV file + if os.path.exists(csv_path): + try: + return pd.read_csv(csv_path) + except Exception as e: + print(f"Error loading CSV file: {e}") + + # If CSV doesn't exist or couldn't be loaded, fetch from API + print(f"Fetching dataset for {database_id}.{indicator} from IMF API...") + dataset = imfp.imf_dataset(database_id=database_id, indicator=[indicator]) + + # Save to CSV file for future use + os.makedirs("data", exist_ok=True) # Ensure the data directory exists + dataset.to_csv(csv_path, index=False) + print(f"Dataset saved to {csv_path}") + + return dataset + +``` + +```{python} +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns +import numpy as np +from sklearn.linear_model import LinearRegression +from tabulate import tabulate +import statsmodels.api as sm +from statsmodels.tsa.stattools import adfuller +from statsmodels.tsa.arima.model import ARIMA +from statsmodels.tsa.vector_ar.var_model import VAR +from statsmodels.graphics.tsaplots import plot_acf, plot_pacf +from sklearn.metrics import mean_absolute_error +from sklearn.metrics import mean_squared_error +from statsmodels.tsa.stattools import grangercausalitytests +``` + +## Data Fetching +In this section, we extracted four datasets through API calls: Gender Inequality Index(GII), GDP Deflator, Nominal GDP, and Population. Two databases were used: Gender Equality and International Financial Statistics (IFS). + +```{python} +from pathlib import Path +Path("data").mkdir(exist_ok=True) +``` + +```{python} +# Load or fetch databases +databases = load_or_fetch_databases() + +# Filter out databases that contain a year in the description +databases[ + ~databases['description'].str.contains(r"[\d]{4}", regex=True) +] + +# view_dataframe_in_browser(databases) +``` + + +```{python} +datasets = ["GENDER_EQUALITY", "IFS"] +params = {} + +# Fetch valid parameters for two datasets +for dataset in datasets: + params[dataset] = load_or_fetch_parameters(dataset) + + valid_keys = list(params[dataset].keys()) + print(f"Parameters for {dataset}: ", valid_keys) + +# view_dataframe_in_browser(params.get("IFS").get("indicator")) +``` + + +```{python} +datasets = {} +dsets = [("GENDER_EQUALITY", "GE_GII"), ("IFS", "NGDP_D_SA_IX"), ("IFS", "NGDP_XDC"), ("IFS", "LP_PE_NUM")] + +for dset in dsets: + datasets[dset[0] + "." + dset[1]] = load_or_fetch_dataset(dset[0], dset[1]) +``` + + +```{python} +# "Gender Inequality Index" +GII = "GENDER_EQUALITY.GE_GII" + +# "Gross Domestic Product, Deflator, Seasonally Adjusted, Index" +GDP_deflator = "IFS.NGDP_D_SA_IX" + +# "Gross Domestic Product, Nominal, Domestic Currency" +GDP_nominal = "IFS.NGDP_XDC" + +# "Population, Persons, Number of" +GDP_population = "IFS.LP_PE_NUM" + +# Assign the datasets to new variables so we don't change the originals +GII_data = datasets[GII] +GDP_deflator_data = datasets[GDP_deflator] +GDP_nominal_data = datasets[GDP_nominal] +GDP_population_data = datasets[GDP_population] +``` + +## Feature Engineering +### Data Cleaning +Since the GDP deflator is reported on a quarterly basis, we need to convert it to a yearly basis. + + +```{python} +# Filter to keep only rows with a partial string match for "Q4" in the time_period column +GDP_deflator_data = GDP_deflator_data[GDP_deflator_data['time_period'].str.contains("Q4")] + +``` + +```{python} +# Split the time_period into year and quarter and keep the year only +GDP_deflator_data.loc[:, 'time_period'] = GDP_deflator_data['time_period'].str[0:4] + +``` + + +```{python} +datasets = [GII_data, GDP_deflator_data, GDP_nominal_data, GDP_population_data] + +for i, dataset in enumerate(datasets): + # Use .loc to modify the columns + datasets[i].loc[:, 'obs_value'] = pd.to_numeric(datasets[i]['obs_value'], errors='coerce') + datasets[i].loc[:, 'time_period'] = pd.to_numeric(datasets[i]['time_period'], errors='coerce') + datasets[i].loc[:, 'unit_mult'] = pd.to_numeric(datasets[i]['unit_mult'], errors='coerce') +``` + +```{python} +# Calculate percent change for each ref_area +GII_data[f'pct_change'] = GII_data.sort_values(['ref_area', 'time_period']).groupby('ref_area')['obs_value'].pct_change() + +# Display the first few rows of the updated dataset +GII_data.head() +``` + +```{python} +# Create a new dataframe with only the required columns +GII_data = GII_data[['ref_area', 'time_period', 'pct_change']].copy() + +# Display the first few rows of the new dataset +GII_data.head() +``` + + + +```{python} +# GDP Deflator Dataset +# Create a new dataframe with only the required columns +GDP_deflator_data = GDP_deflator_data[['ref_area', 'time_period', 'unit_mult', 'obs_value']].copy() + +# Divide obs_value by 100 +GDP_deflator_data['obs_value'] = GDP_deflator_data['obs_value'] / 100 + +# Display the first few rows of the new dataset +GDP_deflator_data.head() +``` + +```{python} +# GDP Nominal Data +# Create a new dataframe with only the required columns +GDP_nominal_data = GDP_nominal_data[['ref_area', 'time_period', 'unit_mult','obs_value']].copy() + +# Display the first few rows of the new dataset +GDP_nominal_data.head() +``` + +```{python} +# GDP Population Data +# Create a new dataframe with only the required columns +GDP_population_data = GDP_population_data[['ref_area', 'time_period', 'unit_mult','obs_value']].copy() + +# Display the first few rows of the new dataset +GDP_population_data.head() +``` + + +```{python} +# Combine all the datasets above for further calculation +merged_df = pd.merge(pd.merge(GDP_deflator_data,GDP_nominal_data, on=['time_period', 'ref_area'], suffixes=('_index', '_nominal'), how='inner'), GDP_population_data, on=['time_period', 'ref_area'], how='inner') + +# Display the GDP data in a web browser +# view_dataframe_in_browser(merged_df) + +# Display the first few rows of the dataset +merged_df.head() +``` + + +```{python} +merged_df['adjusted_index'] = merged_df['obs_value_index'] * (10 ** (merged_df['unit_mult_index'])) +merged_df['adjusted_nominal'] = merged_df['obs_value_nominal'] * (10 ** (merged_df['unit_mult_nominal'])) +merged_df['adjusted_population'] = merged_df['obs_value'] * (10 ** (merged_df['unit_mult'])) +``` + + +```{python} +# Merged dataset +# Create a new dataframe with only the required columns +merged_df = merged_df[['ref_area', 'time_period','adjusted_nominal', 'adjusted_index', 'adjusted_population']].copy() + +# Display the first few rows of the dataset +merged_df.head() +``` + + +```{python} +# Step 1: Real GDP = (Nominal GDP / GDP Deflator Index) +merged_df['Real_GDP_domestic'] = (merged_df['adjusted_nominal'] / merged_df['adjusted_index']) + +# Step 2: Real GDP per Capita = Real GDP / Population +merged_df['Real_GDP_per_capita'] = merged_df['Real_GDP_domestic'] / merged_df['adjusted_population'] + +# Check the results +merged_df.head() +``` + + +```{python} +# Calculate percent change for each ref_area +merged_df[f'GDP_change'] = merged_df.sort_values(['ref_area', 'time_period']).groupby('ref_area')['Real_GDP_per_capita'].pct_change() + +# Rename dataset +GDP_data = merged_df + +# Display the first few rows of the dataset +GDP_data.head() +``` + + +```{python} +# GII and GDP +# Merge the datasets +combined_data = pd.merge(GII_data, GDP_data, on=["ref_area", "time_period"], how = "inner") + +# Check the combined dataset +combined_data.head() +``` + + +```{python} +# GII and GDP +# Rename columns +combined_data = combined_data.rename(columns = { + "ref_area": "Country", + "time_period": "Time", + "pct_change": "GII_change" +}) + +# Display the GDP data in a web browser +# view_dataframe_in_browser(combined_data) + +# Check the combined dataset +combined_data.head() +``` + +## Data Visualization +### Scatterplot +Scatterplot use dots to represent values of two numeric variables. The horizontal axis is the percent change in Real GDP per capita. The vertical axis is the percent change in Gender Inequality Index(GII). Different colors represent different countries. We used a linear regression line to display the overall pattern. + +Based on the scatterplot, it seems like there is a slight positive relationship between GDP change and GII change as shown by the flat regression line. Gender inequality is decreasing (gender equality is improving) a little faster in country-years with low GDP growth and a little slower in country-years with high GDP growth. + +```{python} +# Drop NAs +combined_data = combined_data.dropna(subset=['GDP_change', 'GII_change']) + +# Plot the data points +plt.figure(figsize=(18, 10)) +for country in combined_data['Country'].unique(): + country_data = combined_data[combined_data['Country'] == country] + plt.scatter(country_data['GDP_change'], country_data['GII_change'], + marker='o',linestyle='-', label=country) +plt.title('Country-Year Analysis of GDP Change vs. GII Change') +plt.xlabel('Percent Change in Real GDP per Capita (Country-Year)') +plt.ylabel('Percent Change in GII (Country-Year)') +plt.grid(True) + +# Prepare data for linear regression +X = combined_data['GDP_change'].values.reshape(-1, 1) +y = combined_data['GII_change'].values + +# Perform linear regression +reg = LinearRegression().fit(X, y) +y_pred = reg.predict(X) + +# Plot the regression line +plt.plot(combined_data['GDP_change'], y_pred, color='red', linewidth=2) + +plt.show() + +``` + +### Time Series Line Plot +Create separate line plots for GDP change and GII change over time for a few key countries might show the trends more clearly. + +US: United States +JP: Japan +GB: United Kindom +FR: France +MX: Mexico + +Based on the line plots, we see GDP change and GII change have different patterns. For example, in Mexico, when there is a big change in real GDP per captia in 1995, the change in GII is pretty stable. +```{python} +# Time Series Line plot for a few key countries +selected_countries = ['US', 'JP', 'GB', 'FR', 'MX'] +combined_data_selected = combined_data[combined_data['Country'].isin(selected_countries)] + +# Set up the Plot Structure +fig, ax = plt.subplots(2, 1, figsize=(18, 10), sharex=True) + +# Plot change in real GDP per capita over time +sns.lineplot(data = combined_data_selected, x = "Time", y = "GDP_change", hue = "Country", ax = ax[0]) +ax[0].set_title("Percent Change in Real GDP per Capita Over Time") +ax[0].set_ylabel("Percent Change in Real GDP per Capita") + +# Plot change in GII over time +sns.lineplot(data = combined_data_selected, x = "Time", y = "GII_change", hue = "Country", ax = ax[1]) +ax[1].set_title("Percent Change in GII over Time") +ax[1].set_xlabel("Time") +ax[1].set_ylabel("GII") + +plt.tight_layout +plt.show() +``` + +### Barplot +Use a barplot to show average changes in GII and GDP percent change for each country to visualize regions where inequality is improving or worsening. + +This plot supports our previous observation how GII change seems to be not be correlated with GDP change. We also saw that, for country SI, Solvenia, there seems to be a large improvement in gender inequality. + +```{python} +# Barplot using average GII and GDP change +# Calculate average change for each country +combined_data_avg = combined_data.groupby('Country')[['GII_change','GDP_change']].mean().reset_index() + +# Prepare to plot structure +plt.figure(figsize = (18,10)) + +# Create the barplot +combined_data_avg.plot(kind = 'bar', x = 'Country') +plt.ylabel('Average Change') +plt.xlabel('Country') +plt.legend(['GII change', 'GDP change']) +plt.grid(axis = 'y') + +# Show the plot +plt.show() +``` + +### Boxplot +Use boxplot to visualize the distribution of GDP and GII change by country, providing information about spread, median, and potential outliers. To provide a more informative view, we sequence countries in an ascending order by the median of percent change in GDP. + +The boxplot displays a slight upward trend with no obvious pattern between GDP and GII change. In coutries with higher GDP change median, they also tend to have a larger spread of the GDP change. The median of GII change remains stable regardless of the magnitude of GDP change, implying weak or no association between GDP and GII change. We observe a potential outlier for country SI, Solvenia, which may explains its large improvement in Gender inequality. + +```{python} +# Box plot for GII and GDP change +# Melt the dataframe to long format for combined boxplot +combined_data_melted = combined_data.melt(id_vars=['Country'], value_vars=['GII_change', 'GDP_change'], + var_name='Change_Type', value_name='Value') + +gdp_medians = combined_data.groupby('Country')['GDP_change'].median().sort_values() + +combined_data_melted['Country'] = pd.Categorical(combined_data_melted['Country'], categories=gdp_medians.index, ordered= True) + +# Prepare the plot structure +plt.figure(figsize=(18, 10)) +sns.boxplot(data = combined_data_melted, x = "Country", y = 'Value', hue = 'Change_Type') +plt.title('Distribution of GII and GDP change by Country') +plt.xlabel('Country') +plt.ylabel('Change') +plt.legend(title = 'Change Type') + +# Show the plot +plt.show() +``` + +### Correlation Matrix +Create a heatmap to show the relationship between GII and GDP change. + +A positive correlation coefficient indicates a positive relationship: the larger the GDP change, the larger the GII change. A negative correlation coefficient indicates a negative relationship: the larger the GDP change, the smaller the GII change. A correlation coefficient closer to 0 indicates there is weak or no relationship. + +Based on the numeric values in the plot, there is a moderately strong positive correlation between GII and GDP change for country Estonia(EE) and Ireland(IE). + +```{python} +# Calculate the correlation +country_correlation = combined_data.groupby('Country')[['GII_change', 'GDP_change']].corr().iloc[0::2, -1].reset_index(name='Correlation') + +# Put the correlation value in a matrix format +correlation_matrix = country_correlation.pivot(index='Country', columns='level_1', values='Correlation') + +# Check for NaN values in the correlation matrix +correlation_matrix.fillna(0, inplace=True) # Replace NaNs with 0 or another value as appropriate + +# Set up the plot structure +plt.figure(figsize=(18, 12)) # Adjust height to give more space for y-axis labels + +# Plot the heatmap +sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0, cbar_kws={"shrink": .8}, linewidths=.5) + +# Enhance axis labels and title +plt.title('Heatmap for GII and GDP Change', fontsize=20) +plt.xlabel('Variables', fontsize=16) +plt.ylabel('Country', fontsize=16) + +# Improve readability of y-axis labels +plt.yticks(fontsize=12) # Adjust the font size for y-axis labels + +# Show the plot +plt.show() +``` + +## Statistical Analysis +### Descriptive Statistics +There is a total of 915 data points. The mean of the GII change in -0.0314868, which indicates the overall grand mean percent change in gender inequality index is -3.15%. The mean of the GDP change is 0.0234633, showing the overall grand mean percent change in real GDP per capita is 2.35%. +```{python} +# Generate summary statistics +descriptive_stats = combined_data.describe() +print(tabulate(descriptive_stats, headers='keys', tablefmt='github')) +``` + +### Regression Analysis +Simple linear regression as a foundational approach provide us with a basic understanding of the relationship between GDP change and GII change. + +Based on the summary, we can conclude the following: +* Becasue p-value = 0.057, if we set alpha, the significance level, to be 0.05, we fail to reject the null hypothesis and conclude there is no significant relationship between percent change in real GDP per capita and gender inequality index. + +* R-squared = 0.004. Only 0.4% of the variance in GII change can be explained by GDP change. + +* We are 95% confident that the interval from -0.003 to 0.169 captures the true slope of GDP change. Because 0 is included, we are uncertain about the effect of GDP change on GII chnage. + +```{python} +# Define independent and depenent variables +X = combined_data['GDP_change'] +y = combined_data['GII_change'] + +# Add a constant to indepdent variable to include an intercept +X = sm.add_constant(X) + +# Fit a simple linear regresion model and print out the summary +model = sm.OLS(y, X).fit() +print(model.summary()) +``` + +### Time Series Analysis +Time series analysis allows us to explore how the relationship between GII and GDP change vary across different time periods, accounting for lagged effects. + +Here is a quick summary of the result: +* Both GII and GDP change time series are stationary. + +* Past GII change values significantly influence cuurent GII change values. + +* VAR model has good model performance on forecasting future values based on historical data. + +* Changes in GDP does not cause/precde the changes in GII. + +#### ADF Test: Stationality Assumption Check +We want to use Augmented Dickey-Fuller (ADF) test to check whether a time series is stationary, which is the model assumption for many time series models. + +Stationarity implies constant mean and variance over time, making it more predictable and stable for forecasting. + +Based on the ADF test output, both GII and GDP change time series are stationary. We can proceed to the time series modeling section. +```{python} +# Augmented Dickey-Fuller (ADF) test for stationarity check +# Create melted datasets +combined_data_time = combined_data.melt(id_vars=['Time', 'Country'], value_vars=['GII_change','GDP_change'], var_name = 'Change_Type', value_name = 'Value') +GII = combined_data_time[(combined_data_time['Change_Type'] == 'GII_change')] + +GDP = combined_data_time[(combined_data_time['Change_Type'] == 'GDP_change')] + +# Stationary Check +def adf_test(series): + result = adfuller(series.dropna()) + print(f'ADF Statistic: {result[0]}') + print(f'p-value: {result[1]}') + if result[1] < 0.05: + print("Series is stationary") + else: + print("Series is not stationary") + +# Output the result +adf_test(GII['Value']) +adf_test(GDP['Value']) +``` + +#### VAR model: Examine variables separately +We fit a VAR (Vector Autoreression) model to see the relationship between GII and GDP change. VAR is particularly useful when dealing with multivariate time series data and allows us to examine the interdependence between variables. + +Based on summary, here are several interpretations we can make: +- We used AIC as the criteria for model selection. Lower value suggests a better fit. +- Given that we want to predict GII change, we will focus on the first set "Results for equation GII_change." +- Past GII_change values significantly influence current GII_change, as shown in the small p-values of lags 1 and 2. +- Lag 2 of GDP_change has a relatively low p-value but is not statistically significant. + +```{python} +# Split the dataset into training and testing sets +split_ratio = 0.7 +split_index = int(len(combined_data) * split_ratio) + +# Training set is used to fit the model +train_data = combined_data.iloc[:split_index] + +# Testing set is used for validation +test_data = combined_data.iloc[split_index:] + +print(f"Training data: {train_data.shape}") +print(f"Test data: {test_data.shape}") +``` + +```{python} +# Fit a VAR model +time_model = VAR(train_data[['GII_change', 'GDP_change']]) +time_model_fitted = time_model.fit(maxlags = 15, ic="aic") + +# Print out the model summary +print(time_model_fitted.summary()) +``` + +#### VAR Model: Forecasting +We apply the model learned above to the test data. Based on the plot, the forecast values seem to follow the actual data well, indicating a good model fit caputuring the underlying trends. +```{python} +# Number of steps to forecast (length of the test set) +n_steps = len(test_data) + +# Get the last values from the training set for forecasting +forecast_input = train_data[['GII_change', 'GDP_change']].values[-time_model_fitted.k_ar:] + +# Forecasting +forecast = time_model_fitted.forecast(y=forecast_input, steps=n_steps) + +# Create a DataFrame for the forecasted values +forecast_df = pd.DataFrame(forecast, index=test_data.index, columns=['GII_forecast', 'GDP_forecast']) + +# Ensure the index of the forecast_df matches the test_data index +forecast_df.index = test_data.index +``` + +```{python} +plt.figure(figsize=(12, 6)) +plt.plot(train_data['GII_change'], label='Training GII', color='blue') +plt.plot(test_data['GII_change'], label='Actual GII', color='orange') +plt.plot(forecast_df['GII_forecast'], label='Forecasted GII', color='green') +plt.title('GII Change Forecast vs Actual') +plt.legend() +plt.show() + +plt.figure(figsize=(12, 6)) +plt.plot(train_data['GDP_change'], label='Training GDP', color='blue') +plt.plot(test_data['GDP_change'], label='Actual GDP', color='orange') +plt.plot(forecast_df['GDP_forecast'], label='Forecasted GDP', color='green') +plt.title('GDP Change Forecast vs Actual') +plt.legend() +plt.show() +``` + +#### VAR Model: Model Performance +Low values of both MAE and RMSE indicate good model performance with small average errors in predictions. +```{python} +mae_gii = mean_absolute_error(test_data['GII_change'], forecast_df['GII_forecast']) +mae_gdp = mean_absolute_error(test_data['GDP_change'], forecast_df['GDP_forecast']) + +print(f'Mean Absolute Error for GII: {mae_gii}') +print(f'Mean Absolute Error for GDP: {mae_gdp}') +``` + + +```{python} +rmse_gii = np.sqrt(mean_squared_error(test_data['GII_change'], forecast_df['GII_forecast'])) +rmse_gdp = np.sqrt(mean_squared_error(test_data['GDP_change'], forecast_df['GDP_forecast'])) + +print(f'RMSE for GII: {rmse_gii}') +print(f'RMSE for GDP: {rmse_gdp}') +``` + +#### VAR Model: Granger causality test +Granger causality test evaluates whether one time series can predict another. + +Based on the output, the lowest p-value is when lag = 2. However, because p-value > 0.05, we fail to reject the null hypothesis and conclude the GDP_change does not Granger-cause the GII_change. +```{python} +# Perform the Granger causality test +max_lag = 3 +test_result = grangercausalitytests(train_data[['GII_change', 'GDP_change']], max_lag, verbose=True) + +``` + +## Conclusion +In wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research. + +## Future Directions +* Additional Predictors: Gender Inequality Index (GII) is a composite measure shaped by a mariad of social factors. What about culture, policy, and environment? +* Specific Country: Are there any specific historical contexts that sparked economic growth and improvement in gender equality? +* Opposite causation: Women entering workforce boost GDP? +* Predictive Power of VAR model: Our model performed well in forecasting trends for both variables independently. + +Let's keep our curiosity alive and our questions flowing! diff --git a/imfp_demo.qmd b/imfp_demo.qmd deleted file mode 100644 index dc284ea..0000000 --- a/imfp_demo.qmd +++ /dev/null @@ -1,211 +0,0 @@ ---- -title: "imfp demo" ---- - -# imfp demo -This data analysis project aims to explore the relationship between economic growth and gender equality using `imfp`, which allows us to download data from IMF (International Monetary Fund). - -In this project, we explored the following: - -1. **Data Fetching** -* Make API call to fetch 4 datasets: GII (Gender Inequality Index), Nominal GDP, GDP Deflator Index, Population series - -2. **Feature Engineering** -* Cleaning: Convert GDP Deflator Index to a yearly basis and variables to numeric -* Dependent Variable: Percent Change of Gender Inequality Index -* Independent Variable: Percent Change of Real GDP per Capita -* Transform variables to display magnitude of change -* Merge the datasets - -3. **Data Visualization** -* Scatterplot -* Time Series Line Plots -* Barplot -* Boxplot -* Heatmap - -4. **Statistical Analysis** -* Descriptive Statistics -* Regression Analysis -* Time Series Analysis - -Ready for some insights about if a stronger economy mean more equal opportunities for all genders? Let’s dive in! - -## Suggested packages -`imfp` can be integrated with other python tools to streamline the computational process. - -`pandas`: view and manipulate data frame -`matplotlib.pyplot`: make plots -`seaborn`: make plots -`numpy`: computation -`LinearRegression`: implement linear regression -`tabulate`: format data into tables - -utils is a a custom module that contains helper functions, reusable code, or general-purpose tools that simplify certain tasks. `load_or_fetch_databases`, `load_or_fetch_parameters` `load_or_fetch_dataset` are used to load and retreive database, parameters, and dataset from a local or remote source. `view_dataframe_in_browser` displays dataframe in a web browser. - -`statsmodels.api`, `adfuller`, `ARIMA`,`VAR`,`plot_acf`,`plot_pacf`,`mean_absolute_error`,`mean_squared_error`, and`grangercausalitytests` are specifically used for time series analysis. - -```{python} -#| echo: false -import os -import pickle -from tempfile import NamedTemporaryFile -import pandas as pd -import imfp -import webbrowser - - -# Function to display a DataFrame in a web browser -def view_dataframe_in_browser(df): - html = df.to_html() - with NamedTemporaryFile(delete=False, mode="w", suffix=".html") as f: - url = "file://" + f.name - f.write(html) - webbrowser.open(url) - - -# Function to load databases from CSV or fetch from API -def load_or_fetch_databases(): - csv_path = os.path.join("data", "databases.csv") - - # Try to load from CSV - if os.path.exists(csv_path): - try: - return pd.read_csv(csv_path) - except Exception as e: - print(f"Error loading CSV: {e}") - - # If CSV doesn't exist or couldn't be loaded, fetch from API - print("Fetching databases from IMF API...") - databases = imfp.imf_databases() - - # Save to CSV for future use - databases.to_csv(csv_path, index=False) - print(f"Databases saved to {csv_path}") - - return databases - - -def load_or_fetch_parameters(database_name): - pickle_path = os.path.join("data", f"{database_name}.pickle") - - # Try to load from pickle file - if os.path.exists(pickle_path): - try: - with open(pickle_path, "rb") as f: - return pickle.load(f) - except Exception as e: - print(f"Error loading pickle file: {e}") - - # If pickle doesn't exist or couldn't be loaded, fetch from API - print(f"Fetching parameters for {database_name} from IMF API...") - parameters = imfp.imf_parameters(database_name) - - # Save to pickle file for future use - os.makedirs("data", exist_ok=True) # Ensure the data directory exists - with open(pickle_path, "wb") as f: - pickle.dump(parameters, f) - print(f"Parameters saved to {pickle_path}") - - return parameters - - -def load_or_fetch_dataset(database_id, indicator): - file_name = f"{database_id}.{indicator}.csv" - csv_path = os.path.join("data", file_name) - - # Try to load from CSV file - if os.path.exists(csv_path): - try: - return pd.read_csv(csv_path) - except Exception as e: - print(f"Error loading CSV file: {e}") - - # If CSV doesn't exist or couldn't be loaded, fetch from API - print(f"Fetching dataset for {database_id}.{indicator} from IMF API...") - dataset = imfp.imf_dataset(database_id=database_id, indicator=[indicator]) - - # Save to CSV file for future use - os.makedirs("data", exist_ok=True) # Ensure the data directory exists - dataset.to_csv(csv_path, index=False) - print(f"Dataset saved to {csv_path}") - - return dataset - -``` - -```{python} -import pandas as pd -import matplotlib.pyplot as plt -import seaborn as sns -import numpy as np -from sklearn.linear_model import LinearRegression -from tabulate import tabulate -import statsmodels.api as sm -from statsmodels.tsa.stattools import adfuller -from statsmodels.tsa.arima.model import ARIMA -from statsmodels.tsa.vector_ar.var_model import VAR -from statsmodels.graphics.tsaplots import plot_acf, plot_pacf -from sklearn.metrics import mean_absolute_error -from sklearn.metrics import mean_squared_error -from statsmodels.tsa.stattools import grangercausalitytests -``` - -## Data Fetching - -```{python} -# Load or fetch databases -databases = load_or_fetch_databases() - -# Filter out databases that contain a year in the description -databases[ - ~databases['description'].str.contains(r"[\d]{4}", regex=True) -] - -# view_dataframe_in_browser(databases) -``` - - -```{python} -datasets = ["GENDER_EQUALITY", "IFS"] -params = {} - -# Fetch valid parameters for two datasets -for dataset in datasets: - params[dataset] = load_or_fetch_parameters(dataset) - - valid_keys = list(params[dataset].keys()) - print(f"Parameters for {dataset}: ", valid_keys) - -# view_dataframe_in_browser(params.get("IFS").get("indicator")) -``` - - -```{python} -datasets = {} -dsets = [("GENDER_EQUALITY", "GE_GII"), ("IFS", "NGDP_D_SA_IX"), ("IFS", "NGDP_XDC"), ("IFS", "LP_PE_NUM")] - -for dset in dsets: - datasets[dset[0] + "." + dset[1]] = load_or_fetch_dataset(dset[0], dset[1]) -``` - - -```{python} -# "Gender Inequality Index" -GII = "GENDER_EQUALITY.GE_GII" - -# "Gross Domestic Product, Deflator, Seasonally Adjusted, Index" -GDP_deflator = "IFS.NGDP_D_SA_IX" - -# "Gross Domestic Product, Nominal, Domestic Currency" -GDP_nominal = "IFS.NGDP_XDC" - -# "Population, Persons, Number of" -GDP_population = "IFS.LP_PE_NUM" - -# Assign the datasets to new variables so we don't change the originals -GII_data = datasets[GII] -GDP_deflator_data = datasets[GDP_deflator] -GDP_nominal_data = datasets[GDP_nominal] -GDP_population_data = datasets[GDP_population] -``` \ No newline at end of file diff --git a/imfp_demo.quarto_ipynb b/imfp_demo.quarto_ipynb deleted file mode 100644 index 98ff525..0000000 --- a/imfp_demo.quarto_ipynb +++ /dev/null @@ -1,183 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "title: \"imfp demo\"\n", - "---\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "# imfp demo\n", - "This data analysis project aims to explore the relationship between economic growth and gender equality using `imfp`, which allows us to download data from IMF (International Monetary Fund).\n", - "\n", - "In this project, we explored the following:\n", - "\n", - "1. **Data Fetching**\n", - "* Make API call to fetch 4 datasets: GII (Gender Inequality Index), Nominal GDP, GDP Deflator Index, Population series\n", - "\n", - "2. **Feature Engineering**\n", - "* Cleaning: Convert GDP Deflator Index to a yearly basis and variables to numeric\n", - "* Dependent Variable: Percent Change of Gender Inequality Index\n", - "* Independent Variable: Percent Change of Real GDP per Capita \n", - "* Transform variables to display magnitude of change \n", - "* Merge the datasets\n", - "\n", - "3. **Data Visualization**\n", - "* Scatterplot\n", - "* Time Series Line Plots\n", - "* Barplot\n", - "* Boxplot\n", - "* Heatmap\n", - "\n", - "4. **Statistical Analysis**\n", - "* Descriptive Statistics\n", - "* Regression Analysis\n", - "* Time Series Analysis\n", - "\n", - "Ready for some insights about if a stronger economy mean more equal opportunities for all genders? Let’s dive in!\n", - "\n", - "## Suggested packages\n", - "`imfp` can be integrated with other python tools to streamline the computational process.\n", - "\n", - "`pandas`: view and manipulate data frame\n", - "`matplotlib.pyplot`: make plots\n", - "`seaborn`: make plots\n", - "`numpy`: computation\n", - "`LinearRegression`: implement linear regression\n", - "`tabulate`: format data into tables\n", - "\n", - "utils is a a custom module that contains helper functions, reusable code, or general-purpose tools that simplify certain tasks. `load_or_fetch_databases`, `load_or_fetch_parameters` `load_or_fetch_dataset` are used to load and retreive database, parameters, and dataset from a local or remote source. `view_dataframe_in_browser` displays dataframe in a web browser.\n", - "\n", - "`statsmodels.api`, `adfuller`, `ARIMA`,`VAR`,`plot_acf`,`plot_pacf`,`mean_absolute_error`,`mean_squared_error`, and`grangercausalitytests` are specifically used for time series analysis. \n" - ], - "id": "6c95637f" - }, - { - "cell_type": "code", - "metadata": {}, - "source": [ - "import pandas as pd\n", - "from utils import load_or_fetch_databases, view_dataframe_in_browser\n", - "from utils import load_or_fetch_parameters\n", - "from utils import load_or_fetch_dataset\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import numpy as np\n", - "from sklearn.linear_model import LinearRegression\n", - "from tabulate import tabulate\n", - "import statsmodels.api as sm\n", - "from statsmodels.tsa.stattools import adfuller\n", - "from statsmodels.tsa.arima.model import ARIMA\n", - "from statsmodels.tsa.vector_ar.var_model import VAR\n", - "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n", - "from sklearn.metrics import mean_absolute_error\n", - "from sklearn.metrics import mean_squared_error\n", - "from statsmodels.tsa.stattools import grangercausalitytests" - ], - "id": "53b15ef5", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Fetching\n" - ], - "id": "fb369804" - }, - { - "cell_type": "code", - "metadata": {}, - "source": [ - "# Load or fetch databases\n", - "databases = load_or_fetch_databases()\n", - "\n", - "# Filter out databases that contain a year in the description\n", - "databases[\n", - " ~databases['description'].str.contains(r\"[\\d]{4}\", regex=True)\n", - "]\n", - "\n", - "# view_dataframe_in_browser(databases)" - ], - "id": "b837d8e2", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": {}, - "source": [ - "datasets = [\"GENDER_EQUALITY\", \"IFS\"]\n", - "params = {}\n", - "\n", - "# Fetch valid parameters for two datasets\n", - "for dataset in datasets:\n", - " params[dataset] = load_or_fetch_parameters(dataset)\n", - "\n", - " valid_keys = list(params[dataset].keys())\n", - " print(f\"Parameters for {dataset}: \", valid_keys)\n", - "\n", - "# view_dataframe_in_browser(params.get(\"IFS\").get(\"indicator\"))" - ], - "id": "1b358f02", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": {}, - "source": [ - "datasets = {}\n", - "dsets = [(\"GENDER_EQUALITY\", \"GE_GII\"), (\"IFS\", \"NGDP_D_SA_IX\"), (\"IFS\", \"NGDP_XDC\"), (\"IFS\", \"LP_PE_NUM\")]\n", - "\n", - "for dset in dsets:\n", - " datasets[dset[0] + \".\" + dset[1]] = load_or_fetch_dataset(dset[0], dset[1])" - ], - "id": "59b2dd78", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": {}, - "source": [ - "# \"Gender Inequality Index\"\n", - "GII = \"GENDER_EQUALITY.GE_GII\"\n", - "\n", - "# \"Gross Domestic Product, Deflator, Seasonally Adjusted, Index\"\n", - "GDP_deflator = \"IFS.NGDP_D_SA_IX\"\n", - "\n", - "# \"Gross Domestic Product, Nominal, Domestic Currency\"\n", - "GDP_nominal = \"IFS.NGDP_XDC\"\n", - "\n", - "# \"Population, Persons, Number of\"\n", - "GDP_population = \"IFS.LP_PE_NUM\"\n", - "\n", - "# Assign the datasets to new variables so we don't change the originals\n", - "GII_data = datasets[GII]\n", - "GDP_deflator_data = datasets[GDP_deflator]\n", - "GDP_nominal_data = datasets[GDP_nominal]\n", - "GDP_population_data = datasets[GDP_population]" - ], - "id": "f7eaee43", - "execution_count": null, - "outputs": [] - } - ], - "metadata": { - "kernelspec": { - "name": "python3", - "language": "python", - "display_name": "Python 3 (ipykernel)", - "path": "/Users/sammi/Library/Caches/pypoetry/virtualenvs/imfp-0AlIckOY-py3.13/share/jupyter/kernels/python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file From 517931507a40899c1cee88e1efb1b97699fe4abd Mon Sep 17 00:00:00 2001 From: jennyyzxu Date: Wed, 25 Dec 2024 18:02:38 -0800 Subject: [PATCH 3/4] Finalized the quarto notebook. Added more explanation to the code section. Added blog write up to the website. 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--- /dev/null +++ b/demo/demo/_quarto.yml @@ -0,0 +1,21 @@ +project: + type: website + +website: + title: "imfp Demo" + navbar: + left: + - href: About.qmd + text: About + - href: imfp_demo.qmd + text: Code + - href: listing.qmd + text: Blog + +format: + html: + theme: cosmo + css: styles.css + + + diff --git a/demo/demo/_site/About.html b/demo/demo/_site/About.html new file mode 100644 index 0000000..83a97f4 --- /dev/null +++ b/demo/demo/_site/About.html @@ -0,0 +1,564 @@ + + + + + + + + + +Jenny Xu – imfp Demo + + + + + + + + + + + + + + + + + + + + + + + + + + +

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    Jenny Xu

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    Hi there! My name is Jenny, and I’m a third-year student at University of California, Davis, double majoring in Statistics and Psychology. I’ve always been interested in becoming a data analyst working in tech, internet, or research industries. Interning at Promptly Technologies helped me learn a ton. A quick fun fact for me is that my MBTI is ISFJ (Defender)!

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    Jenny Xu

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    Hi there! My name is Jenny, and I’m a third-year student at University of California, Davis, double majoring in Statistics and Psychology. I’ve always been interested in becoming a data analyst working in tech, internet, or research industries. Interning at Promptly Technologies helped me learn a ton. A quick fun fact for me is that my MBTI is ISFJ (Defender)!

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100644 index 0000000..647d216 --- /dev/null +++ b/demo/demo/_site/Blog/blog/Blog.html @@ -0,0 +1,672 @@ + + + + + + + + + + + + +Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data – imfp Demo + + + + + + + + + + + + + + + + + + + + + + + + + + + +

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    Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data

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    + Apply imfp in a real world data-analysis project. +
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    Data Analysis
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    Author
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    Jenny Xu

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    December 25, 2024

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    Introduction

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    Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders?

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    Our main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables.

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    Ready for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!

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    Data Preparation

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    ​​To explore how economic growth connects with gender equality, we turned to the Imfp library to access a wealth of global economic data, ready to analyze through some straightforward API calls. Let’s break down our two main variables of interest. Here’s the lowdown on the 4 key data series we pulled together:

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    Dependent Variable: Percent Change in Gender Inequality Index (GII)

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    Our gender equality measure, the Gender Inequality Index (GII), is a composite score that reflects inequality in three key areas: reproductive health, empowerment, and labor market, ranging from 0 (equality) to 1 (inequality). We calculated the percent change in GII over time to make it easier to compare GII change across countries with different baseline levels of inequality.

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    Independent Variable: Percent Change in Real GDP per capita

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    Our economic growth measure is real GDP per capita, an indicator of a country’s economic output per person, adjusted for inflation. The higher the real GDP per capita, the wealthier and more prosperous a country is. Here is a peek at the formula:

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    Data Visualization

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    Scatterplot

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    Nothing like a good scatterplot to help us understand the relationship between two numbers, right? For our project, the scatterplot lets us visually explore how Percent Change in Real GDP per capita relates to Percent Change in GII. Each dot on the plot represents a country-year combo. The gentle upward slope suggests that in years of low GDP growth, gender inequality tends to decrease a bit faster, while in years of high GDP growth, the inequality drops at a slightly slower rate. It’s a subtle clue, but slower-growing economies might be making slightly quicker strides in gender equality.

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    Time Series Line plot

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    The time series line plot gives us the big picture of GDP and GII changes over time across a few select countries, including the United States, Japan, the United Kingdom, France, and Mexico. Mexico, for example, saw great GDP swings in 1995, yet its GII stayed relatively stable. Meanwhile, Japan and France saw some dramatic GII shifts. These fluctuations leave us wondering: what historical events sparked these changes? We may need to take a deeper look into each country’s unique timeline to find the answers.

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    Boxplot

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    Next, we have the boxplot to help us visualize the distribution. By sequencing countries in ascending order of median GDP change, we see an interesting trend: countries with higher median GDP growth also seem to show more variability in their GDP changes. The GII change, on the other hand, keeps a relatively stable median, hinting at a weaker association with GDP growth.

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    Heatmap

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    Finally, our heatmap wraps it all up with a quick, intuitive look at correlations between GDP and GII across countries. In countries like Estonia (EE) and Ireland (IE), Australia (AT), South Africa (ZA), Thailand (TH), Slovenia (SI), we see positive correlations in warm colors, indicating that as GDP growth rates increase, GII growth rates tend to rise as well. For countries like Cyprus (CY), Romania (RO), and the United States (US), we see cooler colors, representing negative correlations: as GDP growth rate rises, GII growth rates decrease, suggesting an improvement in gender equality alongside economic growth. This color-coded comparison leaves us curious—could the distinction between developed and developing countries play a role in these relationships? It’s a question that might inspire future research directions!

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    Statistical Analysis

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    Descriptive Statistics

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    Let’s start with a quick overview of our descriptive statistics: our dataset includes 915 data points. On average, GII change is -3.15%, indicating an overall reduction in gender inequality over time. Meanwhile, GDP change averages 2.35%, hinting at modest economic growth. A full breakdown of these values can be found in the table below for more detail.

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    Regression Analysis

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    To get a sense of any relationship between GDP change and GII change, we ran a simple linear regression. Here’s what we found:

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    • P-value: With a p-value of 0.057, we fail to reject the null hypothesis. There is no statistically significant relationship. In other words, we don’t find strong evidence of a link between GDP and GII changes.

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    • R-squared: GDP change explains only 0.4% of the variation in GII change—a pretty minimal effect.

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    • 95% Confidence Interval: We’re 95% confident the interval from -0.003 to 0.169 captures the true slope. The effect of GDP change on GII is uncertain as 0 is within this range.

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    Time Series Analysis

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    Time series analysis allows us to dig deeper into how GII and GDP changes play out over time—and potentially influence one another. Here’s the rundown:

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    • Stationarity check: Both GII and GDP change series are stationary, meaning their means and variances are stable over time, setting us up for reliable forecasting.

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    • VAR Model (Vector Autoregression): This multivariate model lets us explore how past values in both series relate to current values. Notably, past GII changes significantly impact current GII changes.

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    • Model Performance: The VAR model has good fit, as shown by the forecast closely tracking actual values. Low MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) confirm that our model predicts GII and GDP change with minimal error. Check out the plots and table below!

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    • Granger Causality Test: Curious whether GDP changes can predict changes in GII? The Granger causality test says no. With p-values above 0.05, we fail to find evidence that GDP changes drive GII changes. This suggests that while both variables evolve over time, they don’t seem to directly influence each other.

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    Conclusion

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    In wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research.

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    In terms of the Imfp library, we gained valuable experience working with real-world economic indicators like GDP and the GII. The library’s function in fetching databases, parameters, and datasets made it straightforward to access IMF’s data, allowing us to focus on analysis instead. The integration of other Python tools—such as seaborn, matplotlib, and numpy—not only streamlined our computational processes but also ensured consistency across the project.

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    Future Directions

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    First, we must consider what other factors might influence the relationship between GDP and GII change. The GII is a composite index, shaped by a myriad of social factors, including cultural norms, legal frameworks, and environmental shifts. Future studies could benefit from incorporating additional predictors into the analysis and exploring the interaction between economic growth and gender equality within specific country contexts.

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    Second, there’s potential to enhance the predictive power of our Vector Autoregression (VAR) time series model. While we established that GDP change does not cause GII change, our model performed well in forecasting trends for both variables independently. In practice, policymakers may want to forecast GII trends independently of GDP if they are implementing gender-focused policies. Future research could investigate time series modeling to further unravel the dynamics of GII and GDP changes.

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    So, as we wrap up this chapter, let’s keep our curiosity alive and our questions flowing. After all, every end is just a new beginning in the quest for knowledge!

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    Blog

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    + + + + + \ No newline at end of file diff --git a/demo/demo/_site/Blog/post/Blog.html b/demo/demo/_site/Blog/post/Blog.html new file mode 100644 index 0000000..647d216 --- /dev/null +++ b/demo/demo/_site/Blog/post/Blog.html @@ -0,0 +1,672 @@ + + + + + + + + + + + + +Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data – imfp Demo + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data

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    + Apply imfp in a real world data-analysis project. +
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    Data Analysis
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    Author
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    Jenny Xu

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    Published
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    December 25, 2024

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    Introduction

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    Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders?

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    Our main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables.

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    Ready for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!

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    Data Preparation

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    ​​To explore how economic growth connects with gender equality, we turned to the Imfp library to access a wealth of global economic data, ready to analyze through some straightforward API calls. Let’s break down our two main variables of interest. Here’s the lowdown on the 4 key data series we pulled together:

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    Dependent Variable: Percent Change in Gender Inequality Index (GII)

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    Our gender equality measure, the Gender Inequality Index (GII), is a composite score that reflects inequality in three key areas: reproductive health, empowerment, and labor market, ranging from 0 (equality) to 1 (inequality). We calculated the percent change in GII over time to make it easier to compare GII change across countries with different baseline levels of inequality.

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    Independent Variable: Percent Change in Real GDP per capita

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    Our economic growth measure is real GDP per capita, an indicator of a country’s economic output per person, adjusted for inflation. The higher the real GDP per capita, the wealthier and more prosperous a country is. Here is a peek at the formula:

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    Data Visualization

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    Scatterplot

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    Nothing like a good scatterplot to help us understand the relationship between two numbers, right? For our project, the scatterplot lets us visually explore how Percent Change in Real GDP per capita relates to Percent Change in GII. Each dot on the plot represents a country-year combo. The gentle upward slope suggests that in years of low GDP growth, gender inequality tends to decrease a bit faster, while in years of high GDP growth, the inequality drops at a slightly slower rate. It’s a subtle clue, but slower-growing economies might be making slightly quicker strides in gender equality.

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    Time Series Line plot

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    The time series line plot gives us the big picture of GDP and GII changes over time across a few select countries, including the United States, Japan, the United Kingdom, France, and Mexico. Mexico, for example, saw great GDP swings in 1995, yet its GII stayed relatively stable. Meanwhile, Japan and France saw some dramatic GII shifts. These fluctuations leave us wondering: what historical events sparked these changes? We may need to take a deeper look into each country’s unique timeline to find the answers.

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    Boxplot

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    Next, we have the boxplot to help us visualize the distribution. By sequencing countries in ascending order of median GDP change, we see an interesting trend: countries with higher median GDP growth also seem to show more variability in their GDP changes. The GII change, on the other hand, keeps a relatively stable median, hinting at a weaker association with GDP growth.

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    Heatmap

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    Finally, our heatmap wraps it all up with a quick, intuitive look at correlations between GDP and GII across countries. In countries like Estonia (EE) and Ireland (IE), Australia (AT), South Africa (ZA), Thailand (TH), Slovenia (SI), we see positive correlations in warm colors, indicating that as GDP growth rates increase, GII growth rates tend to rise as well. For countries like Cyprus (CY), Romania (RO), and the United States (US), we see cooler colors, representing negative correlations: as GDP growth rate rises, GII growth rates decrease, suggesting an improvement in gender equality alongside economic growth. This color-coded comparison leaves us curious—could the distinction between developed and developing countries play a role in these relationships? It’s a question that might inspire future research directions!

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    Statistical Analysis

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    Descriptive Statistics

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    Let’s start with a quick overview of our descriptive statistics: our dataset includes 915 data points. On average, GII change is -3.15%, indicating an overall reduction in gender inequality over time. Meanwhile, GDP change averages 2.35%, hinting at modest economic growth. A full breakdown of these values can be found in the table below for more detail.

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    Regression Analysis

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    To get a sense of any relationship between GDP change and GII change, we ran a simple linear regression. Here’s what we found:

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    • P-value: With a p-value of 0.057, we fail to reject the null hypothesis. There is no statistically significant relationship. In other words, we don’t find strong evidence of a link between GDP and GII changes.

    • +
    • R-squared: GDP change explains only 0.4% of the variation in GII change—a pretty minimal effect.

    • +
    • 95% Confidence Interval: We’re 95% confident the interval from -0.003 to 0.169 captures the true slope. The effect of GDP change on GII is uncertain as 0 is within this range.

    • +
    +
    +
    +

    Time Series Analysis

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    Time series analysis allows us to dig deeper into how GII and GDP changes play out over time—and potentially influence one another. Here’s the rundown:

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    • Stationarity check: Both GII and GDP change series are stationary, meaning their means and variances are stable over time, setting us up for reliable forecasting.

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    • VAR Model (Vector Autoregression): This multivariate model lets us explore how past values in both series relate to current values. Notably, past GII changes significantly impact current GII changes.

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    • Model Performance: The VAR model has good fit, as shown by the forecast closely tracking actual values. Low MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) confirm that our model predicts GII and GDP change with minimal error. Check out the plots and table below!

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    • Granger Causality Test: Curious whether GDP changes can predict changes in GII? The Granger causality test says no. With p-values above 0.05, we fail to find evidence that GDP changes drive GII changes. This suggests that while both variables evolve over time, they don’t seem to directly influence each other.

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    +

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    Conclusion

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    In wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research.

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    In terms of the Imfp library, we gained valuable experience working with real-world economic indicators like GDP and the GII. The library’s function in fetching databases, parameters, and datasets made it straightforward to access IMF’s data, allowing us to focus on analysis instead. The integration of other Python tools—such as seaborn, matplotlib, and numpy—not only streamlined our computational processes but also ensured consistency across the project.

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    Future Directions

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    First, we must consider what other factors might influence the relationship between GDP and GII change. The GII is a composite index, shaped by a myriad of social factors, including cultural norms, legal frameworks, and environmental shifts. Future studies could benefit from incorporating additional predictors into the analysis and exploring the interaction between economic growth and gender equality within specific country contexts.

    +

    Second, there’s potential to enhance the predictive power of our Vector Autoregression (VAR) time series model. While we established that GDP change does not cause GII change, our model performed well in forecasting trends for both variables independently. In practice, policymakers may want to forecast GII trends independently of GDP if they are implementing gender-focused policies. Future research could investigate time series modeling to further unravel the dynamics of GII and GDP changes.

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    So, as we wrap up this chapter, let’s keep our curiosity alive and our questions flowing. After all, every end is just a new beginning in the quest for knowledge!

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    Blog

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    + + + + + \ No newline at end of file diff --git a/demo/demo/_site/Code/imfp_demo.html b/demo/demo/_site/Code/imfp_demo.html new file mode 100644 index 0000000..f4f6888 --- /dev/null +++ b/demo/demo/_site/Code/imfp_demo.html @@ -0,0 +1,2439 @@ + + + + + + + + + +imfp Demo Project – imfp Demo + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    imfp Demo Project

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    This data analysis project aims to explore the relationship between economic growth and gender equality using imfp, which allows us to download data from IMF (International Monetary Fund). imfp can be integrated with other python tools to streamline the computational process. To demonstrate its functionality, the project experimented with a variety of visualization and analysis methods.

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    Ready for some insights about if a stronger economy mean more equal opportunities for all genders? Let’s dive in!

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    Executive Summary

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    In this project, we explored the following:

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    1. Data Fetching
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    • Make API call to fetch 4 datasets: GII (Gender Inequality Index), Nominal GDP, GDP Deflator Index, Population series
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    1. Feature Engineering
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    • Cleaning: Convert GDP Deflator Index to a yearly basis and variables to numeric
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    • Dependent Variable: Percent Change of Gender Inequality Index
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    • Independent Variable: Percent Change of Real GDP per Capita
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    • Transform variables to display magnitude of change
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    • Merge the datasets
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    1. Data Visualization
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    • Scatterplot
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    • Time Series Line Plots
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    • Barplot
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    • Boxplot
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    • Heatmap
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    1. Statistical Analysis
    2. +
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    • Descriptive Statistics
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    • Regression Analysis
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    • Time Series Analysis
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    Suggested packages

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    pandas: view and manipulate data frame; matplotlib.pyplot: make plots; seaborn: make plots; numpy: computation; LinearRegression: implement linear regression; tabulate: format data into tables;

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    A custom module is written to simplify the process of making API calls and fetching information with imfp library. load_or_fetch_databases, load_or_fetch_parameters load_or_fetch_dataset load and retreive database, parameters, and dataset from a local or remote source. view_dataframe_in_browser displays dataframe in a web browser.

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    statsmodels.api, adfuller, ARIMA,VAR,plot_acf,plot_pacf,mean_absolute_error,mean_squared_error, andgrangercausalitytests are specifically used for time series analysis.

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    +
    import os
    +import pickle
    +from tempfile import NamedTemporaryFile
    +import pandas as pd
    +import imfp
    +import webbrowser
    +
    +
    +# Function to display a DataFrame in a web browser
    +def view_dataframe_in_browser(df):
    +    html = df.to_html()
    +    with NamedTemporaryFile(delete=False, mode="w", suffix=".html") as f:
    +        url = "file://" + f.name
    +        f.write(html)
    +    webbrowser.open(url)
    +
    +
    +# Function to load databases from CSV or fetch from API
    +def load_or_fetch_databases():
    +    csv_path = os.path.join("data", "databases.csv")
    +
    +    # Try to load from CSV
    +    if os.path.exists(csv_path):
    +        try:
    +            return pd.read_csv(csv_path)
    +        except Exception as e:
    +            print(f"Error loading CSV: {e}")
    +
    +    # If CSV doesn't exist or couldn't be loaded, fetch from API
    +    print("Fetching databases from IMF API...")
    +    databases = imfp.imf_databases()
    +
    +    # Save to CSV for future use
    +    databases.to_csv(csv_path, index=False)
    +    print(f"Databases saved to {csv_path}")
    +
    +    return databases
    +
    +
    +def load_or_fetch_parameters(database_name):
    +    pickle_path = os.path.join("data", f"{database_name}.pickle")
    +
    +    # Try to load from pickle file
    +    if os.path.exists(pickle_path):
    +        try:
    +            with open(pickle_path, "rb") as f:
    +                return pickle.load(f)
    +        except Exception as e:
    +            print(f"Error loading pickle file: {e}")
    +
    +    # If pickle doesn't exist or couldn't be loaded, fetch from API
    +    print(f"Fetching parameters for {database_name} from IMF API...")
    +    parameters = imfp.imf_parameters(database_name)
    +
    +    # Save to pickle file for future use
    +    os.makedirs("data", exist_ok=True)  # Ensure the data directory exists
    +    with open(pickle_path, "wb") as f:
    +        pickle.dump(parameters, f)
    +    print(f"Parameters saved to {pickle_path}")
    +
    +    return parameters
    +
    +
    +def load_or_fetch_dataset(database_id, indicator):
    +    file_name = f"{database_id}.{indicator}.csv"
    +    csv_path = os.path.join("data", file_name)
    +
    +    # Try to load from CSV file
    +    if os.path.exists(csv_path):
    +        try:
    +            return pd.read_csv(csv_path)
    +        except Exception as e:
    +            print(f"Error loading CSV file: {e}")
    +
    +    # If CSV doesn't exist or couldn't be loaded, fetch from API
    +    print(f"Fetching dataset for {database_id}.{indicator} from IMF API...")
    +    dataset = imfp.imf_dataset(database_id=database_id, indicator=[indicator])
    +
    +    # Save to CSV file for future use
    +    os.makedirs("data", exist_ok=True)  # Ensure the data directory exists
    +    dataset.to_csv(csv_path, index=False)
    +    print(f"Dataset saved to {csv_path}")
    +
    +    return dataset
    +
    +
    +
    import pandas as pd
    +import matplotlib.pyplot as plt
    +import seaborn as sns
    +import numpy as np
    +from sklearn.linear_model import LinearRegression
    +from tabulate import tabulate
    +import statsmodels.api as sm
    +from statsmodels.tsa.stattools import adfuller
    +from statsmodels.tsa.arima.model import ARIMA
    +from statsmodels.tsa.vector_ar.var_model import VAR
    +from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
    +from sklearn.metrics import mean_absolute_error
    +from sklearn.metrics import mean_squared_error
    +from statsmodels.tsa.stattools import grangercausalitytests
    +
    +
    +
    +

    Data Fetching

    +

    In this section, we extracted four datasets through API calls: Gender Inequality Index(GII), GDP Deflator, Nominal GDP, and Population. Two databases were used: Gender Equality and International Financial Statistics (IFS).

    +
    +
    from pathlib import Path
    +Path("data").mkdir(exist_ok=True)
    +
    +
    +
    # Load or fetch databases
    +databases = load_or_fetch_databases()
    +
    +# Filter out databases that contain a year in the description
    +databases[
    +  ~databases['description'].str.contains(r"[\d]{4}", regex=True)
    +]
    +
    +# view_dataframe_in_browser(databases)
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    database_iddescription
    25HPDDHistorical Public Debt (HPDD)
    34RAFIT2AGGRevenue Administration Fiscal Information Tool...
    47GENDER_EQUALITYGender Equality
    63PGCSPrivate and Public Capital Stock Dataset
    69GENDER_BUDGETINGGender Budgeting
    125CPIConsumer Price Index (CPI)
    153IRFCLInternational Reserves and Foreign Currency Li...
    191IFS_DISCONTINUEDInternational Financial Statistics (IFS), Disc...
    192EQExport Quality
    193EDExport Diversification
    226FISCALDECENTRALIZATIONFiscal Decentralization
    291FDIFinancial Development Index
    292PSBSFADPublic Sector Balance Sheet (PSBS)(FAD)
    293UNSDG_IMF_INPUTSSustainable Development Goals, IMF Inputs
    294CPISCoordinated Portfolio Investment Survey (CPIS)
    295PCTOTCommodity Terms of Trade
    296FMFiscal Monitor (FM)
    297AFRREOSub-Saharan Africa Regional Economic Outlook (...
    298WHDREOWestern Hemisphere Regional Economic Outlook (...
    299MCDREOMiddle East and Central Asia Regional Economic...
    300APDREOAsia and Pacific Regional Economic Outlook (AP...
    301BOPAGGBalance of Payments (BOP), World and Regional ...
    302PCPSPrimary Commodity Price System (PCPS)
    303CDISCoordinated Direct Investment Survey (CDIS)
    304BOPBalance of Payments (BOP)
    305COFERCurrency Composition of Official Foreign Excha...
    306DOTDirection of Trade Statistics (DOTS)
    307RAFIT3PRA-FIT Round3 Completion and Participation Rates
    308FASFinancial Access Survey (FAS)
    309FSIFinancial Soundness Indicators (FSIs)
    310FSIREFinancial Soundness Indicators: Reporting enti...
    311BOPSDMXUSDBalance of Payments (BOP), Global SDMX (US Dol...
    312NAMAIN_IDC_NSystem of National Accounts (SNA), NA_MAIN
    313GFSRGovernment Finance Statistics (GFS), Revenue
    314GFSSSUCGovernment Finance Statistics (GFS), Statement...
    315GFSCOFOGGovernment Finance Statistics (GFS), Expenditu...
    316GFSFALCSGovernment Finance Statistics (GFS), Financial...
    317GFSIBSGovernment Finance Statistics (GFS), Integrate...
    318GFSMABGovernment Finance Statistics (GFS), Main Aggr...
    319GFSEGovernment Finance Statistics (GFS), Expense
    320IFSInternational Financial Statistics (IFS)
    321MFSMonetary and Financial Statistics (MFS)
    + +
    +
    +
    +
    +
    datasets = ["GENDER_EQUALITY", "IFS"]
    +params = {}
    +
    +# Fetch valid parameters for two datasets
    +for dataset in datasets:
    +    params[dataset] = load_or_fetch_parameters(dataset)
    +
    +    valid_keys = list(params[dataset].keys())
    +    print(f"Parameters for {dataset}: ", valid_keys)
    +
    +# view_dataframe_in_browser(params.get("IFS").get("indicator"))
    +
    +
    Parameters for GENDER_EQUALITY:  ['freq', 'ref_area', 'indicator']
    +Parameters for IFS:  ['freq', 'ref_area', 'indicator']
    +
    +
    +
    +
    datasets = {}
    +dsets = [("GENDER_EQUALITY", "GE_GII"), ("IFS", "NGDP_D_SA_IX"), ("IFS", "NGDP_XDC"), ("IFS", "LP_PE_NUM")]
    +
    +for dset in dsets:
    +    datasets[dset[0] + "." + dset[1]] = load_or_fetch_dataset(dset[0], dset[1])
    +
    +
    +
    # "Gender Inequality Index"
    +GII = "GENDER_EQUALITY.GE_GII"
    +
    +# "Gross Domestic Product, Deflator, Seasonally Adjusted, Index"
    +GDP_deflator = "IFS.NGDP_D_SA_IX"
    +
    +# "Gross Domestic Product, Nominal, Domestic Currency"
    +GDP_nominal = "IFS.NGDP_XDC"
    +
    +# "Population, Persons, Number of"
    +GDP_population = "IFS.LP_PE_NUM"
    +
    +# Assign the datasets to new variables so we don't change the originals
    +GII_data = datasets[GII]
    +GDP_deflator_data = datasets[GDP_deflator]
    +GDP_nominal_data = datasets[GDP_nominal]
    +GDP_population_data = datasets[GDP_population]
    +
    +
    +
    +

    Feature Engineering

    +
    +

    Data Cleaning

    +

    Since the GDP deflator is reported on a quarterly basis, we need to convert it to a yearly basis.

    +
    +
    # Filter to keep only rows with a partial string match for "Q4" in the time_period column
    +GDP_deflator_data = GDP_deflator_data[GDP_deflator_data['time_period'].str.contains("Q4")]
    +
    +
    +
    # Split the time_period into year and quarter and keep the year only
    +GDP_deflator_data.loc[:, 'time_period'] = GDP_deflator_data['time_period'].str[0:4]
    +
    +
    +
    datasets = [GII_data, GDP_deflator_data, GDP_nominal_data, GDP_population_data]
    +
    +for i, dataset in enumerate(datasets):    
    +    # Use .loc to modify the columns
    +    datasets[i].loc[:, 'obs_value'] = pd.to_numeric(datasets[i]['obs_value'], errors='coerce')
    +    datasets[i].loc[:, 'time_period'] = pd.to_numeric(datasets[i]['time_period'], errors='coerce')
    +    datasets[i].loc[:, 'unit_mult'] = pd.to_numeric(datasets[i]['unit_mult'], errors='coerce')
    +
    +
    +
    # Calculate percent change for each ref_area
    +GII_data[f'pct_change'] = GII_data.sort_values(['ref_area', 'time_period']).groupby('ref_area')['obs_value'].pct_change()
    +
    +# Display the first few rows of the updated dataset
    +GII_data.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    freqref_areaindicatorunit_multtime_formattime_periodobs_valuepct_change
    0AAFGE_GII0P1Y19900.828244NaN
    1AAFGE_GII0P1Y19910.817706-0.012723
    2AAFGE_GII0P1Y19920.809806-0.009662
    3AAFGE_GII0P1Y19930.803078-0.008308
    4AAFGE_GII0P1Y19940.797028-0.007533
    + +
    +
    +
    +
    +
    # Create a new dataframe with only the required columns
    +GII_data = GII_data[['ref_area', 'time_period', 'pct_change']].copy()
    +
    +# Display the first few rows of the new dataset
    +GII_data.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ref_areatime_periodpct_change
    0AF1990NaN
    1AF1991-0.012723
    2AF1992-0.009662
    3AF1993-0.008308
    4AF1994-0.007533
    + +
    +
    +
    +
    +
    # GDP Deflator Dataset
    +# Create a new dataframe with only the required columns
    +GDP_deflator_data = GDP_deflator_data[['ref_area', 'time_period', 'unit_mult', 'obs_value']].copy()
    +
    +# Divide obs_value by 100
    +GDP_deflator_data['obs_value'] = GDP_deflator_data['obs_value'] / 100
    +
    +# Display the first few rows of the new dataset
    +GDP_deflator_data.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ref_areatime_periodunit_multobs_value
    3FI199000.732006
    7FI199100.739841
    11FI199200.746543
    15FI199300.756193
    19FI199400.779373
    + +
    +
    +
    +
    +
    # GDP Nominal Data
    +# Create a new dataframe with only the required columns
    +GDP_nominal_data = GDP_nominal_data[['ref_area', 'time_period', 'unit_mult','obs_value']].copy()
    +
    +# Display the first few rows of the new dataset
    +GDP_nominal_data.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ref_areatime_periodunit_multobs_value
    0NE200562418864.0
    1NE200662596972.0
    2NE200762762961.0
    3NE200863247835.0
    4NE200963403683.0
    + +
    +
    +
    +
    +
    # GDP Population Data 
    +# Create a new dataframe with only the required columns
    +GDP_population_data = GDP_population_data[['ref_area', 'time_period', 'unit_mult','obs_value']].copy()
    +
    +# Display the first few rows of the new dataset
    +GDP_population_data.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ref_areatime_periodunit_multobs_value
    0GA19503473.296
    1GA19513476.381
    2GA19523478.655
    3GA19533480.536
    4GA19543482.332
    + +
    +
    +
    +
    +
    # Combine all the datasets above for further calculation
    +merged_df = pd.merge(pd.merge(GDP_deflator_data,GDP_nominal_data, on=['time_period', 'ref_area'], suffixes=('_index', '_nominal'), how='inner'), GDP_population_data, on=['time_period', 'ref_area'], how='inner')
    +
    +# Display the GDP data in a web browser
    +# view_dataframe_in_browser(merged_df)
    +
    +# Display the first few rows of the dataset
    +merged_df.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ref_areatime_periodunit_mult_indexobs_value_indexunit_mult_nominalobs_value_nominalunit_multobs_value
    0FI199000.732006690964.034996.220
    1FI199100.739841686913.035019.134
    2FI199200.746543684786.035044.928
    3FI199300.756193685610.035071.782
    4FI199400.779373690646.035097.090
    + +
    +
    +
    +
    +
    merged_df['adjusted_index'] = merged_df['obs_value_index'] * (10 ** (merged_df['unit_mult_index']))
    +merged_df['adjusted_nominal'] = merged_df['obs_value_nominal'] * (10 ** (merged_df['unit_mult_nominal']))
    +merged_df['adjusted_population'] = merged_df['obs_value'] * (10 ** (merged_df['unit_mult']))
    +
    +
    +
    # Merged dataset
    +# Create a new dataframe with only the required columns
    +merged_df = merged_df[['ref_area', 'time_period','adjusted_nominal', 'adjusted_index', 'adjusted_population']].copy()
    +
    +# Display the first few rows of the dataset
    +merged_df.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ref_areatime_periodadjusted_nominaladjusted_indexadjusted_population
    0FI19909.096400e+100.7320064996220.0
    1FI19918.691300e+100.7398415019134.0
    2FI19928.478600e+100.7465435044928.0
    3FI19938.561000e+100.7561935071782.0
    4FI19949.064600e+100.7793735097090.0
    + +
    +
    +
    +
    +
    # Step 1: Real GDP = (Nominal GDP / GDP Deflator Index)
    +merged_df['Real_GDP_domestic'] = (merged_df['adjusted_nominal'] / merged_df['adjusted_index'])
    +
    +# Step 2: Real GDP per Capita = Real GDP / Population
    +merged_df['Real_GDP_per_capita'] = merged_df['Real_GDP_domestic'] / merged_df['adjusted_population']
    +
    +# Check the results
    +merged_df.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ref_areatime_periodadjusted_nominaladjusted_indexadjusted_populationReal_GDP_domesticReal_GDP_per_capita
    0FI19909.096400e+100.7320064996220.01.242667e+1124872.143699
    1FI19918.691300e+100.7398415019134.01.174753e+1123405.490395
    2FI19928.478600e+100.7465435044928.01.135715e+1122512.011198
    3FI19938.561000e+100.7561935071782.01.132119e+1122321.919259
    4FI19949.064600e+100.7793735097090.01.163063e+1122818.181344
    + +
    +
    +
    +
    +
    # Calculate percent change for each ref_area
    +merged_df[f'GDP_change'] = merged_df.sort_values(['ref_area', 'time_period']).groupby('ref_area')['Real_GDP_per_capita'].pct_change()
    +
    +# Rename dataset
    +GDP_data = merged_df
    +
    +# Display the first few rows of the dataset
    +GDP_data.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ref_areatime_periodadjusted_nominaladjusted_indexadjusted_populationReal_GDP_domesticReal_GDP_per_capitaGDP_change
    0FI19909.096400e+100.7320064996220.01.242667e+1124872.143699NaN
    1FI19918.691300e+100.7398415019134.01.174753e+1123405.490395-0.058968
    2FI19928.478600e+100.7465435044928.01.135715e+1122512.011198-0.038174
    3FI19938.561000e+100.7561935071782.01.132119e+1122321.919259-0.008444
    4FI19949.064600e+100.7793735097090.01.163063e+1122818.1813440.022232
    + +
    +
    +
    +
    +
    # GII and GDP
    +# Merge the datasets
    +combined_data = pd.merge(GII_data, GDP_data, on=["ref_area", "time_period"], how = "inner")
    +
    +# Check the combined dataset
    +combined_data.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ref_areatime_periodpct_changeadjusted_nominaladjusted_indexadjusted_populationReal_GDP_domesticReal_GDP_per_capitaGDP_change
    0AR2004-0.0218964.851153e+110.40967238491970.01.184155e+1230763.687776NaN
    1AR2005-0.0085815.825382e+110.47010838892924.01.239159e+1231860.7727760.035662
    2AR20060.0073797.159043e+110.52653539289876.01.359651e+1234605.6280840.086152
    3AR2007-0.0201178.969801e+110.62913939684303.01.425727e+1235926.7345750.038176
    4AR20080.0007331.149646e+120.75775340080159.01.517178e+1237853.6002520.053633
    + +
    +
    +
    +
    +
    # GII and GDP
    +# Rename columns
    +combined_data = combined_data.rename(columns = {
    +    "ref_area": "Country",
    +    "time_period": "Time",
    +    "pct_change": "GII_change"
    +})
    +
    +# Display the GDP data in a web browser
    +# view_dataframe_in_browser(combined_data)
    +
    +# Check the combined dataset
    +combined_data.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    CountryTimeGII_changeadjusted_nominaladjusted_indexadjusted_populationReal_GDP_domesticReal_GDP_per_capitaGDP_change
    0AR2004-0.0218964.851153e+110.40967238491970.01.184155e+1230763.687776NaN
    1AR2005-0.0085815.825382e+110.47010838892924.01.239159e+1231860.7727760.035662
    2AR20060.0073797.159043e+110.52653539289876.01.359651e+1234605.6280840.086152
    3AR2007-0.0201178.969801e+110.62913939684303.01.425727e+1235926.7345750.038176
    4AR20080.0007331.149646e+120.75775340080159.01.517178e+1237853.6002520.053633
    + +
    +
    +
    +
    +
    +
    +

    Data Visualization

    +
    +

    Scatterplot

    +

    Scatterplot use dots to represent values of two numeric variables. The horizontal axis is the percent change in Real GDP per capita. The vertical axis is the percent change in Gender Inequality Index(GII). Different colors represent different countries. We used a linear regression line to display the overall pattern.

    +

    Based on the scatterplot, it seems like there is a slight positive relationship between GDP change and GII change as shown by the flat regression line. Gender inequality is decreasing (gender equality is improving) a little faster in country-years with low GDP growth and a little slower in country-years with high GDP growth.

    +
    +
    # Drop NAs
    +combined_data = combined_data.dropna(subset=['GDP_change', 'GII_change'])
    +
    +# Plot the data points
    +plt.figure(figsize=(18, 10))
    +for country in combined_data['Country'].unique():
    +    country_data = combined_data[combined_data['Country'] == country]
    +    plt.scatter(country_data['GDP_change'], country_data['GII_change'],
    +             marker='o',linestyle='-', label=country)
    +plt.title('Country-Year Analysis of GDP Change vs. GII Change')
    +plt.xlabel('Percent Change in Real GDP per Capita (Country-Year)')
    +plt.ylabel('Percent Change in GII (Country-Year)')
    +plt.grid(True)
    +
    +# Prepare data for linear regression
    +X = combined_data['GDP_change'].values.reshape(-1, 1)
    +y = combined_data['GII_change'].values
    +
    +# Perform linear regression
    +reg = LinearRegression().fit(X, y)
    +y_pred = reg.predict(X)
    +
    +# Plot the regression line
    +plt.plot(combined_data['GDP_change'], y_pred, color='red', linewidth=2)
    +
    +plt.show()
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Time Series Line Plot

    +

    Create separate line plots for GDP change and GII change over time for a few key countries might show the trends more clearly.

    +

    US: United States JP: Japan GB: United Kindom FR: France MX: Mexico

    +

    Based on the line plots, we see GDP change and GII change have different patterns. For example, in Mexico, when there is a big change in real GDP per captia in 1995, the change in GII is pretty stable.

    +
    +
    # Time Series Line plot for a few key countries
    +selected_countries  = ['US', 'JP', 'GB', 'FR', 'MX']
    +combined_data_selected = combined_data[combined_data['Country'].isin(selected_countries)]
    +
    +# Set up the Plot Structure
    +fig, ax = plt.subplots(2, 1, figsize=(18, 10), sharex=True)
    +
    +# Plot change in real GDP per capita over time
    +sns.lineplot(data = combined_data_selected, x = "Time", y = "GDP_change", hue = "Country", ax = ax[0])
    +ax[0].set_title("Percent Change in Real GDP per Capita Over Time")
    +ax[0].set_ylabel("Percent Change in Real GDP per Capita")
    +
    +# Plot change in GII over time
    +sns.lineplot(data = combined_data_selected, x = "Time", y = "GII_change", hue = "Country", ax = ax[1])
    +ax[1].set_title("Percent Change in GII over Time")
    +ax[1].set_xlabel("Time")
    +ax[1].set_ylabel("GII")
    +
    +plt.tight_layout
    +plt.show()
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Barplot

    +

    Use a barplot to show average changes in GII and GDP percent change for each country to visualize regions where inequality is improving or worsening.

    +

    This plot supports our previous observation how GII change seems to be not be correlated with GDP change. We also saw that, for country SI, Solvenia, there seems to be a large improvement in gender inequality.

    +
    +
    # Barplot using average GII and GDP change
    +# Calculate average change for each country
    +combined_data_avg = combined_data.groupby('Country')[['GII_change','GDP_change']].mean().reset_index()
    +
    +# Prepare to plot structure 
    +plt.figure(figsize = (18,10))
    +
    +# Create the barplot
    +combined_data_avg.plot(kind = 'bar', x = 'Country')
    +plt.ylabel('Average Change')
    +plt.xlabel('Country')
    +plt.legend(['GII change', 'GDP change'])
    +plt.grid(axis = 'y')
    +
    +# Show the plot
    +plt.show()
    +
    +
    <Figure size 1728x960 with 0 Axes>
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Boxplot

    +

    Use boxplot to visualize the distribution of GDP and GII change by country, providing information about spread, median, and potential outliers. To provide a more informative view, we sequence countries in an ascending order by the median of percent change in GDP.

    +

    The boxplot displays a slight upward trend with no obvious pattern between GDP and GII change. In coutries with higher GDP change median, they also tend to have a larger spread of the GDP change. The median of GII change remains stable regardless of the magnitude of GDP change, implying weak or no association between GDP and GII change. We observe a potential outlier for country SI, Solvenia, which may explains its large improvement in Gender inequality.

    +
    +
    # Box plot for GII and GDP change
    +# Melt the dataframe to long format for combined boxplot
    +combined_data_melted = combined_data.melt(id_vars=['Country'], value_vars=['GII_change', 'GDP_change'], 
    +                                          var_name='Change_Type', value_name='Value')
    +
    +gdp_medians = combined_data.groupby('Country')['GDP_change'].median().sort_values()
    +
    +combined_data_melted['Country'] = pd.Categorical(combined_data_melted['Country'], categories=gdp_medians.index, ordered= True)
    +
    +# Prepare the plot structure
    +plt.figure(figsize=(18, 10))
    +sns.boxplot(data = combined_data_melted, x = "Country", y = 'Value', hue = 'Change_Type')
    +plt.title('Distribution of GII and GDP change by Country')
    +plt.xlabel('Country')
    +plt.ylabel('Change')
    +plt.legend(title = 'Change Type')
    +
    +# Show the plot
    +plt.show()
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Correlation Matrix

    +

    Create a heatmap to show the relationship between GII and GDP change.

    +

    A positive correlation coefficient indicates a positive relationship: the larger the GDP change, the larger the GII change. A negative correlation coefficient indicates a negative relationship: the larger the GDP change, the smaller the GII change. A correlation coefficient closer to 0 indicates there is weak or no relationship.

    +

    Based on the numeric values in the plot, there is a moderately strong positive correlation between GII and GDP change for country Estonia(EE) and Ireland(IE).

    +
    +
    # Calculate the correlation
    +country_correlation = combined_data.groupby('Country')[['GII_change', 'GDP_change']].corr().iloc[0::2, -1].reset_index(name='Correlation')
    +
    +# Put the correlation value in a matrix format
    +correlation_matrix = country_correlation.pivot(index='Country', columns='level_1', values='Correlation')
    +
    +# Check for NaN values in the correlation matrix
    +correlation_matrix.fillna(0, inplace=True)  # Replace NaNs with 0 or another value as appropriate
    +
    +# Set up the plot structure
    +plt.figure(figsize=(18, 12))  # Adjust height to give more space for y-axis labels
    +
    +# Plot the heatmap
    +sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0, cbar_kws={"shrink": .8}, linewidths=.5)
    +
    +# Enhance axis labels and title
    +plt.title('Heatmap for GII and GDP Change', fontsize=20)
    +plt.xlabel('Variables', fontsize=16)
    +plt.ylabel('Country', fontsize=16)
    +
    +# Improve readability of y-axis labels
    +plt.yticks(fontsize=12)  # Adjust the font size for y-axis labels
    +
    +# Show the plot
    +plt.show()
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +
    +

    Statistical Analysis

    +
    +

    Descriptive Statistics

    +

    There is a total of 915 data points. The mean of the GII change in -0.0314868, which indicates the overall grand mean percent change in gender inequality index is -3.15%. The mean of the GDP change is 0.0234633, showing the overall grand mean percent change in real GDP per capita is 2.35%.

    +
    +
    # Generate summary statistics
    +descriptive_stats = combined_data.describe()
    +print(tabulate(descriptive_stats, headers='keys', tablefmt='github'))
    +
    +
    |       |   GII_change |   adjusted_nominal |   adjusted_index |   adjusted_population |   Real_GDP_domestic |   Real_GDP_per_capita |   GDP_change |
    +|-------|--------------|--------------------|------------------|-----------------------|---------------------|-----------------------|--------------|
    +| count | 914          |      914           |      914         |         914           |       914           |         914           | 914          |
    +| mean  |  -0.0314506  |        7.85541e+13 |        0.855504  |           4.50061e+07 |         7.60737e+13 |           1.00595e+06 |   0.023532   |
    +| std   |   0.054783   |        6.01311e+14 |        0.210057  |           1.24493e+08 |         5.52717e+14 |           3.88155e+06 |   0.0418127  |
    +| min   |  -0.755003   |        2.18714e+09 |        0.0360636 |      396324           |         5.21733e+09 |        1798.42        |  -0.285847   |
    +| 25%   |  -0.0424105  |        1.07655e+11 |        0.743355  |           5.0737e+06  |         1.48206e+11 |       17176           |   0.00384809 |
    +| 50%   |  -0.020248   |        7.50706e+11 |        0.888883  |           1.0337e+07  |         9.94933e+11 |       35401           |   0.0222486  |
    +| 75%   |  -0.00451301 |        2.5911e+12  |        1.00175   |           4.5159e+07  |         2.9412e+12  |      120391           |   0.0427318  |
    +| max   |   0.156992   |        9.54613e+15 |        2.0788    |           1.28084e+09 |         7.92007e+15 |           3.14532e+07 |   0.241984   |
    +
    +
    +
    +
    +

    Regression Analysis

    +

    Simple linear regression as a foundational approach provide us with a basic understanding of the relationship between GDP change and GII change.

    +

    Based on the summary, we can conclude the following: * Becasue p-value = 0.057, if we set alpha, the significance level, to be 0.05, we fail to reject the null hypothesis and conclude there is no significant relationship between percent change in real GDP per capita and gender inequality index.

    +
      +
    • R-squared = 0.004. Only 0.4% of the variance in GII change can be explained by GDP change.

    • +
    • We are 95% confident that the interval from -0.003 to 0.169 captures the true slope of GDP change. Because 0 is included, we are uncertain about the effect of GDP change on GII chnage.

    • +
    +
    +
    # Define independent and depenent variables
    +X = combined_data['GDP_change']
    +y = combined_data['GII_change']
    +
    +# Add a constant to indepdent variable to include an intercept
    +X = sm.add_constant(X)
    +
    +# Fit a simple linear regresion model and print out the summary
    +model = sm.OLS(y, X).fit()
    +print(model.summary())
    +
    +
                                OLS Regression Results                            
    +==============================================================================
    +Dep. Variable:             GII_change   R-squared:                       0.004
    +Model:                            OLS   Adj. R-squared:                  0.003
    +Method:                 Least Squares   F-statistic:                     3.551
    +Date:                Wed, 25 Dec 2024   Prob (F-statistic):             0.0598
    +Time:                        16:32:05   Log-Likelihood:                 1360.0
    +No. Observations:                 914   AIC:                            -2716.
    +Df Residuals:                     912   BIC:                            -2706.
    +Df Model:                           1                                         
    +Covariance Type:            nonrobust                                         
    +==============================================================================
    +                 coef    std err          t      P>|t|      [0.025      0.975]
    +------------------------------------------------------------------------------
    +const         -0.0334      0.002    -16.069      0.000      -0.037      -0.029
    +GDP_change     0.0816      0.043      1.885      0.060      -0.003       0.167
    +==============================================================================
    +Omnibus:                      841.660   Durbin-Watson:                   1.384
    +Prob(Omnibus):                  0.000   Jarque-Bera (JB):            53999.190
    +Skew:                          -3.938   Prob(JB):                         0.00
    +Kurtosis:                      39.823   Cond. No.                         23.9
    +==============================================================================
    +
    +Notes:
    +[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
    +
    +
    +
    +
    +

    Time Series Analysis

    +

    Time series analysis allows us to explore how the relationship between GII and GDP change vary across different time periods, accounting for lagged effects.

    +

    Here is a quick summary of the result: * Both GII and GDP change time series are stationary.

    +
      +
    • Past GII change values significantly influence cuurent GII change values.

    • +
    • VAR model has good model performance on forecasting future values based on historical data.

    • +
    • Changes in GDP does not cause/precde the changes in GII.

    • +
    +
    +

    ADF Test: Stationality Assumption Check

    +

    We want to use Augmented Dickey-Fuller (ADF) test to check whether a time series is stationary, which is the model assumption for many time series models.

    +

    Stationarity implies constant mean and variance over time, making it more predictable and stable for forecasting.

    +

    Based on the ADF test output, both GII and GDP change time series are stationary. We can proceed to the time series modeling section.

    +
    +
    # Augmented Dickey-Fuller (ADF) test for stationarity check
    +# Create melted datasets
    +combined_data_time = combined_data.melt(id_vars=['Time', 'Country'], value_vars=['GII_change','GDP_change'], var_name = 'Change_Type', value_name = 'Value')
    +GII = combined_data_time[(combined_data_time['Change_Type'] == 'GII_change')]                         
    +
    +GDP = combined_data_time[(combined_data_time['Change_Type'] == 'GDP_change')]
    +
    +# Stationary Check
    +def adf_test(series):
    +    result = adfuller(series.dropna())
    +    print(f'ADF Statistic: {result[0]}')
    +    print(f'p-value: {result[1]}')
    +    if result[1] < 0.05:
    +        print("Series is stationary")
    +    else:
    +        print("Series is not stationary")
    +
    +# Output the result
    +adf_test(GII['Value'])
    +adf_test(GDP['Value'])
    +
    +
    ADF Statistic: -16.069430375623295
    +p-value: 5.527565277907626e-29
    +Series is stationary
    +ADF Statistic: -8.535683414310181
    +p-value: 1.0095278443825336e-13
    +Series is stationary
    +
    +
    +
    +
    +

    VAR model: Examine variables separately

    +

    We fit a VAR (Vector Autoreression) model to see the relationship between GII and GDP change. VAR is particularly useful when dealing with multivariate time series data and allows us to examine the interdependence between variables.

    +

    Based on summary, here are several interpretations we can make: - We used AIC as the criteria for model selection. Lower value suggests a better fit. - Given that we want to predict GII change, we will focus on the first set “Results for equation GII_change.” - Past GII_change values significantly influence current GII_change, as shown in the small p-values of lags 1 and 2. - Lag 2 of GDP_change has a relatively low p-value but is not statistically significant.

    +
    +
    # Split the dataset into training and testing sets
    +split_ratio = 0.7
    +split_index = int(len(combined_data) * split_ratio)
    +
    +# Training set is used to fit the model
    +train_data = combined_data.iloc[:split_index]
    +
    +# Testing set is used for validation
    +test_data = combined_data.iloc[split_index:]
    +
    +print(f"Training data: {train_data.shape}")
    +print(f"Test data: {test_data.shape}")
    +
    +
    Training data: (639, 9)
    +Test data: (275, 9)
    +
    +
    +
    +
    # Fit a VAR model 
    +time_model = VAR(train_data[['GII_change', 'GDP_change']])
    +time_model_fitted = time_model.fit(maxlags = 15, ic="aic")
    +
    +# Print out the model summary
    +print(time_model_fitted.summary())
    +
    +
      Summary of Regression Results   
    +==================================
    +Model:                         VAR
    +Method:                        OLS
    +Date:           Wed, 25, Dec, 2024
    +Time:                     16:32:06
    +--------------------------------------------------------------------
    +No. of Equations:         2.00000    BIC:                   -12.2270
    +Nobs:                     636.000    HQIC:                  -12.2869
    +Log likelihood:           2128.47    FPE:                4.43927e-06
    +AIC:                     -12.3250    Det(Omega_mle):     4.34314e-06
    +--------------------------------------------------------------------
    +Results for equation GII_change
    +================================================================================
    +                   coefficient       std. error           t-stat            prob
    +--------------------------------------------------------------------------------
    +const                -0.023760         0.003176           -7.481           0.000
    +L1.GII_change         0.211342         0.039900            5.297           0.000
    +L1.GDP_change        -0.015713         0.046080           -0.341           0.733
    +L2.GII_change         0.111672         0.040402            2.764           0.006
    +L2.GDP_change         0.088293         0.046452            1.901           0.057
    +L3.GII_change        -0.021861         0.039774           -0.550           0.583
    +L3.GDP_change         0.000651         0.046141            0.014           0.989
    +================================================================================
    +
    +Results for equation GDP_change
    +================================================================================
    +                   coefficient       std. error           t-stat            prob
    +--------------------------------------------------------------------------------
    +const                 0.014585         0.002720            5.362           0.000
    +L1.GII_change         0.004128         0.034171            0.121           0.904
    +L1.GDP_change         0.141566         0.039464            3.587           0.000
    +L2.GII_change        -0.009734         0.034600           -0.281           0.778
    +L2.GDP_change         0.044282         0.039782            1.113           0.266
    +L3.GII_change        -0.037787         0.034063           -1.109           0.267
    +L3.GDP_change         0.143870         0.039516            3.641           0.000
    +================================================================================
    +
    +Correlation matrix of residuals
    +              GII_change  GDP_change
    +GII_change      1.000000    0.040992
    +GDP_change      0.040992    1.000000
    +
    +
    +
    +
    +
    +
    /Users/sammi/Library/Caches/pypoetry/virtualenvs/imfp-0AlIckOY-py3.13/lib/python3.13/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: An unsupported index was provided. As a result, forecasts cannot be generated. To use the model for forecasting, use one of the supported classes of index.
    +  self._init_dates(dates, freq)
    +
    +
    +
    +
    +

    VAR Model: Forecasting

    +

    We apply the model learned above to the test data. Based on the plot, the forecast values seem to follow the actual data well, indicating a good model fit caputuring the underlying trends.

    +
    +
    # Number of steps to forecast (length of the test set)
    +n_steps = len(test_data)
    +
    +# Get the last values from the training set for forecasting
    +forecast_input = train_data[['GII_change', 'GDP_change']].values[-time_model_fitted.k_ar:]
    +
    +# Forecasting
    +forecast = time_model_fitted.forecast(y=forecast_input, steps=n_steps)
    +
    +# Create a DataFrame for the forecasted values
    +forecast_df = pd.DataFrame(forecast, index=test_data.index, columns=['GII_forecast', 'GDP_forecast'])
    +
    +# Ensure the index of the forecast_df matches the test_data index
    +forecast_df.index = test_data.index
    +
    +
    +
    plt.figure(figsize=(12, 6))
    +plt.plot(train_data['GII_change'], label='Training GII', color='blue')
    +plt.plot(test_data['GII_change'], label='Actual GII', color='orange')
    +plt.plot(forecast_df['GII_forecast'], label='Forecasted GII', color='green')
    +plt.title('GII Change Forecast vs Actual')
    +plt.legend()
    +plt.show()
    +
    +plt.figure(figsize=(12, 6))
    +plt.plot(train_data['GDP_change'], label='Training GDP', color='blue')
    +plt.plot(test_data['GDP_change'], label='Actual GDP', color='orange')
    +plt.plot(forecast_df['GDP_forecast'], label='Forecasted GDP', color='green')
    +plt.title('GDP Change Forecast vs Actual')
    +plt.legend()
    +plt.show()
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    VAR Model: Model Performance

    +

    Low values of both MAE and RMSE indicate good model performance with small average errors in predictions.

    +
    +
    mae_gii = mean_absolute_error(test_data['GII_change'], forecast_df['GII_forecast'])
    +mae_gdp = mean_absolute_error(test_data['GDP_change'], forecast_df['GDP_forecast'])
    +
    +print(f'Mean Absolute Error for GII: {mae_gii}')
    +print(f'Mean Absolute Error for GDP: {mae_gdp}')
    +
    +
    Mean Absolute Error for GII: 0.03296113056900485
    +Mean Absolute Error for GDP: 0.027636232780964363
    +
    +
    +
    +
    rmse_gii = np.sqrt(mean_squared_error(test_data['GII_change'], forecast_df['GII_forecast']))
    +rmse_gdp = np.sqrt(mean_squared_error(test_data['GDP_change'], forecast_df['GDP_forecast']))
    +
    +print(f'RMSE for GII: {rmse_gii}')
    +print(f'RMSE for GDP: {rmse_gdp}')
    +
    +
    RMSE for GII: 0.06284754560659174
    +RMSE for GDP: 0.038429216092797085
    +
    +
    +
    +
    +

    VAR Model: Granger causality test

    +

    Granger causality test evaluates whether one time series can predict another.

    +

    Based on the output, the lowest p-value is when lag = 2. However, because p-value > 0.05, we fail to reject the null hypothesis and conclude the GDP_change does not Granger-cause the GII_change.

    +
    +
    # Perform the Granger causality test
    +max_lag = 3
    +test_result = grangercausalitytests(train_data[['GII_change', 'GDP_change']], max_lag, verbose=True)
    +
    +
    
    +Granger Causality
    +number of lags (no zero) 1
    +ssr based F test:         F=0.0000  , p=0.9972  , df_denom=635, df_num=1
    +ssr based chi2 test:   chi2=0.0000  , p=0.9972  , df=1
    +likelihood ratio test: chi2=0.0000  , p=0.9972  , df=1
    +parameter F test:         F=0.0000  , p=0.9972  , df_denom=635, df_num=1
    +
    +Granger Causality
    +number of lags (no zero) 2
    +ssr based F test:         F=1.8529  , p=0.1576  , df_denom=632, df_num=2
    +ssr based chi2 test:   chi2=3.7351  , p=0.1545  , df=2
    +likelihood ratio test: chi2=3.7242  , p=0.1553  , df=2
    +parameter F test:         F=1.8529  , p=0.1576  , df_denom=632, df_num=2
    +
    +Granger Causality
    +number of lags (no zero) 3
    +ssr based F test:         F=1.2359  , p=0.2958  , df_denom=629, df_num=3
    +ssr based chi2 test:   chi2=3.7489  , p=0.2899  , df=3
    +likelihood ratio test: chi2=3.7379  , p=0.2912  , df=3
    +parameter F test:         F=1.2359  , p=0.2958  , df_denom=629, df_num=3
    +
    +
    +
    /Users/sammi/Library/Caches/pypoetry/virtualenvs/imfp-0AlIckOY-py3.13/lib/python3.13/site-packages/statsmodels/tsa/stattools.py:1556: FutureWarning: verbose is deprecated since functions should not print results
    +  warnings.warn(
    +
    +
    +
    +
    +
    +
    +

    Conclusion

    +

    In wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research.

    +
    +
    +

    Future Directions

    +
      +
    • Additional Predictors: Gender Inequality Index (GII) is a composite measure shaped by a mariad of social factors. What about culture, policy, and environment?
    • +
    • Specific Country: Are there any specific historical contexts that sparked economic growth and improvement in gender equality?
    • +
    • Opposite causation: Women entering workforce boost GDP?
    • +
    • Predictive Power of VAR model: Our model performed well in forecasting trends for both variables independently.
    • +
    +

    Let’s keep our curiosity alive and our questions flowing!

    + + +
    + +
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0000000..9624f28 --- /dev/null +++ b/demo/demo/_site/imfp_demo.html @@ -0,0 +1,2462 @@ + + + + + + + + + +imfp Demo Project – imfp Demo + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    +
    + +
    + +
    + + + + +
    + +
    +
    +

    imfp Demo Project

    +
    + + + +
    + + + + +
    + + + +
    + + +

    This data analysis project aims to explore the relationship between economic growth and gender equality using imfp, which allows us to download data from IMF (International Monetary Fund). imfp can be integrated with other python tools to streamline the computational process. To demonstrate its functionality, the project experimented with a variety of visualization and analysis methods.

    +
    +

    Executive Summary

    +

    In this project, we explored the following:

    +
      +
    1. Data Fetching
    2. +
    +
      +
    • Make API call to fetch 4 datasets: GII (Gender Inequality Index), Nominal GDP, GDP Deflator Index, Population series
    • +
    +
      +
    1. Feature Engineering
    2. +
    +
      +
    • Cleaning: Convert GDP Deflator Index to a yearly basis and variables to numeric
    • +
    • Dependent Variable: Percent Change of Gender Inequality Index
    • +
    • Independent Variable: Percent Change of Real GDP per Capita
    • +
    • Transform variables to display magnitude of change
    • +
    • Merge the datasets
    • +
    +
      +
    1. Data Visualization
    2. +
    +
      +
    • Scatterplot
    • +
    • Time Series Line Plots
    • +
    • Barplot
    • +
    • Boxplot
    • +
    • Heatmap
    • +
    +
      +
    1. Statistical Analysis
    2. +
    +
      +
    • Descriptive Statistics
    • +
    • Regression Analysis
    • +
    • Time Series Analysis
    • +
    +
    +
    +

    Suggested packages

    +

    The integration of other Python tools not only streamlined our computational processes but also ensured consistency across the project.

    +

    A custom module is written to simplify the process of making API calls and fetching information with imfp library. load_or_fetch_databases, load_or_fetch_parameters load_or_fetch_dataset load and retreive database, parameters, and dataset from a local or remote source. view_dataframe_in_browser displays dataframe in a web browser.

    +
    +
    import os
    +import pickle
    +from tempfile import NamedTemporaryFile
    +import pandas as pd
    +import imfp
    +import webbrowser
    +
    +
    +# Function to display a DataFrame in a web browser
    +def view_dataframe_in_browser(df):
    +    html = df.to_html()
    +    with NamedTemporaryFile(delete=False, mode="w", suffix=".html") as f:
    +        url = "file://" + f.name
    +        f.write(html)
    +    webbrowser.open(url)
    +
    +
    +# Function to load databases from CSV or fetch from API
    +def load_or_fetch_databases():
    +    csv_path = os.path.join("data", "databases.csv")
    +
    +    # Try to load from CSV
    +    if os.path.exists(csv_path):
    +        try:
    +            return pd.read_csv(csv_path)
    +        except Exception as e:
    +            print(f"Error loading CSV: {e}")
    +
    +    # If CSV doesn't exist or couldn't be loaded, fetch from API
    +    print("Fetching databases from IMF API...")
    +    databases = imfp.imf_databases()
    +
    +    # Save to CSV for future use
    +    databases.to_csv(csv_path, index=False)
    +    print(f"Databases saved to {csv_path}")
    +
    +    return databases
    +
    +
    +def load_or_fetch_parameters(database_name):
    +    pickle_path = os.path.join("data", f"{database_name}.pickle")
    +
    +    # Try to load from pickle file
    +    if os.path.exists(pickle_path):
    +        try:
    +            with open(pickle_path, "rb") as f:
    +                return pickle.load(f)
    +        except Exception as e:
    +            print(f"Error loading pickle file: {e}")
    +
    +    # If pickle doesn't exist or couldn't be loaded, fetch from API
    +    print(f"Fetching parameters for {database_name} from IMF API...")
    +    parameters = imfp.imf_parameters(database_name)
    +
    +    # Save to pickle file for future use
    +    os.makedirs("data", exist_ok=True)  # Ensure the data directory exists
    +    with open(pickle_path, "wb") as f:
    +        pickle.dump(parameters, f)
    +    print(f"Parameters saved to {pickle_path}")
    +
    +    return parameters
    +
    +
    +def load_or_fetch_dataset(database_id, indicator):
    +    file_name = f"{database_id}.{indicator}.csv"
    +    csv_path = os.path.join("data", file_name)
    +
    +    # Try to load from CSV file
    +    if os.path.exists(csv_path):
    +        try:
    +            return pd.read_csv(csv_path)
    +        except Exception as e:
    +            print(f"Error loading CSV file: {e}")
    +
    +    # If CSV doesn't exist or couldn't be loaded, fetch from API
    +    print(f"Fetching dataset for {database_id}.{indicator} from IMF API...")
    +    dataset = imfp.imf_dataset(database_id=database_id, indicator=[indicator])
    +
    +    # Save to CSV file for future use
    +    os.makedirs("data", exist_ok=True)  # Ensure the data directory exists
    +    dataset.to_csv(csv_path, index=False)
    +    print(f"Dataset saved to {csv_path}")
    +
    +    return dataset
    +
    +

    Here is a brief introduction about the packages used:

    +

    pandas: view and manipulate data frame

    +

    matplotlib.pyplot: make plots

    +

    seaborn: make plots

    +

    numpy: computation

    +

    LinearRegression: implement linear regression

    +

    tabulate: format data into tables

    +

    statsmodels.api, adfuller, ARIMA,VAR,plot_acf,plot_pacf,mean_absolute_error,mean_squared_error, andgrangercausalitytests are specifically used for time series analysis.

    +
    +
    import pandas as pd
    +import matplotlib.pyplot as plt
    +import seaborn as sns
    +import numpy as np
    +from sklearn.linear_model import LinearRegression
    +from tabulate import tabulate
    +import statsmodels.api as sm
    +from statsmodels.tsa.stattools import adfuller
    +from statsmodels.tsa.arima.model import ARIMA
    +from statsmodels.tsa.vector_ar.var_model import VAR
    +from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
    +from sklearn.metrics import mean_absolute_error
    +from sklearn.metrics import mean_squared_error
    +from statsmodels.tsa.stattools import grangercausalitytests
    +
    +
    +
    +

    Data Fetching

    +

    In this section, we extracted four datasets through API calls: Gender Inequality Index(GII), GDP Deflator, Nominal GDP, and Population.

    +
    +
    from pathlib import Path
    +Path("data").mkdir(exist_ok=True)
    +
    +
    +
    # Load or fetch databases
    +databases = load_or_fetch_databases()
    +
    +# Filter out databases that contain a year in the description
    +databases[
    +  ~databases['description'].str.contains(r"[\d]{4}", regex=True)
    +]
    +
    +# view_dataframe_in_browser(databases)
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    database_iddescription
    25HPDDHistorical Public Debt (HPDD)
    34RAFIT2AGGRevenue Administration Fiscal Information Tool...
    47GENDER_EQUALITYGender Equality
    63PGCSPrivate and Public Capital Stock Dataset
    69GENDER_BUDGETINGGender Budgeting
    125CPIConsumer Price Index (CPI)
    153IRFCLInternational Reserves and Foreign Currency Li...
    191IFS_DISCONTINUEDInternational Financial Statistics (IFS), Disc...
    192EQExport Quality
    193EDExport Diversification
    226FISCALDECENTRALIZATIONFiscal Decentralization
    291FDIFinancial Development Index
    292PSBSFADPublic Sector Balance Sheet (PSBS)(FAD)
    293UNSDG_IMF_INPUTSSustainable Development Goals, IMF Inputs
    294CPISCoordinated Portfolio Investment Survey (CPIS)
    295PCTOTCommodity Terms of Trade
    296FMFiscal Monitor (FM)
    297AFRREOSub-Saharan Africa Regional Economic Outlook (...
    298WHDREOWestern Hemisphere Regional Economic Outlook (...
    299MCDREOMiddle East and Central Asia Regional Economic...
    300APDREOAsia and Pacific Regional Economic Outlook (AP...
    301BOPAGGBalance of Payments (BOP), World and Regional ...
    302PCPSPrimary Commodity Price System (PCPS)
    303CDISCoordinated Direct Investment Survey (CDIS)
    304BOPBalance of Payments (BOP)
    305COFERCurrency Composition of Official Foreign Excha...
    306DOTDirection of Trade Statistics (DOTS)
    307RAFIT3PRA-FIT Round3 Completion and Participation Rates
    308FASFinancial Access Survey (FAS)
    309FSIFinancial Soundness Indicators (FSIs)
    310FSIREFinancial Soundness Indicators: Reporting enti...
    311BOPSDMXUSDBalance of Payments (BOP), Global SDMX (US Dol...
    312NAMAIN_IDC_NSystem of National Accounts (SNA), NA_MAIN
    313GFSRGovernment Finance Statistics (GFS), Revenue
    314GFSSSUCGovernment Finance Statistics (GFS), Statement...
    315GFSCOFOGGovernment Finance Statistics (GFS), Expenditu...
    316GFSFALCSGovernment Finance Statistics (GFS), Financial...
    317GFSIBSGovernment Finance Statistics (GFS), Integrate...
    318GFSMABGovernment Finance Statistics (GFS), Main Aggr...
    319GFSEGovernment Finance Statistics (GFS), Expense
    320IFSInternational Financial Statistics (IFS)
    321MFSMonetary and Financial Statistics (MFS)
    + +
    +
    +
    +

    Two databases were used: Gender Equality and International Financial Statistics (IFS).

    +
    +
    datasets = ["GENDER_EQUALITY", "IFS"]
    +params = {}
    +
    +# Fetch valid parameters for two datasets
    +for dataset in datasets:
    +    params[dataset] = load_or_fetch_parameters(dataset)
    +
    +    valid_keys = list(params[dataset].keys())
    +    print(f"Parameters for {dataset}: ", valid_keys)
    +
    +# view_dataframe_in_browser(params.get("IFS").get("indicator"))
    +
    +
    Parameters for GENDER_EQUALITY:  ['freq', 'ref_area', 'indicator']
    +Parameters for IFS:  ['freq', 'ref_area', 'indicator']
    +
    +
    +

    “freq” stands for Frequency, such as Annual, Monthly, or Quarterly. “ref_area” stands for Geogrpahical Area, such as US (United States), JP (Japan), and GB (United Kindom). “indicator” refers to the specific dataset in the database, including input code and description. For example, if we display all the indicators for IFS database, the GDP deflator dataset has an input code of “NGDP_D_SA_IX” with a full name description of Gross Domestic Product, Deflator, Seasonally Adjusted, Index.

    +

    We paired the database with the specific dataset indicator to read and store the csv file.

    +
    +
    datasets = {}
    +dsets = [("GENDER_EQUALITY", "GE_GII"), ("IFS", "NGDP_D_SA_IX"), ("IFS", "NGDP_XDC"), ("IFS", "LP_PE_NUM")]
    +
    +for dset in dsets:
    +    datasets[dset[0] + "." + dset[1]] = load_or_fetch_dataset(dset[0], dset[1])
    +
    +
    +
    # "Gender Inequality Index"
    +GII = "GENDER_EQUALITY.GE_GII"
    +
    +# "Gross Domestic Product, Deflator, Seasonally Adjusted, Index"
    +GDP_deflator = "IFS.NGDP_D_SA_IX"
    +
    +# "Gross Domestic Product, Nominal, Domestic Currency"
    +GDP_nominal = "IFS.NGDP_XDC"
    +
    +# "Population, Persons, Number of"
    +GDP_population = "IFS.LP_PE_NUM"
    +
    +# Assign the datasets to new variables so we don't change the originals
    +GII_data = datasets[GII]
    +GDP_deflator_data = datasets[GDP_deflator]
    +GDP_nominal_data = datasets[GDP_nominal]
    +GDP_population_data = datasets[GDP_population]
    +
    +
    +
    +

    Feature Engineering

    +
    +

    Data Cleaning

    +

    Since the GDP deflator was reported on a quarterly basis, we converted it to a yearly basis.

    +
    +
    # Filter to keep only rows with a partial string match for "Q4" in the time_period column
    +GDP_deflator_data = GDP_deflator_data[GDP_deflator_data['time_period'].str.contains("Q4")]
    +
    +
    +
    # Split the time_period into year and quarter and keep the year only
    +GDP_deflator_data.loc[:, 'time_period'] = GDP_deflator_data['time_period'].str[0:4]
    +
    +

    We made all the variables numeric.

    +
    +
    datasets = [GII_data, GDP_deflator_data, GDP_nominal_data, GDP_population_data]
    +
    +for i, dataset in enumerate(datasets):    
    +    # Use .loc to modify the columns
    +    datasets[i].loc[:, 'obs_value'] = pd.to_numeric(datasets[i]['obs_value'], errors='coerce')
    +    datasets[i].loc[:, 'time_period'] = pd.to_numeric(datasets[i]['time_period'], errors='coerce')
    +    datasets[i].loc[:, 'unit_mult'] = pd.to_numeric(datasets[i]['unit_mult'], errors='coerce')
    +
    +
    +
    +

    GII Percent Change: Dependent Variable

    +

    We kept percents as decimals to make them easy to work with for calculation. Different countries have different baseline level of economic growth and gender equality. We calculated the percent change to make them comparable.

    +
    +
    # Calculate percent change for each ref_area
    +GII_data[f'pct_change'] = GII_data.sort_values(['ref_area', 'time_period']).groupby('ref_area')['obs_value'].pct_change()
    +
    +# Display the first few rows of the updated dataset
    +GII_data.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    freqref_areaindicatorunit_multtime_formattime_periodobs_valuepct_change
    0AAFGE_GII0P1Y19900.828244NaN
    1AAFGE_GII0P1Y19910.817706-0.012723
    2AAFGE_GII0P1Y19920.809806-0.009662
    3AAFGE_GII0P1Y19930.803078-0.008308
    4AAFGE_GII0P1Y19940.797028-0.007533
    + +
    +
    +
    +

    We subset the data frame to keep only the columns we want:

    +
    +
    # Create a new dataframe with only the required columns
    +GII_data = GII_data[['ref_area', 'time_period', 'pct_change']].copy()
    +
    +# Display the first few rows of the new dataset
    +GII_data.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ref_areatime_periodpct_change
    0AF1990NaN
    1AF1991-0.012723
    2AF1992-0.009662
    3AF1993-0.008308
    4AF1994-0.007533
    + +
    +
    +
    +
    +
    +

    GDP: Independent Variable

    +

    We kept the columns we want only for GDP-related datasets for easier table merging.

    +
    +
    # GDP Deflator Dataset
    +# Create a new dataframe with only the required columns
    +GDP_deflator_data = GDP_deflator_data[['ref_area', 'time_period', 'unit_mult', 'obs_value']].copy()
    +
    +# Divide obs_value by 100
    +GDP_deflator_data['obs_value'] = GDP_deflator_data['obs_value'] / 100
    +
    +# Display the first few rows of the new dataset
    +GDP_deflator_data.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ref_areatime_periodunit_multobs_value
    3FI199000.732006
    7FI199100.739841
    11FI199200.746543
    15FI199300.756193
    19FI199400.779373
    + +
    +
    +
    +
    +
    # GDP Nominal Data
    +# Create a new dataframe with only the required columns
    +GDP_nominal_data = GDP_nominal_data[['ref_area', 'time_period', 'unit_mult','obs_value']].copy()
    +
    +# Display the first few rows of the new dataset
    +GDP_nominal_data.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ref_areatime_periodunit_multobs_value
    0NE200562418864.0
    1NE200662596972.0
    2NE200762762961.0
    3NE200863247835.0
    4NE200963403683.0
    + +
    +
    +
    +
    +
    # GDP Population Data 
    +# Create a new dataframe with only the required columns
    +GDP_population_data = GDP_population_data[['ref_area', 'time_period', 'unit_mult','obs_value']].copy()
    +
    +# Display the first few rows of the new dataset
    +GDP_population_data.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ref_areatime_periodunit_multobs_value
    0GA19503473.296
    1GA19513476.381
    2GA19523478.655
    3GA19533480.536
    4GA19543482.332
    + +
    +
    +
    +
    +
    # Combine all the datasets above for further calculation
    +merged_df = pd.merge(pd.merge(GDP_deflator_data,GDP_nominal_data, on=['time_period', 'ref_area'], suffixes=('_index', '_nominal'), how='inner'), GDP_population_data, on=['time_period', 'ref_area'], how='inner')
    +
    +# Display the GDP data in a web browser
    +# view_dataframe_in_browser(merged_df)
    +
    +# Display the first few rows of the dataset
    +merged_df.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ref_areatime_periodunit_mult_indexobs_value_indexunit_mult_nominalobs_value_nominalunit_multobs_value
    0FI199000.732006690964.034996.220
    1FI199100.739841686913.035019.134
    2FI199200.746543684786.035044.928
    3FI199300.756193685610.035071.782
    4FI199400.779373690646.035097.090
    + +
    +
    +
    +

    We want to adjust GDP data based on unit multiplier. Unit multiplier stands for the number of zeroes we need to add to the value column. For example, in 1950, the observed population data for country GA (Georgia) was 473.296. With a unit muliplier of 3, the adjusted population would be 473296.

    +
    +
    merged_df['adjusted_index'] = merged_df['obs_value_index'] * (10 ** (merged_df['unit_mult_index']))
    +merged_df['adjusted_nominal'] = merged_df['obs_value_nominal'] * (10 ** (merged_df['unit_mult_nominal']))
    +merged_df['adjusted_population'] = merged_df['obs_value'] * (10 ** (merged_df['unit_mult']))
    +
    +
    +
    # Merged dataset
    +# Create a new dataframe with only the required columns
    +merged_df = merged_df[['ref_area', 'time_period','adjusted_nominal', 'adjusted_index', 'adjusted_population']].copy()
    +
    +# Display the first few rows of the dataset
    +merged_df.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ref_areatime_periodadjusted_nominaladjusted_indexadjusted_population
    0FI19909.096400e+100.7320064996220.0
    1FI19918.691300e+100.7398415019134.0
    2FI19928.478600e+100.7465435044928.0
    3FI19938.561000e+100.7561935071782.0
    4FI19949.064600e+100.7793735097090.0
    + +
    +
    +
    +

    We wanted to compute the Real GDP per capita.

    +
    +
    # Step 1: Real GDP = (Nominal GDP / GDP Deflator Index)
    +merged_df['Real_GDP_domestic'] = (merged_df['adjusted_nominal'] / merged_df['adjusted_index'])
    +
    +# Step 2: Real GDP per Capita = Real GDP / Population
    +merged_df['Real_GDP_per_capita'] = merged_df['Real_GDP_domestic'] / merged_df['adjusted_population']
    +
    +# Check the results
    +merged_df.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ref_areatime_periodadjusted_nominaladjusted_indexadjusted_populationReal_GDP_domesticReal_GDP_per_capita
    0FI19909.096400e+100.7320064996220.01.242667e+1124872.143699
    1FI19918.691300e+100.7398415019134.01.174753e+1123405.490395
    2FI19928.478600e+100.7465435044928.01.135715e+1122512.011198
    3FI19938.561000e+100.7561935071782.01.132119e+1122321.919259
    4FI19949.064600e+100.7793735097090.01.163063e+1122818.181344
    + +
    +
    +
    +

    We calculated the percentage change in Real GDP per capita and put it in a new column.

    +
    +
    # Calculate percent change for each ref_area
    +merged_df[f'GDP_change'] = merged_df.sort_values(['ref_area', 'time_period']).groupby('ref_area')['Real_GDP_per_capita'].pct_change()
    +
    +# Rename dataset
    +GDP_data = merged_df
    +
    +# Display the first few rows of the dataset
    +GDP_data.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ref_areatime_periodadjusted_nominaladjusted_indexadjusted_populationReal_GDP_domesticReal_GDP_per_capitaGDP_change
    0FI19909.096400e+100.7320064996220.01.242667e+1124872.143699NaN
    1FI19918.691300e+100.7398415019134.01.174753e+1123405.490395-0.058968
    2FI19928.478600e+100.7465435044928.01.135715e+1122512.011198-0.038174
    3FI19938.561000e+100.7561935071782.01.132119e+1122321.919259-0.008444
    4FI19949.064600e+100.7793735097090.01.163063e+1122818.1813440.022232
    + +
    +
    +
    +
    +
    # GII and GDP
    +# Merge the datasets
    +combined_data = pd.merge(GII_data, GDP_data, on=["ref_area", "time_period"], how = "inner")
    +
    +# Check the combined dataset
    +combined_data.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ref_areatime_periodpct_changeadjusted_nominaladjusted_indexadjusted_populationReal_GDP_domesticReal_GDP_per_capitaGDP_change
    0AR2004-0.0218964.851153e+110.40967238491970.01.184155e+1230763.687776NaN
    1AR2005-0.0085815.825382e+110.47010838892924.01.239159e+1231860.7727760.035662
    2AR20060.0073797.159043e+110.52653539289876.01.359651e+1234605.6280840.086152
    3AR2007-0.0201178.969801e+110.62913939684303.01.425727e+1235926.7345750.038176
    4AR20080.0007331.149646e+120.75775340080159.01.517178e+1237853.6002520.053633
    + +
    +
    +
    +
    +
    # GII and GDP
    +# Rename columns
    +combined_data = combined_data.rename(columns = {
    +    "ref_area": "Country",
    +    "time_period": "Time",
    +    "pct_change": "GII_change"
    +})
    +
    +# Display the GDP data in a web browser
    +# view_dataframe_in_browser(combined_data)
    +
    +# Check the combined dataset
    +combined_data.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    CountryTimeGII_changeadjusted_nominaladjusted_indexadjusted_populationReal_GDP_domesticReal_GDP_per_capitaGDP_change
    0AR2004-0.0218964.851153e+110.40967238491970.01.184155e+1230763.687776NaN
    1AR2005-0.0085815.825382e+110.47010838892924.01.239159e+1231860.7727760.035662
    2AR20060.0073797.159043e+110.52653539289876.01.359651e+1234605.6280840.086152
    3AR2007-0.0201178.969801e+110.62913939684303.01.425727e+1235926.7345750.038176
    4AR20080.0007331.149646e+120.75775340080159.01.517178e+1237853.6002520.053633
    + +
    +
    +
    +
    +
    +
    +

    Data Visualization

    +
    +

    Scatterplot

    +

    Scatterplot use dots to represent values of two numeric variables. The horizontal axis was the percent change in Real GDP per capita. The vertical axis was the percent change in Gender Inequality Index(GII). Different colors represented different countries. We used a linear regression line to display the overall pattern.

    +

    Based on the scatterplot, it seemed like there was a slight positive relationship between GDP change and GII change as shown by the flat regression line. Gender inequality was decreasing (gender equality was improving) a little faster in country-years with low GDP growth and a little slower in country-years with high GDP growth.

    +
    +
    # Drop NAs
    +combined_data = combined_data.dropna(subset=['GDP_change', 'GII_change'])
    +
    +# Plot the data points
    +plt.figure(figsize=(18, 10))
    +for country in combined_data['Country'].unique():
    +    country_data = combined_data[combined_data['Country'] == country]
    +    plt.scatter(country_data['GDP_change'], country_data['GII_change'],
    +             marker='o',linestyle='-', label=country)
    +plt.title('Country-Year Analysis of GDP Change vs. GII Change')
    +plt.xlabel('Percent Change in Real GDP per Capita (Country-Year)')
    +plt.ylabel('Percent Change in GII (Country-Year)')
    +plt.grid(True)
    +
    +# Prepare data for linear regression
    +X = combined_data['GDP_change'].values.reshape(-1, 1)
    +y = combined_data['GII_change'].values
    +
    +# Perform linear regression
    +reg = LinearRegression().fit(X, y)
    +y_pred = reg.predict(X)
    +
    +# Plot the regression line
    +plt.plot(combined_data['GDP_change'], y_pred, color='red', linewidth=2)
    +
    +plt.show()
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Time Series Line Plot

    +

    We created separate line plots for GDP change and GII change over time for a few key countries might show the trends more clearly.

    +

    US: United States

    +

    JP: Japan

    +

    GB: United Kindom

    +

    FR: France

    +

    MX: Mexico

    +

    Based on the line plots, we saw GDP change and GII change have different patterns. For example, in Mexico, when there was a big change in real GDP per captia in 1995, the change in GII was pretty stable.

    +
    +
    # Time Series Line plot for a few key countries
    +selected_countries  = ['US', 'JP', 'GB', 'FR', 'MX']
    +combined_data_selected = combined_data[combined_data['Country'].isin(selected_countries)]
    +
    +# Set up the Plot Structure
    +fig, ax = plt.subplots(2, 1, figsize=(18, 10), sharex=True)
    +
    +# Plot change in real GDP per capita over time
    +sns.lineplot(data = combined_data_selected, x = "Time", y = "GDP_change", hue = "Country", ax = ax[0])
    +ax[0].set_title("Percent Change in Real GDP per Capita Over Time")
    +ax[0].set_ylabel("Percent Change in Real GDP per Capita")
    +
    +# Plot change in GII over time
    +sns.lineplot(data = combined_data_selected, x = "Time", y = "GII_change", hue = "Country", ax = ax[1])
    +ax[1].set_title("Percent Change in GII over Time")
    +ax[1].set_xlabel("Time")
    +ax[1].set_ylabel("GII")
    +
    +plt.tight_layout
    +plt.show()
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Barplot

    +

    We used a barplot to show average changes in GII and GDP percent change for each country to visualize regions where inequality was improving or worsening.

    +

    This plot supported our previous observation how GII change seemed to be not be correlated with GDP change. We also saw that, for country SI, Solvenia, there seems to be a large improvement in gender inequality.

    +
    +
    # Barplot using average GII and GDP change
    +# Calculate average change for each country
    +combined_data_avg = combined_data.groupby('Country')[['GII_change','GDP_change']].mean().reset_index()
    +
    +# Prepare to plot structure 
    +plt.figure(figsize = (18,10))
    +
    +# Create the barplot
    +combined_data_avg.plot(kind = 'bar', x = 'Country')
    +plt.ylabel('Average Change')
    +plt.xlabel('Country')
    +plt.legend(['GII change', 'GDP change'])
    +plt.grid(axis = 'y')
    +
    +# Show the plot
    +plt.show()
    +
    +
    <Figure size 1728x960 with 0 Axes>
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Boxplot

    +

    We used boxplot to visualize the distribution of GDP and GII change by country, providing information about spread, median, and potential outliers. To provide a more informative view, we sequenced countries in an ascending order by the median of percent change in GDP.

    +

    The boxplot displayed a slight upward trend with no obvious pattern between GDP and GII change. In coutries with higher GDP change median, they also tend to have a larger spread of the GDP change. The median of GII change remained stable regardless of the magnitude of GDP change, implying weak or no association between GDP and GII change. We observed a potential outlier for country SI, Solvenia, which may explained its large improvement in Gender inequality.

    +
    +
    # Box plot for GII and GDP change
    +# Melt the dataframe to long format for combined boxplot
    +combined_data_melted = combined_data.melt(id_vars=['Country'], value_vars=['GII_change', 'GDP_change'], 
    +                                          var_name='Change_Type', value_name='Value')
    +
    +gdp_medians = combined_data.groupby('Country')['GDP_change'].median().sort_values()
    +
    +combined_data_melted['Country'] = pd.Categorical(combined_data_melted['Country'], categories=gdp_medians.index, ordered= True)
    +
    +# Prepare the plot structure
    +plt.figure(figsize=(18, 10))
    +sns.boxplot(data = combined_data_melted, x = "Country", y = 'Value', hue = 'Change_Type')
    +plt.title('Distribution of GII and GDP change by Country')
    +plt.xlabel('Country')
    +plt.ylabel('Change')
    +plt.legend(title = 'Change Type')
    +
    +# Show the plot
    +plt.show()
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    Correlation Matrix

    +

    We created a heatmap to show the relationship between GII and GDP change.

    +

    A positive correlation coefficient indicates a positive relationship: the larger the GDP change, the larger the GII change. A negative correlation coefficient indicates a negative relationship: the larger the GDP change, the smaller the GII change. A correlation coefficient closer to 0 indicates there is weak or no relationship.

    +

    Based on the numeric values in the plot, there was a moderately strong positive correlation between GII and GDP change for country Estonia(EE) and Ireland(IE).

    +
    +
    # Calculate the correlation
    +country_correlation = combined_data.groupby('Country')[['GII_change', 'GDP_change']].corr().iloc[0::2, -1].reset_index(name='Correlation')
    +
    +# Put the correlation value in a matrix format
    +correlation_matrix = country_correlation.pivot(index='Country', columns='level_1', values='Correlation')
    +
    +# Check for NaN values in the correlation matrix
    +correlation_matrix.fillna(0, inplace=True)  # Replace NaNs with 0 or another value as appropriate
    +
    +# Set up the plot structure
    +plt.figure(figsize=(18, 12))  # Adjust height to give more space for y-axis labels
    +
    +# Plot the heatmap
    +sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0, cbar_kws={"shrink": .8}, linewidths=.5)
    +
    +# Enhance axis labels and title
    +plt.title('Heatmap for GII and GDP Change', fontsize=20)
    +plt.xlabel('Variables', fontsize=16)
    +plt.ylabel('Country', fontsize=16)
    +
    +# Improve readability of y-axis labels
    +plt.yticks(fontsize=12)  # Adjust the font size for y-axis labels
    +
    +# Show the plot
    +plt.show()
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +
    +

    Statistical Analysis

    +
    +

    Descriptive Statistics

    +

    There was a total of 915 data points. The mean of the GII change in -0.0314868, which indicated the overall grand mean percent change in gender inequality index is -3.15%. The mean of the GDP change was 0.0234633, showing the overall grand mean percent change in real GDP per capita was 2.35%.

    +
    +
    # Generate summary statistics
    +descriptive_stats = combined_data.describe()
    +print(tabulate(descriptive_stats, headers='keys', tablefmt='github'))
    +
    +
    |       |   GII_change |   adjusted_nominal |   adjusted_index |   adjusted_population |   Real_GDP_domestic |   Real_GDP_per_capita |   GDP_change |
    +|-------|--------------|--------------------|------------------|-----------------------|---------------------|-----------------------|--------------|
    +| count | 914          |      914           |      914         |         914           |       914           |         914           | 914          |
    +| mean  |  -0.0314506  |        7.85541e+13 |        0.855504  |           4.50061e+07 |         7.60737e+13 |           1.00595e+06 |   0.023532   |
    +| std   |   0.054783   |        6.01311e+14 |        0.210057  |           1.24493e+08 |         5.52717e+14 |           3.88155e+06 |   0.0418127  |
    +| min   |  -0.755003   |        2.18714e+09 |        0.0360636 |      396324           |         5.21733e+09 |        1798.42        |  -0.285847   |
    +| 25%   |  -0.0424105  |        1.07655e+11 |        0.743355  |           5.0737e+06  |         1.48206e+11 |       17176           |   0.00384809 |
    +| 50%   |  -0.020248   |        7.50706e+11 |        0.888883  |           1.0337e+07  |         9.94933e+11 |       35401           |   0.0222486  |
    +| 75%   |  -0.00451301 |        2.5911e+12  |        1.00175   |           4.5159e+07  |         2.9412e+12  |      120391           |   0.0427318  |
    +| max   |   0.156992   |        9.54613e+15 |        2.0788    |           1.28084e+09 |         7.92007e+15 |           3.14532e+07 |   0.241984   |
    +
    +
    +
    +
    +

    Regression Analysis

    +

    Simple linear regression as a foundational approach provide us with a basic understanding of the relationship between GDP change and GII change.

    +

    Based on the summary, we concluded the following:

    +
      +
    • Becasue p-value = 0.057, if we set alpha, the significance level, to be 0.05, we failed to reject the null hypothesis and conclude there was no significant relationship between percent change in real GDP per capita and gender inequality index.

    • +
    • R-squared = 0.004. Only 0.4% of the variance in GII change could be explained by GDP change.

    • +
    • We were 95% confident that the interval from -0.003 to 0.169 captured the true slope of GDP change. Because 0 was included, we are uncertain about the effect of GDP change on GII chnage.

    • +
    +
    +
    # Define independent and depenent variables
    +X = combined_data['GDP_change']
    +y = combined_data['GII_change']
    +
    +# Add a constant to indepdent variable to include an intercept
    +X = sm.add_constant(X)
    +
    +# Fit a simple linear regresion model and print out the summary
    +model = sm.OLS(y, X).fit()
    +print(model.summary())
    +
    +
                                OLS Regression Results                            
    +==============================================================================
    +Dep. Variable:             GII_change   R-squared:                       0.004
    +Model:                            OLS   Adj. R-squared:                  0.003
    +Method:                 Least Squares   F-statistic:                     3.551
    +Date:                Wed, 25 Dec 2024   Prob (F-statistic):             0.0598
    +Time:                        18:00:11   Log-Likelihood:                 1360.0
    +No. Observations:                 914   AIC:                            -2716.
    +Df Residuals:                     912   BIC:                            -2706.
    +Df Model:                           1                                         
    +Covariance Type:            nonrobust                                         
    +==============================================================================
    +                 coef    std err          t      P>|t|      [0.025      0.975]
    +------------------------------------------------------------------------------
    +const         -0.0334      0.002    -16.069      0.000      -0.037      -0.029
    +GDP_change     0.0816      0.043      1.885      0.060      -0.003       0.167
    +==============================================================================
    +Omnibus:                      841.660   Durbin-Watson:                   1.384
    +Prob(Omnibus):                  0.000   Jarque-Bera (JB):            53999.190
    +Skew:                          -3.938   Prob(JB):                         0.00
    +Kurtosis:                      39.823   Cond. No.                         23.9
    +==============================================================================
    +
    +Notes:
    +[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
    +
    +
    +
    +
    +

    Time Series Analysis

    +

    Time series analysis allows us to explore how the relationship between GII and GDP change vary across different time periods, accounting for lagged effects.

    +

    Here was a quick summary of the result: * Both GII and GDP change time series were stationary.

    +
      +
    • Past GII change values significantly influenced cuurent GII change values.

    • +
    • VAR model had good model performance on forecasting future values based on historical data.

    • +
    • Changes in GDP did not cause/precde the changes in GII.

    • +
    +
    +

    ADF Test: Stationality Assumption Check

    +

    We wanted to use Augmented Dickey-Fuller (ADF) test to check whether a time series was stationary, which was the model assumption for many time series models.

    +

    Stationarity implied constant mean and variance over time, making it more predictable and stable for forecasting.

    +

    Based on the ADF test output, both GII and GDP change time series were stationary. We proceeded to the time series modeling section.

    +
    +
    # Augmented Dickey-Fuller (ADF) test for stationarity check
    +# Create melted datasets
    +combined_data_time = combined_data.melt(id_vars=['Time', 'Country'], value_vars=['GII_change','GDP_change'], var_name = 'Change_Type', value_name = 'Value')
    +GII = combined_data_time[(combined_data_time['Change_Type'] == 'GII_change')]                         
    +
    +GDP = combined_data_time[(combined_data_time['Change_Type'] == 'GDP_change')]
    +
    +# Stationary Check
    +def adf_test(series):
    +    result = adfuller(series.dropna())
    +    print(f'ADF Statistic: {result[0]}')
    +    print(f'p-value: {result[1]}')
    +    if result[1] < 0.05:
    +        print("Series is stationary")
    +    else:
    +        print("Series is not stationary")
    +
    +# Output the result
    +adf_test(GII['Value'])
    +adf_test(GDP['Value'])
    +
    +
    ADF Statistic: -16.069430375623295
    +p-value: 5.527565277907626e-29
    +Series is stationary
    +ADF Statistic: -8.535683414310181
    +p-value: 1.0095278443825336e-13
    +Series is stationary
    +
    +
    +
    +
    +

    VAR model: Examine variables separately

    +

    We fitted a VAR (Vector Autoreression) model to see the relationship between GII and GDP change. VAR is particularly useful when dealing with multivariate time series data and allows us to examine the interdependence between variables.

    +

    Based on summary, here were several interpretations we could make:

    +
      +
    • We used AIC as the criteria for model selection. Lower value suggests a better fit.

    • +
    • Given that we wanted to predict GII change, we focused on the first set “Results for equation GII_change.”

    • +
    • Past GII_change values significantly influenced current GII_change, as shown in the small p-values of lags 1 and 2.

    • +
    • Lag 2 of GDP_change had a relatively low p-value but is not statistically significant.

    • +
    +
    +
    # Split the dataset into training and testing sets
    +split_ratio = 0.7
    +split_index = int(len(combined_data) * split_ratio)
    +
    +# Training set is used to fit the model
    +train_data = combined_data.iloc[:split_index]
    +
    +# Testing set is used for validation
    +test_data = combined_data.iloc[split_index:]
    +
    +print(f"Training data: {train_data.shape}")
    +print(f"Test data: {test_data.shape}")
    +
    +
    Training data: (639, 9)
    +Test data: (275, 9)
    +
    +
    +
    +
    # Fit a VAR model 
    +time_model = VAR(train_data[['GII_change', 'GDP_change']])
    +time_model_fitted = time_model.fit(maxlags = 15, ic="aic")
    +
    +# Print out the model summary
    +print(time_model_fitted.summary())
    +
    +
      Summary of Regression Results   
    +==================================
    +Model:                         VAR
    +Method:                        OLS
    +Date:           Wed, 25, Dec, 2024
    +Time:                     18:00:11
    +--------------------------------------------------------------------
    +No. of Equations:         2.00000    BIC:                   -12.2270
    +Nobs:                     636.000    HQIC:                  -12.2869
    +Log likelihood:           2128.47    FPE:                4.43927e-06
    +AIC:                     -12.3250    Det(Omega_mle):     4.34314e-06
    +--------------------------------------------------------------------
    +Results for equation GII_change
    +================================================================================
    +                   coefficient       std. error           t-stat            prob
    +--------------------------------------------------------------------------------
    +const                -0.023760         0.003176           -7.481           0.000
    +L1.GII_change         0.211342         0.039900            5.297           0.000
    +L1.GDP_change        -0.015713         0.046080           -0.341           0.733
    +L2.GII_change         0.111672         0.040402            2.764           0.006
    +L2.GDP_change         0.088293         0.046452            1.901           0.057
    +L3.GII_change        -0.021861         0.039774           -0.550           0.583
    +L3.GDP_change         0.000651         0.046141            0.014           0.989
    +================================================================================
    +
    +Results for equation GDP_change
    +================================================================================
    +                   coefficient       std. error           t-stat            prob
    +--------------------------------------------------------------------------------
    +const                 0.014585         0.002720            5.362           0.000
    +L1.GII_change         0.004128         0.034171            0.121           0.904
    +L1.GDP_change         0.141566         0.039464            3.587           0.000
    +L2.GII_change        -0.009734         0.034600           -0.281           0.778
    +L2.GDP_change         0.044282         0.039782            1.113           0.266
    +L3.GII_change        -0.037787         0.034063           -1.109           0.267
    +L3.GDP_change         0.143870         0.039516            3.641           0.000
    +================================================================================
    +
    +Correlation matrix of residuals
    +              GII_change  GDP_change
    +GII_change      1.000000    0.040992
    +GDP_change      0.040992    1.000000
    +
    +
    +
    +
    +
    +
    /Users/sammi/Library/Caches/pypoetry/virtualenvs/imfp-0AlIckOY-py3.13/lib/python3.13/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: An unsupported index was provided. As a result, forecasts cannot be generated. To use the model for forecasting, use one of the supported classes of index.
    +  self._init_dates(dates, freq)
    +
    +
    +
    +
    +

    VAR Model: Forecasting

    +

    We applied the model learned above to the test data. Based on the plot, the forecast values seem to follow the actual data well, indicating a good model fit caputuring the underlying trends.

    +
    +
    # Number of steps to forecast (length of the test set)
    +n_steps = len(test_data)
    +
    +# Get the last values from the training set for forecasting
    +forecast_input = train_data[['GII_change', 'GDP_change']].values[-time_model_fitted.k_ar:]
    +
    +# Forecasting
    +forecast = time_model_fitted.forecast(y=forecast_input, steps=n_steps)
    +
    +# Create a DataFrame for the forecasted values
    +forecast_df = pd.DataFrame(forecast, index=test_data.index, columns=['GII_forecast', 'GDP_forecast'])
    +
    +# Ensure the index of the forecast_df matches the test_data index
    +forecast_df.index = test_data.index
    +
    +
    +
    plt.figure(figsize=(12, 6))
    +plt.plot(train_data['GII_change'], label='Training GII', color='blue')
    +plt.plot(test_data['GII_change'], label='Actual GII', color='orange')
    +plt.plot(forecast_df['GII_forecast'], label='Forecasted GII', color='green')
    +plt.title('GII Change Forecast vs Actual')
    +plt.legend()
    +plt.show()
    +
    +plt.figure(figsize=(12, 6))
    +plt.plot(train_data['GDP_change'], label='Training GDP', color='blue')
    +plt.plot(test_data['GDP_change'], label='Actual GDP', color='orange')
    +plt.plot(forecast_df['GDP_forecast'], label='Forecasted GDP', color='green')
    +plt.title('GDP Change Forecast vs Actual')
    +plt.legend()
    +plt.show()
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    VAR Model: Model Performance

    +

    Low values of both MAE and RMSE indicate good model performance with small average errors in predictions.

    +
    +
    mae_gii = mean_absolute_error(test_data['GII_change'], forecast_df['GII_forecast'])
    +mae_gdp = mean_absolute_error(test_data['GDP_change'], forecast_df['GDP_forecast'])
    +
    +print(f'Mean Absolute Error for GII: {mae_gii}')
    +print(f'Mean Absolute Error for GDP: {mae_gdp}')
    +
    +
    Mean Absolute Error for GII: 0.03296113056900485
    +Mean Absolute Error for GDP: 0.027636232780964363
    +
    +
    +
    +
    rmse_gii = np.sqrt(mean_squared_error(test_data['GII_change'], forecast_df['GII_forecast']))
    +rmse_gdp = np.sqrt(mean_squared_error(test_data['GDP_change'], forecast_df['GDP_forecast']))
    +
    +print(f'RMSE for GII: {rmse_gii}')
    +print(f'RMSE for GDP: {rmse_gdp}')
    +
    +
    RMSE for GII: 0.06284754560659174
    +RMSE for GDP: 0.038429216092797085
    +
    +
    +
    +
    +

    VAR Model: Granger causality test

    +

    Granger causality test evaluates whether one time series can predict another.

    +

    Based on the output, the lowest p-value is when lag = 2. However, because p-value > 0.05, we fail to reject the null hypothesis and conclude the GDP_change does not Granger-cause the GII_change.

    +
    +
    # Perform the Granger causality test
    +max_lag = 3
    +test_result = grangercausalitytests(train_data[['GII_change', 'GDP_change']], max_lag, verbose=True)
    +
    +
    
    +Granger Causality
    +number of lags (no zero) 1
    +ssr based F test:         F=0.0000  , p=0.9972  , df_denom=635, df_num=1
    +ssr based chi2 test:   chi2=0.0000  , p=0.9972  , df=1
    +likelihood ratio test: chi2=0.0000  , p=0.9972  , df=1
    +parameter F test:         F=0.0000  , p=0.9972  , df_denom=635, df_num=1
    +
    +Granger Causality
    +number of lags (no zero) 2
    +ssr based F test:         F=1.8529  , p=0.1576  , df_denom=632, df_num=2
    +ssr based chi2 test:   chi2=3.7351  , p=0.1545  , df=2
    +likelihood ratio test: chi2=3.7242  , p=0.1553  , df=2
    +parameter F test:         F=1.8529  , p=0.1576  , df_denom=632, df_num=2
    +
    +Granger Causality
    +number of lags (no zero) 3
    +ssr based F test:         F=1.2359  , p=0.2958  , df_denom=629, df_num=3
    +ssr based chi2 test:   chi2=3.7489  , p=0.2899  , df=3
    +likelihood ratio test: chi2=3.7379  , p=0.2912  , df=3
    +parameter F test:         F=1.2359  , p=0.2958  , df_denom=629, df_num=3
    +
    +
    +
    /Users/sammi/Library/Caches/pypoetry/virtualenvs/imfp-0AlIckOY-py3.13/lib/python3.13/site-packages/statsmodels/tsa/stattools.py:1556: FutureWarning: verbose is deprecated since functions should not print results
    +  warnings.warn(
    +
    +
    +
    +
    +
    +
    +

    Conclusion

    +

    In wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research. Check out the blog for full analysis!

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    Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data

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    Author
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    Jenny Xu

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    December 25, 2024

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    Introduction Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders? Our main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables. Ready for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!

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    Data Preparation ​​To explore how economic growth connects with gender equality, we turned to the Imfp library to access a wealth of global economic data, ready to analyze through some straightforward API calls. Let’s break down our two main variables of interest. Here’s the lowdown on the 4 key data series we pulled together:

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    Dependent Variable: Percent Change in Gender Inequality Index (GII) Our gender equality measure, the Gender Inequality Index (GII), is a composite score that reflects inequality in three key areas: reproductive health, empowerment, and labor market, ranging from 0 (equality) to 1 (inequality). We calculated the percent change in GII over time to make it easier to compare GII change across countries with different baseline levels of inequality.

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    Independent Variable: Percent Change in Real GDP per capita Our economic growth measure is real GDP per capita, an indicator of a country’s economic output per person, adjusted for inflation. The higher the real GDP per capita, the wealthier and more prosperous a country is. Here is a peek at the formula:

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    Data Visualization Scatterplot

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    Nothing like a good scatterplot to help us understand the relationship between two numbers, right? For our project, the scatterplot lets us visually explore how Percent Change in Real GDP per capita relates to Percent Change in GII. Each dot on the plot represents a country-year combo. The gentle upward slope suggests that in years of low GDP growth, gender inequality tends to decrease a bit faster, while in years of high GDP growth, the inequality drops at a slightly slower rate. It’s a subtle clue, but slower-growing economies might be making slightly quicker strides in gender equality. Time Series Line plot

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    The time series line plot gives us the big picture of GDP and GII changes over time across a few select countries, including the United States, Japan, the United Kingdom, France, and Mexico. Mexico, for example, saw great GDP swings in 1995, yet its GII stayed relatively stable. Meanwhile, Japan and France saw some dramatic GII shifts. These fluctuations leave us wondering: what historical events sparked these changes? We may need to take a deeper look into each country’s unique timeline to find the answers.

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    Boxplot

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    Next, we have the boxplot to help us visualize the distribution. By sequencing countries in ascending order of median GDP change, we see an interesting trend: countries with higher median GDP growth also seem to show more variability in their GDP changes. The GII change, on the other hand, keeps a relatively stable median, hinting at a weaker association with GDP growth.

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    Heatmap

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    Finally, our heatmap wraps it all up with a quick, intuitive look at correlations between GDP and GII across countries. In countries like Estonia (EE) and Ireland (IE), Australia (AT), South Africa (ZA), Thailand (TH), Slovenia (SI), we see positive correlations in warm colors, indicating that as GDP growth rates increase, GII growth rates tend to rise as well. For countries like Cyprus (CY), Romania (RO), and the United States (US), we see cooler colors, representing negative correlations: as GDP growth rate rises, GII growth rates decrease, suggesting an improvement in gender equality alongside economic growth. This color-coded comparison leaves us curious—could the distinction between developed and developing countries play a role in these relationships? It’s a question that might inspire future research directions!

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    Statistical Analysis Descriptive Statistics Let’s start with a quick overview of our descriptive statistics: our dataset includes 915 data points. On average, GII change is -3.15%, indicating an overall reduction in gender inequality over time. Meanwhile, GDP change averages 2.35%, hinting at modest economic growth. A full breakdown of these values can be found in the table below for more detail.

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    Regression Analysis To get a sense of any relationship between GDP change and GII change, we ran a simple linear regression. Here’s what we found: P-value: With a p-value of 0.057, we fail to reject the null hypothesis. There is no statistically significant relationship. In other words, we don’t find strong evidence of a link between GDP and GII changes. R-squared: GDP change explains only 0.4% of the variation in GII change—a pretty minimal effect. 95% Confidence Interval: We’re 95% confident the interval from -0.003 to 0.169 captures the true slope. The effect of GDP change on GII is uncertain as 0 is within this range.

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    Time Series Analysis Time series analysis allows us to dig deeper into how GII and GDP changes play out over time—and potentially influence one another. Here’s the rundown: Stationarity check: Both GII and GDP change series are stationary, meaning their means and variances are stable over time, setting us up for reliable forecasting. VAR Model (Vector Autoregression): This multivariate model lets us explore how past values in both series relate to current values. Notably, past GII changes significantly impact current GII changes. Model Performance: The VAR model has good fit, as shown by the forecast closely tracking actual values. Low MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) confirm that our model predicts GII and GDP change with minimal error. Check out the plots and table below! Granger Causality Test: Curious whether GDP changes can predict changes in GII? The Granger causality test says no. With p-values above 0.05, we fail to find evidence that GDP changes drive GII changes. This suggests that while both variables evolve over time, they don’t seem to directly influence each other.

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    Conclusion In wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research. In terms of the Imfp library, we gained valuable experience working with real-world economic indicators like GDP and the GII. The library’s function in fetching databases, parameters, and datasets made it straightforward to access IMF’s data, allowing us to focus on analysis instead. The integration of other Python tools—such as seaborn, matplotlib, and numpy—not only streamlined our computational processes but also ensured consistency across the project. Future Directions First, we must consider what other factors might influence the relationship between GDP and GII change. The GII is a composite index, shaped by a myriad of social factors, including cultural norms, legal frameworks, and environmental shifts. Future studies could benefit from incorporating additional predictors into the analysis and exploring the interaction between economic growth and gender equality within specific country contexts. Second, there’s potential to enhance the predictive power of our Vector Autoregression (VAR) time series model. While we established that GDP change does not cause GII change, our model performed well in forecasting trends for both variables independently. In practice, policymakers may want to forecast GII trends independently of GDP if they are implementing gender-focused policies. Future research could investigate time series modeling to further unravel the dynamics of GII and GDP changes. So, as we wrap up this chapter, let’s keep our curiosity alive and our questions flowing. After all, every end is just a new beginning in the quest for knowledge!

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    Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data

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    + Apply imfp in a real world data-analysis project. +
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    Data Analysis
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    Author
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    Jenny Xu

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    Published
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    December 25, 2024

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    Introduction

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    Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders?

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    Our main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables.

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    Ready for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!

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    Data Preparation

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    ​​To explore how economic growth connects with gender equality, we turned to the Imfp library to access a wealth of global economic data, ready to analyze through some straightforward API calls. Let’s break down our two main variables of interest. Here’s the lowdown on the 4 key data series we pulled together:

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    Dependent Variable: Percent Change in Gender Inequality Index (GII)

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    Our gender equality measure, the Gender Inequality Index (GII), is a composite score that reflects inequality in three key areas: reproductive health, empowerment, and labor market, ranging from 0 (equality) to 1 (inequality). We calculated the percent change in GII over time to make it easier to compare GII change across countries with different baseline levels of inequality.

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    Independent Variable: Percent Change in Real GDP per capita

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    Our economic growth measure is real GDP per capita, an indicator of a country’s economic output per person, adjusted for inflation. The higher the real GDP per capita, the wealthier and more prosperous a country is. Here is a peek at the formula:

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    Data Visualization

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    Scatterplot

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    Nothing like a good scatterplot to help us understand the relationship between two numbers, right? For our project, the scatterplot lets us visually explore how Percent Change in Real GDP per capita relates to Percent Change in GII. Each dot on the plot represents a country-year combo. The gentle upward slope suggests that in years of low GDP growth, gender inequality tends to decrease a bit faster, while in years of high GDP growth, the inequality drops at a slightly slower rate. It’s a subtle clue, but slower-growing economies might be making slightly quicker strides in gender equality.

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    Time Series Line plot

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    The time series line plot gives us the big picture of GDP and GII changes over time across a few select countries, including the United States, Japan, the United Kingdom, France, and Mexico. Mexico, for example, saw great GDP swings in 1995, yet its GII stayed relatively stable. Meanwhile, Japan and France saw some dramatic GII shifts. These fluctuations leave us wondering: what historical events sparked these changes? We may need to take a deeper look into each country’s unique timeline to find the answers.

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    Boxplot

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    Next, we have the boxplot to help us visualize the distribution. By sequencing countries in ascending order of median GDP change, we see an interesting trend: countries with higher median GDP growth also seem to show more variability in their GDP changes. The GII change, on the other hand, keeps a relatively stable median, hinting at a weaker association with GDP growth.

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    Heatmap

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    Finally, our heatmap wraps it all up with a quick, intuitive look at correlations between GDP and GII across countries. In countries like Estonia (EE) and Ireland (IE), Australia (AT), South Africa (ZA), Thailand (TH), Slovenia (SI), we see positive correlations in warm colors, indicating that as GDP growth rates increase, GII growth rates tend to rise as well. For countries like Cyprus (CY), Romania (RO), and the United States (US), we see cooler colors, representing negative correlations: as GDP growth rate rises, GII growth rates decrease, suggesting an improvement in gender equality alongside economic growth. This color-coded comparison leaves us curious—could the distinction between developed and developing countries play a role in these relationships? It’s a question that might inspire future research directions!

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    Statistical Analysis

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    Descriptive Statistics

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    Let’s start with a quick overview of our descriptive statistics: our dataset includes 915 data points. On average, GII change is -3.15%, indicating an overall reduction in gender inequality over time. Meanwhile, GDP change averages 2.35%, hinting at modest economic growth. A full breakdown of these values can be found in the table below for more detail.

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    Regression Analysis

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    To get a sense of any relationship between GDP change and GII change, we ran a simple linear regression. Here’s what we found:

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    • P-value: With a p-value of 0.057, we fail to reject the null hypothesis. There is no statistically significant relationship. In other words, we don’t find strong evidence of a link between GDP and GII changes.

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    • R-squared: GDP change explains only 0.4% of the variation in GII change—a pretty minimal effect.

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    • 95% Confidence Interval: We’re 95% confident the interval from -0.003 to 0.169 captures the true slope. The effect of GDP change on GII is uncertain as 0 is within this range.

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    Time Series Analysis

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    Time series analysis allows us to dig deeper into how GII and GDP changes play out over time—and potentially influence one another. Here’s the rundown:

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    • Stationarity check: Both GII and GDP change series are stationary, meaning their means and variances are stable over time, setting us up for reliable forecasting.

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    • VAR Model (Vector Autoregression): This multivariate model lets us explore how past values in both series relate to current values. Notably, past GII changes significantly impact current GII changes.

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    • Model Performance: The VAR model has good fit, as shown by the forecast closely tracking actual values. Low MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) confirm that our model predicts GII and GDP change with minimal error. Check out the plots and table below!

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    • Granger Causality Test: Curious whether GDP changes can predict changes in GII? The Granger causality test says no. With p-values above 0.05, we fail to find evidence that GDP changes drive GII changes. This suggests that while both variables evolve over time, they don’t seem to directly influence each other.

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    Conclusion

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    In wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research.

    +

    In terms of the Imfp library, we gained valuable experience working with real-world economic indicators like GDP and the GII. The library’s function in fetching databases, parameters, and datasets made it straightforward to access IMF’s data, allowing us to focus on analysis instead. The integration of other Python tools—such as seaborn, matplotlib, and numpy—not only streamlined our computational processes but also ensured consistency across the project.

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    Future Directions

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    First, we must consider what other factors might influence the relationship between GDP and GII change. The GII is a composite index, shaped by a myriad of social factors, including cultural norms, legal frameworks, and environmental shifts. Future studies could benefit from incorporating additional predictors into the analysis and exploring the interaction between economic growth and gender equality within specific country contexts.

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    Second, there’s potential to enhance the predictive power of our Vector Autoregression (VAR) time series model. While we established that GDP change does not cause GII change, our model performed well in forecasting trends for both variables independently. In practice, policymakers may want to forecast GII trends independently of GDP if they are implementing gender-focused policies. Future research could investigate time series modeling to further unravel the dynamics of GII and GDP changes.

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    So, as we wrap up this chapter, let’s keep our curiosity alive and our questions flowing. After all, every end is just a new beginning in the quest for knowledge!

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imfp Demo + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

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    Blog

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    + + + + + \ No newline at end of file diff --git a/demo/demo/_site/posts/welcome/index.html b/demo/demo/_site/posts/welcome/index.html new file mode 100644 index 0000000..0fe0bc1 --- /dev/null +++ b/demo/demo/_site/posts/welcome/index.html @@ -0,0 +1,590 @@ + + + + + + + + + + + +Welcome To My Blog – blog + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Welcome To My Blog

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    news
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    Author
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    Tristan O’Malley

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    Published
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    December 22, 2024

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    This is the first post in a Quarto blog. Welcome!

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    Since this post doesn’t specify an explicit image, the first image in the post will be used in the listing page of posts.

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It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders? Our main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables. Ready for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!\nData Preparation ​​To explore how economic growth connects with gender equality, we turned to the Imfp library to access a wealth of global economic data, ready to analyze through some straightforward API calls. Let’s break down our two main variables of interest. Here’s the lowdown on the 4 key data series we pulled together:\nDependent Variable: Percent Change in Gender Inequality Index (GII) Our gender equality measure, the Gender Inequality Index (GII), is a composite score that reflects inequality in three key areas: reproductive health, empowerment, and labor market, ranging from 0 (equality) to 1 (inequality). We calculated the percent change in GII over time to make it easier to compare GII change across countries with different baseline levels of inequality.\nIndependent Variable: Percent Change in Real GDP per capita Our economic growth measure is real GDP per capita, an indicator of a country’s economic output per person, adjusted for inflation. The higher the real GDP per capita, the wealthier and more prosperous a country is. Here is a peek at the formula:\nData Visualization Scatterplot\nNothing like a good scatterplot to help us understand the relationship between two numbers, right? For our project, the scatterplot lets us visually explore how Percent Change in Real GDP per capita relates to Percent Change in GII. Each dot on the plot represents a country-year combo. The gentle upward slope suggests that in years of low GDP growth, gender inequality tends to decrease a bit faster, while in years of high GDP growth, the inequality drops at a slightly slower rate. It’s a subtle clue, but slower-growing economies might be making slightly quicker strides in gender equality. Time Series Line plot\nThe time series line plot gives us the big picture of GDP and GII changes over time across a few select countries, including the United States, Japan, the United Kingdom, France, and Mexico. Mexico, for example, saw great GDP swings in 1995, yet its GII stayed relatively stable. Meanwhile, Japan and France saw some dramatic GII shifts. These fluctuations leave us wondering: what historical events sparked these changes? We may need to take a deeper look into each country’s unique timeline to find the answers.\nBoxplot\nNext, we have the boxplot to help us visualize the distribution. By sequencing countries in ascending order of median GDP change, we see an interesting trend: countries with higher median GDP growth also seem to show more variability in their GDP changes. The GII change, on the other hand, keeps a relatively stable median, hinting at a weaker association with GDP growth.\nHeatmap\nFinally, our heatmap wraps it all up with a quick, intuitive look at correlations between GDP and GII across countries. In countries like Estonia (EE) and Ireland (IE), Australia (AT), South Africa (ZA), Thailand (TH), Slovenia (SI), we see positive correlations in warm colors, indicating that as GDP growth rates increase, GII growth rates tend to rise as well. For countries like Cyprus (CY), Romania (RO), and the United States (US), we see cooler colors, representing negative correlations: as GDP growth rate rises, GII growth rates decrease, suggesting an improvement in gender equality alongside economic growth. This color-coded comparison leaves us curious—could the distinction between developed and developing countries play a role in these relationships? It’s a question that might inspire future research directions!\nStatistical Analysis Descriptive Statistics Let’s start with a quick overview of our descriptive statistics: our dataset includes 915 data points. On average, GII change is -3.15%, indicating an overall reduction in gender inequality over time. Meanwhile, GDP change averages 2.35%, hinting at modest economic growth. A full breakdown of these values can be found in the table below for more detail.\nRegression Analysis To get a sense of any relationship between GDP change and GII change, we ran a simple linear regression. Here’s what we found: P-value: With a p-value of 0.057, we fail to reject the null hypothesis. There is no statistically significant relationship. In other words, we don’t find strong evidence of a link between GDP and GII changes. R-squared: GDP change explains only 0.4% of the variation in GII change—a pretty minimal effect. 95% Confidence Interval: We’re 95% confident the interval from -0.003 to 0.169 captures the true slope. The effect of GDP change on GII is uncertain as 0 is within this range.\nTime Series Analysis Time series analysis allows us to dig deeper into how GII and GDP changes play out over time—and potentially influence one another. Here’s the rundown: Stationarity check: Both GII and GDP change series are stationary, meaning their means and variances are stable over time, setting us up for reliable forecasting. VAR Model (Vector Autoregression): This multivariate model lets us explore how past values in both series relate to current values. Notably, past GII changes significantly impact current GII changes. Model Performance: The VAR model has good fit, as shown by the forecast closely tracking actual values. Low MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) confirm that our model predicts GII and GDP change with minimal error. Check out the plots and table below! Granger Causality Test: Curious whether GDP changes can predict changes in GII? The Granger causality test says no. With p-values above 0.05, we fail to find evidence that GDP changes drive GII changes. This suggests that while both variables evolve over time, they don’t seem to directly influence each other.\nConclusion In wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research. In terms of the Imfp library, we gained valuable experience working with real-world economic indicators like GDP and the GII. The library’s function in fetching databases, parameters, and datasets made it straightforward to access IMF’s data, allowing us to focus on analysis instead. The integration of other Python tools—such as seaborn, matplotlib, and numpy—not only streamlined our computational processes but also ensured consistency across the project. Future Directions First, we must consider what other factors might influence the relationship between GDP and GII change. The GII is a composite index, shaped by a myriad of social factors, including cultural norms, legal frameworks, and environmental shifts. Future studies could benefit from incorporating additional predictors into the analysis and exploring the interaction between economic growth and gender equality within specific country contexts. Second, there’s potential to enhance the predictive power of our Vector Autoregression (VAR) time series model. While we established that GDP change does not cause GII change, our model performed well in forecasting trends for both variables independently. In practice, policymakers may want to forecast GII trends independently of GDP if they are implementing gender-focused policies. Future research could investigate time series modeling to further unravel the dynamics of GII and GDP changes. So, as we wrap up this chapter, let’s keep our curiosity alive and our questions flowing. After all, every end is just a new beginning in the quest for knowledge!" + }, + { + "objectID": "posts/welcome/index.html", + "href": "posts/welcome/index.html", + "title": "Welcome To My Blog", + "section": "", + "text": "This is the first post in a Quarto blog. Welcome!\n\nSince this post doesn’t specify an explicit image, the first image in the post will be used in the listing page of posts." + }, + { + "objectID": "posts/blog/Blog.html", + "href": "posts/blog/Blog.html", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "", + "text": "Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders?\nOur main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables.\nReady for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!" + }, + { + "objectID": "posts/blog/Blog.html#introduction", + "href": "posts/blog/Blog.html#introduction", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "", + "text": "Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders?\nOur main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables.\nReady for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!" + }, + { + "objectID": "posts/blog/Blog.html#data-preparation", + "href": "posts/blog/Blog.html#data-preparation", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "Data Preparation", + "text": "Data Preparation\n​​To explore how economic growth connects with gender equality, we turned to the Imfp library to access a wealth of global economic data, ready to analyze through some straightforward API calls. Let’s break down our two main variables of interest. Here’s the lowdown on the 4 key data series we pulled together:\n\n\nDependent Variable: Percent Change in Gender Inequality Index (GII)\nOur gender equality measure, the Gender Inequality Index (GII), is a composite score that reflects inequality in three key areas: reproductive health, empowerment, and labor market, ranging from 0 (equality) to 1 (inequality). We calculated the percent change in GII over time to make it easier to compare GII change across countries with different baseline levels of inequality.\n\n\nIndependent Variable: Percent Change in Real GDP per capita\nOur economic growth measure is real GDP per capita, an indicator of a country’s economic output per person, adjusted for inflation. The higher the real GDP per capita, the wealthier and more prosperous a country is. Here is a peek at the formula:" + }, + { + "objectID": "posts/blog/Blog.html#data-visualization", + "href": "posts/blog/Blog.html#data-visualization", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "Data Visualization", + "text": "Data Visualization\n\nScatterplot\nNothing like a good scatterplot to help us understand the relationship between two numbers, right? For our project, the scatterplot lets us visually explore how Percent Change in Real GDP per capita relates to Percent Change in GII. Each dot on the plot represents a country-year combo. The gentle upward slope suggests that in years of low GDP growth, gender inequality tends to decrease a bit faster, while in years of high GDP growth, the inequality drops at a slightly slower rate. It’s a subtle clue, but slower-growing economies might be making slightly quicker strides in gender equality.\n \n\n\nTime Series Line plot\nThe time series line plot gives us the big picture of GDP and GII changes over time across a few select countries, including the United States, Japan, the United Kingdom, France, and Mexico. Mexico, for example, saw great GDP swings in 1995, yet its GII stayed relatively stable. Meanwhile, Japan and France saw some dramatic GII shifts. These fluctuations leave us wondering: what historical events sparked these changes? We may need to take a deeper look into each country’s unique timeline to find the answers.\n \n\n\nBoxplot\nNext, we have the boxplot to help us visualize the distribution. By sequencing countries in ascending order of median GDP change, we see an interesting trend: countries with higher median GDP growth also seem to show more variability in their GDP changes. The GII change, on the other hand, keeps a relatively stable median, hinting at a weaker association with GDP growth.\n \n\n\nHeatmap\nFinally, our heatmap wraps it all up with a quick, intuitive look at correlations between GDP and GII across countries. In countries like Estonia (EE) and Ireland (IE), Australia (AT), South Africa (ZA), Thailand (TH), Slovenia (SI), we see positive correlations in warm colors, indicating that as GDP growth rates increase, GII growth rates tend to rise as well. For countries like Cyprus (CY), Romania (RO), and the United States (US), we see cooler colors, representing negative correlations: as GDP growth rate rises, GII growth rates decrease, suggesting an improvement in gender equality alongside economic growth. This color-coded comparison leaves us curious—could the distinction between developed and developing countries play a role in these relationships? It’s a question that might inspire future research directions!" + }, + { + "objectID": "posts/blog/Blog.html#statistical-analysis", + "href": "posts/blog/Blog.html#statistical-analysis", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "Statistical Analysis", + "text": "Statistical Analysis\n\nDescriptive Statistics\nLet’s start with a quick overview of our descriptive statistics: our dataset includes 915 data points. On average, GII change is -3.15%, indicating an overall reduction in gender inequality over time. Meanwhile, GDP change averages 2.35%, hinting at modest economic growth. A full breakdown of these values can be found in the table below for more detail.\n\n\n\nRegression Analysis\nTo get a sense of any relationship between GDP change and GII change, we ran a simple linear regression. Here’s what we found:\n\nP-value: With a p-value of 0.057, we fail to reject the null hypothesis. There is no statistically significant relationship. In other words, we don’t find strong evidence of a link between GDP and GII changes.\nR-squared: GDP change explains only 0.4% of the variation in GII change—a pretty minimal effect.\n95% Confidence Interval: We’re 95% confident the interval from -0.003 to 0.169 captures the true slope. The effect of GDP change on GII is uncertain as 0 is within this range.\n\n\n\nTime Series Analysis\nTime series analysis allows us to dig deeper into how GII and GDP changes play out over time—and potentially influence one another. Here’s the rundown:\n\nStationarity check: Both GII and GDP change series are stationary, meaning their means and variances are stable over time, setting us up for reliable forecasting.\nVAR Model (Vector Autoregression): This multivariate model lets us explore how past values in both series relate to current values. Notably, past GII changes significantly impact current GII changes.\nModel Performance: The VAR model has good fit, as shown by the forecast closely tracking actual values. Low MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) confirm that our model predicts GII and GDP change with minimal error. Check out the plots and table below!\nGranger Causality Test: Curious whether GDP changes can predict changes in GII? The Granger causality test says no. With p-values above 0.05, we fail to find evidence that GDP changes drive GII changes. This suggests that while both variables evolve over time, they don’t seem to directly influence each other." + }, + { + "objectID": "posts/blog/Blog.html#conclusion", + "href": "posts/blog/Blog.html#conclusion", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "Conclusion", + "text": "Conclusion\nIn wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research.\nIn terms of the Imfp library, we gained valuable experience working with real-world economic indicators like GDP and the GII. The library’s function in fetching databases, parameters, and datasets made it straightforward to access IMF’s data, allowing us to focus on analysis instead. The integration of other Python tools—such as seaborn, matplotlib, and numpy—not only streamlined our computational processes but also ensured consistency across the project." + }, + { + "objectID": "posts/blog/Blog.html#future-directions", + "href": "posts/blog/Blog.html#future-directions", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "Future Directions", + "text": "Future Directions\nFirst, we must consider what other factors might influence the relationship between GDP and GII change. The GII is a composite index, shaped by a myriad of social factors, including cultural norms, legal frameworks, and environmental shifts. Future studies could benefit from incorporating additional predictors into the analysis and exploring the interaction between economic growth and gender equality within specific country contexts.\nSecond, there’s potential to enhance the predictive power of our Vector Autoregression (VAR) time series model. While we established that GDP change does not cause GII change, our model performed well in forecasting trends for both variables independently. In practice, policymakers may want to forecast GII trends independently of GDP if they are implementing gender-focused policies. Future research could investigate time series modeling to further unravel the dynamics of GII and GDP changes.\nSo, as we wrap up this chapter, let’s keep our curiosity alive and our questions flowing. After all, every end is just a new beginning in the quest for knowledge!" + }, + { + "objectID": "imfp_demo.html", + "href": "imfp_demo.html", + "title": "imfp Demo Project", + "section": "", + "text": "This data analysis project aims to explore the relationship between economic growth and gender equality using imfp, which allows us to download data from IMF (International Monetary Fund). imfp can be integrated with other python tools to streamline the computational process. To demonstrate its functionality, the project experimented with a variety of visualization and analysis methods." + }, + { + "objectID": "imfp_demo.html#executive-summary", + "href": "imfp_demo.html#executive-summary", + "title": "imfp Demo Project", + "section": "Executive Summary", + "text": "Executive Summary\nIn this project, we explored the following:\n\nData Fetching\n\n\nMake API call to fetch 4 datasets: GII (Gender Inequality Index), Nominal GDP, GDP Deflator Index, Population series\n\n\nFeature Engineering\n\n\nCleaning: Convert GDP Deflator Index to a yearly basis and variables to numeric\nDependent Variable: Percent Change of Gender Inequality Index\nIndependent Variable: Percent Change of Real GDP per Capita\nTransform variables to display magnitude of change\nMerge the datasets\n\n\nData Visualization\n\n\nScatterplot\nTime Series Line Plots\nBarplot\nBoxplot\nHeatmap\n\n\nStatistical Analysis\n\n\nDescriptive Statistics\nRegression Analysis\nTime Series Analysis" + }, + { + "objectID": "imfp_demo.html#suggested-packages", + "href": "imfp_demo.html#suggested-packages", + "title": "imfp Demo Project", + "section": "Suggested packages", + "text": "Suggested packages\nThe integration of other Python tools not only streamlined our computational processes but also ensured consistency across the project.\nA custom module is written to simplify the process of making API calls and fetching information with imfp library. load_or_fetch_databases, load_or_fetch_parameters load_or_fetch_dataset load and retreive database, parameters, and dataset from a local or remote source. view_dataframe_in_browser displays dataframe in a web browser.\n\nimport os\nimport pickle\nfrom tempfile import NamedTemporaryFile\nimport pandas as pd\nimport imfp\nimport webbrowser\n\n\n# Function to display a DataFrame in a web browser\ndef view_dataframe_in_browser(df):\n html = df.to_html()\n with NamedTemporaryFile(delete=False, mode=\"w\", suffix=\".html\") as f:\n url = \"file://\" + f.name\n f.write(html)\n webbrowser.open(url)\n\n\n# Function to load databases from CSV or fetch from API\ndef load_or_fetch_databases():\n csv_path = os.path.join(\"data\", \"databases.csv\")\n\n # Try to load from CSV\n if os.path.exists(csv_path):\n try:\n return pd.read_csv(csv_path)\n except Exception as e:\n print(f\"Error loading CSV: {e}\")\n\n # If CSV doesn't exist or couldn't be loaded, fetch from API\n print(\"Fetching databases from IMF API...\")\n databases = imfp.imf_databases()\n\n # Save to CSV for future use\n databases.to_csv(csv_path, index=False)\n print(f\"Databases saved to {csv_path}\")\n\n return databases\n\n\ndef load_or_fetch_parameters(database_name):\n pickle_path = os.path.join(\"data\", f\"{database_name}.pickle\")\n\n # Try to load from pickle file\n if os.path.exists(pickle_path):\n try:\n with open(pickle_path, \"rb\") as f:\n return pickle.load(f)\n except Exception as e:\n print(f\"Error loading pickle file: {e}\")\n\n # If pickle doesn't exist or couldn't be loaded, fetch from API\n print(f\"Fetching parameters for {database_name} from IMF API...\")\n parameters = imfp.imf_parameters(database_name)\n\n # Save to pickle file for future use\n os.makedirs(\"data\", exist_ok=True) # Ensure the data directory exists\n with open(pickle_path, \"wb\") as f:\n pickle.dump(parameters, f)\n print(f\"Parameters saved to {pickle_path}\")\n\n return parameters\n\n\ndef load_or_fetch_dataset(database_id, indicator):\n file_name = f\"{database_id}.{indicator}.csv\"\n csv_path = os.path.join(\"data\", file_name)\n\n # Try to load from CSV file\n if os.path.exists(csv_path):\n try:\n return pd.read_csv(csv_path)\n except Exception as e:\n print(f\"Error loading CSV file: {e}\")\n\n # If CSV doesn't exist or couldn't be loaded, fetch from API\n print(f\"Fetching dataset for {database_id}.{indicator} from IMF API...\")\n dataset = imfp.imf_dataset(database_id=database_id, indicator=[indicator])\n\n # Save to CSV file for future use\n os.makedirs(\"data\", exist_ok=True) # Ensure the data directory exists\n dataset.to_csv(csv_path, index=False)\n print(f\"Dataset saved to {csv_path}\")\n\n return dataset\n\nHere is a brief introduction about the packages used:\npandas: view and manipulate data frame\nmatplotlib.pyplot: make plots\nseaborn: make plots\nnumpy: computation\nLinearRegression: implement linear regression\ntabulate: format data into tables\nstatsmodels.api, adfuller, ARIMA,VAR,plot_acf,plot_pacf,mean_absolute_error,mean_squared_error, andgrangercausalitytests are specifically used for time series analysis.\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\nfrom tabulate import tabulate\nimport statsmodels.api as sm\nfrom statsmodels.tsa.stattools import adfuller\nfrom statsmodels.tsa.arima.model import ARIMA\nfrom statsmodels.tsa.vector_ar.var_model import VAR\nfrom statsmodels.graphics.tsaplots import plot_acf, plot_pacf\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.metrics import mean_squared_error\nfrom statsmodels.tsa.stattools import grangercausalitytests" + }, + { + "objectID": "imfp_demo.html#data-fetching", + "href": "imfp_demo.html#data-fetching", + "title": "imfp Demo Project", + "section": "Data Fetching", + "text": "Data Fetching\nIn this section, we extracted four datasets through API calls: Gender Inequality Index(GII), GDP Deflator, Nominal GDP, and Population.\n\nfrom pathlib import Path\nPath(\"data\").mkdir(exist_ok=True)\n\n\n# Load or fetch databases\ndatabases = load_or_fetch_databases()\n\n# Filter out databases that contain a year in the description\ndatabases[\n ~databases['description'].str.contains(r\"[\\d]{4}\", regex=True)\n]\n\n# view_dataframe_in_browser(databases)\n\n\n\n\n\n\n\n\ndatabase_id\ndescription\n\n\n\n\n25\nHPDD\nHistorical Public Debt (HPDD)\n\n\n34\nRAFIT2AGG\nRevenue Administration Fiscal Information Tool...\n\n\n47\nGENDER_EQUALITY\nGender Equality\n\n\n63\nPGCS\nPrivate and Public Capital Stock Dataset\n\n\n69\nGENDER_BUDGETING\nGender Budgeting\n\n\n125\nCPI\nConsumer Price Index (CPI)\n\n\n153\nIRFCL\nInternational Reserves and Foreign Currency Li...\n\n\n191\nIFS_DISCONTINUED\nInternational Financial Statistics (IFS), Disc...\n\n\n192\nEQ\nExport Quality\n\n\n193\nED\nExport Diversification\n\n\n226\nFISCALDECENTRALIZATION\nFiscal Decentralization\n\n\n291\nFDI\nFinancial Development Index\n\n\n292\nPSBSFAD\nPublic Sector Balance Sheet (PSBS)(FAD)\n\n\n293\nUNSDG_IMF_INPUTS\nSustainable Development Goals, IMF Inputs\n\n\n294\nCPIS\nCoordinated Portfolio Investment Survey (CPIS)\n\n\n295\nPCTOT\nCommodity Terms of Trade\n\n\n296\nFM\nFiscal Monitor (FM)\n\n\n297\nAFRREO\nSub-Saharan Africa Regional Economic Outlook (...\n\n\n298\nWHDREO\nWestern Hemisphere Regional Economic Outlook (...\n\n\n299\nMCDREO\nMiddle East and Central Asia Regional Economic...\n\n\n300\nAPDREO\nAsia and Pacific Regional Economic Outlook (AP...\n\n\n301\nBOPAGG\nBalance of Payments (BOP), World and Regional ...\n\n\n302\nPCPS\nPrimary Commodity Price System (PCPS)\n\n\n303\nCDIS\nCoordinated Direct Investment Survey (CDIS)\n\n\n304\nBOP\nBalance of Payments (BOP)\n\n\n305\nCOFER\nCurrency Composition of Official Foreign Excha...\n\n\n306\nDOT\nDirection of Trade Statistics (DOTS)\n\n\n307\nRAFIT3P\nRA-FIT Round3 Completion and Participation Rates\n\n\n308\nFAS\nFinancial Access Survey (FAS)\n\n\n309\nFSI\nFinancial Soundness Indicators (FSIs)\n\n\n310\nFSIRE\nFinancial Soundness Indicators: Reporting enti...\n\n\n311\nBOPSDMXUSD\nBalance of Payments (BOP), Global SDMX (US Dol...\n\n\n312\nNAMAIN_IDC_N\nSystem of National Accounts (SNA), NA_MAIN\n\n\n313\nGFSR\nGovernment Finance Statistics (GFS), Revenue\n\n\n314\nGFSSSUC\nGovernment Finance Statistics (GFS), Statement...\n\n\n315\nGFSCOFOG\nGovernment Finance Statistics (GFS), Expenditu...\n\n\n316\nGFSFALCS\nGovernment Finance Statistics (GFS), Financial...\n\n\n317\nGFSIBS\nGovernment Finance Statistics (GFS), Integrate...\n\n\n318\nGFSMAB\nGovernment Finance Statistics (GFS), Main Aggr...\n\n\n319\nGFSE\nGovernment Finance Statistics (GFS), Expense\n\n\n320\nIFS\nInternational Financial Statistics (IFS)\n\n\n321\nMFS\nMonetary and Financial Statistics (MFS)\n\n\n\n\n\n\n\nTwo databases were used: Gender Equality and International Financial Statistics (IFS).\n\ndatasets = [\"GENDER_EQUALITY\", \"IFS\"]\nparams = {}\n\n# Fetch valid parameters for two datasets\nfor dataset in datasets:\n params[dataset] = load_or_fetch_parameters(dataset)\n\n valid_keys = list(params[dataset].keys())\n print(f\"Parameters for {dataset}: \", valid_keys)\n\n# view_dataframe_in_browser(params.get(\"IFS\").get(\"indicator\"))\n\nParameters for GENDER_EQUALITY: ['freq', 'ref_area', 'indicator']\nParameters for IFS: ['freq', 'ref_area', 'indicator']\n\n\n“freq” stands for Frequency, such as Annual, Monthly, or Quarterly. “ref_area” stands for Geogrpahical Area, such as US (United States), JP (Japan), and GB (United Kindom). “indicator” refers to the specific dataset in the database, including input code and description. For example, if we display all the indicators for IFS database, the GDP deflator dataset has an input code of “NGDP_D_SA_IX” with a full name description of Gross Domestic Product, Deflator, Seasonally Adjusted, Index.\nWe paired the database with the specific dataset indicator to read and store the csv file.\n\ndatasets = {}\ndsets = [(\"GENDER_EQUALITY\", \"GE_GII\"), (\"IFS\", \"NGDP_D_SA_IX\"), (\"IFS\", \"NGDP_XDC\"), (\"IFS\", \"LP_PE_NUM\")]\n\nfor dset in dsets:\n datasets[dset[0] + \".\" + dset[1]] = load_or_fetch_dataset(dset[0], dset[1])\n\n\n# \"Gender Inequality Index\"\nGII = \"GENDER_EQUALITY.GE_GII\"\n\n# \"Gross Domestic Product, Deflator, Seasonally Adjusted, Index\"\nGDP_deflator = \"IFS.NGDP_D_SA_IX\"\n\n# \"Gross Domestic Product, Nominal, Domestic Currency\"\nGDP_nominal = \"IFS.NGDP_XDC\"\n\n# \"Population, Persons, Number of\"\nGDP_population = \"IFS.LP_PE_NUM\"\n\n# Assign the datasets to new variables so we don't change the originals\nGII_data = datasets[GII]\nGDP_deflator_data = datasets[GDP_deflator]\nGDP_nominal_data = datasets[GDP_nominal]\nGDP_population_data = datasets[GDP_population]" + }, + { + "objectID": "imfp_demo.html#feature-engineering", + "href": "imfp_demo.html#feature-engineering", + "title": "imfp Demo Project", + "section": "Feature Engineering", + "text": "Feature Engineering\n\nData Cleaning\nSince the GDP deflator was reported on a quarterly basis, we converted it to a yearly basis.\n\n# Filter to keep only rows with a partial string match for \"Q4\" in the time_period column\nGDP_deflator_data = GDP_deflator_data[GDP_deflator_data['time_period'].str.contains(\"Q4\")]\n\n\n# Split the time_period into year and quarter and keep the year only\nGDP_deflator_data.loc[:, 'time_period'] = GDP_deflator_data['time_period'].str[0:4]\n\nWe made all the variables numeric.\n\ndatasets = [GII_data, GDP_deflator_data, GDP_nominal_data, GDP_population_data]\n\nfor i, dataset in enumerate(datasets): \n # Use .loc to modify the columns\n datasets[i].loc[:, 'obs_value'] = pd.to_numeric(datasets[i]['obs_value'], errors='coerce')\n datasets[i].loc[:, 'time_period'] = pd.to_numeric(datasets[i]['time_period'], errors='coerce')\n datasets[i].loc[:, 'unit_mult'] = pd.to_numeric(datasets[i]['unit_mult'], errors='coerce')\n\n\n\nGII Percent Change: Dependent Variable\nWe kept percents as decimals to make them easy to work with for calculation. Different countries have different baseline level of economic growth and gender equality. We calculated the percent change to make them comparable.\n\n# Calculate percent change for each ref_area\nGII_data[f'pct_change'] = GII_data.sort_values(['ref_area', 'time_period']).groupby('ref_area')['obs_value'].pct_change()\n\n# Display the first few rows of the updated dataset\nGII_data.head()\n\n\n\n\n\n\n\n\nfreq\nref_area\nindicator\nunit_mult\ntime_format\ntime_period\nobs_value\npct_change\n\n\n\n\n0\nA\nAF\nGE_GII\n0\nP1Y\n1990\n0.828244\nNaN\n\n\n1\nA\nAF\nGE_GII\n0\nP1Y\n1991\n0.817706\n-0.012723\n\n\n2\nA\nAF\nGE_GII\n0\nP1Y\n1992\n0.809806\n-0.009662\n\n\n3\nA\nAF\nGE_GII\n0\nP1Y\n1993\n0.803078\n-0.008308\n\n\n4\nA\nAF\nGE_GII\n0\nP1Y\n1994\n0.797028\n-0.007533\n\n\n\n\n\n\n\nWe subset the data frame to keep only the columns we want:\n\n# Create a new dataframe with only the required columns\nGII_data = GII_data[['ref_area', 'time_period', 'pct_change']].copy()\n\n# Display the first few rows of the new dataset\nGII_data.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\npct_change\n\n\n\n\n0\nAF\n1990\nNaN\n\n\n1\nAF\n1991\n-0.012723\n\n\n2\nAF\n1992\n-0.009662\n\n\n3\nAF\n1993\n-0.008308\n\n\n4\nAF\n1994\n-0.007533\n\n\n\n\n\n\n\n\n\nGDP: Independent Variable\nWe kept the columns we want only for GDP-related datasets for easier table merging.\n\n# GDP Deflator Dataset\n# Create a new dataframe with only the required columns\nGDP_deflator_data = GDP_deflator_data[['ref_area', 'time_period', 'unit_mult', 'obs_value']].copy()\n\n# Divide obs_value by 100\nGDP_deflator_data['obs_value'] = GDP_deflator_data['obs_value'] / 100\n\n# Display the first few rows of the new dataset\nGDP_deflator_data.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nunit_mult\nobs_value\n\n\n\n\n3\nFI\n1990\n0\n0.732006\n\n\n7\nFI\n1991\n0\n0.739841\n\n\n11\nFI\n1992\n0\n0.746543\n\n\n15\nFI\n1993\n0\n0.756193\n\n\n19\nFI\n1994\n0\n0.779373\n\n\n\n\n\n\n\n\n# GDP Nominal Data\n# Create a new dataframe with only the required columns\nGDP_nominal_data = GDP_nominal_data[['ref_area', 'time_period', 'unit_mult','obs_value']].copy()\n\n# Display the first few rows of the new dataset\nGDP_nominal_data.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nunit_mult\nobs_value\n\n\n\n\n0\nNE\n2005\n6\n2418864.0\n\n\n1\nNE\n2006\n6\n2596972.0\n\n\n2\nNE\n2007\n6\n2762961.0\n\n\n3\nNE\n2008\n6\n3247835.0\n\n\n4\nNE\n2009\n6\n3403683.0\n\n\n\n\n\n\n\n\n# GDP Population Data \n# Create a new dataframe with only the required columns\nGDP_population_data = GDP_population_data[['ref_area', 'time_period', 'unit_mult','obs_value']].copy()\n\n# Display the first few rows of the new dataset\nGDP_population_data.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nunit_mult\nobs_value\n\n\n\n\n0\nGA\n1950\n3\n473.296\n\n\n1\nGA\n1951\n3\n476.381\n\n\n2\nGA\n1952\n3\n478.655\n\n\n3\nGA\n1953\n3\n480.536\n\n\n4\nGA\n1954\n3\n482.332\n\n\n\n\n\n\n\n\n# Combine all the datasets above for further calculation\nmerged_df = pd.merge(pd.merge(GDP_deflator_data,GDP_nominal_data, on=['time_period', 'ref_area'], suffixes=('_index', '_nominal'), how='inner'), GDP_population_data, on=['time_period', 'ref_area'], how='inner')\n\n# Display the GDP data in a web browser\n# view_dataframe_in_browser(merged_df)\n\n# Display the first few rows of the dataset\nmerged_df.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nunit_mult_index\nobs_value_index\nunit_mult_nominal\nobs_value_nominal\nunit_mult\nobs_value\n\n\n\n\n0\nFI\n1990\n0\n0.732006\n6\n90964.0\n3\n4996.220\n\n\n1\nFI\n1991\n0\n0.739841\n6\n86913.0\n3\n5019.134\n\n\n2\nFI\n1992\n0\n0.746543\n6\n84786.0\n3\n5044.928\n\n\n3\nFI\n1993\n0\n0.756193\n6\n85610.0\n3\n5071.782\n\n\n4\nFI\n1994\n0\n0.779373\n6\n90646.0\n3\n5097.090\n\n\n\n\n\n\n\nWe want to adjust GDP data based on unit multiplier. Unit multiplier stands for the number of zeroes we need to add to the value column. For example, in 1950, the observed population data for country GA (Georgia) was 473.296. With a unit muliplier of 3, the adjusted population would be 473296.\n\nmerged_df['adjusted_index'] = merged_df['obs_value_index'] * (10 ** (merged_df['unit_mult_index']))\nmerged_df['adjusted_nominal'] = merged_df['obs_value_nominal'] * (10 ** (merged_df['unit_mult_nominal']))\nmerged_df['adjusted_population'] = merged_df['obs_value'] * (10 ** (merged_df['unit_mult']))\n\n\n# Merged dataset\n# Create a new dataframe with only the required columns\nmerged_df = merged_df[['ref_area', 'time_period','adjusted_nominal', 'adjusted_index', 'adjusted_population']].copy()\n\n# Display the first few rows of the dataset\nmerged_df.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nadjusted_nominal\nadjusted_index\nadjusted_population\n\n\n\n\n0\nFI\n1990\n9.096400e+10\n0.732006\n4996220.0\n\n\n1\nFI\n1991\n8.691300e+10\n0.739841\n5019134.0\n\n\n2\nFI\n1992\n8.478600e+10\n0.746543\n5044928.0\n\n\n3\nFI\n1993\n8.561000e+10\n0.756193\n5071782.0\n\n\n4\nFI\n1994\n9.064600e+10\n0.779373\n5097090.0\n\n\n\n\n\n\n\nWe wanted to compute the Real GDP per capita.\n\n# Step 1: Real GDP = (Nominal GDP / GDP Deflator Index)\nmerged_df['Real_GDP_domestic'] = (merged_df['adjusted_nominal'] / merged_df['adjusted_index'])\n\n# Step 2: Real GDP per Capita = Real GDP / Population\nmerged_df['Real_GDP_per_capita'] = merged_df['Real_GDP_domestic'] / merged_df['adjusted_population']\n\n# Check the results\nmerged_df.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nadjusted_nominal\nadjusted_index\nadjusted_population\nReal_GDP_domestic\nReal_GDP_per_capita\n\n\n\n\n0\nFI\n1990\n9.096400e+10\n0.732006\n4996220.0\n1.242667e+11\n24872.143699\n\n\n1\nFI\n1991\n8.691300e+10\n0.739841\n5019134.0\n1.174753e+11\n23405.490395\n\n\n2\nFI\n1992\n8.478600e+10\n0.746543\n5044928.0\n1.135715e+11\n22512.011198\n\n\n3\nFI\n1993\n8.561000e+10\n0.756193\n5071782.0\n1.132119e+11\n22321.919259\n\n\n4\nFI\n1994\n9.064600e+10\n0.779373\n5097090.0\n1.163063e+11\n22818.181344\n\n\n\n\n\n\n\nWe calculated the percentage change in Real GDP per capita and put it in a new column.\n\n# Calculate percent change for each ref_area\nmerged_df[f'GDP_change'] = merged_df.sort_values(['ref_area', 'time_period']).groupby('ref_area')['Real_GDP_per_capita'].pct_change()\n\n# Rename dataset\nGDP_data = merged_df\n\n# Display the first few rows of the dataset\nGDP_data.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nadjusted_nominal\nadjusted_index\nadjusted_population\nReal_GDP_domestic\nReal_GDP_per_capita\nGDP_change\n\n\n\n\n0\nFI\n1990\n9.096400e+10\n0.732006\n4996220.0\n1.242667e+11\n24872.143699\nNaN\n\n\n1\nFI\n1991\n8.691300e+10\n0.739841\n5019134.0\n1.174753e+11\n23405.490395\n-0.058968\n\n\n2\nFI\n1992\n8.478600e+10\n0.746543\n5044928.0\n1.135715e+11\n22512.011198\n-0.038174\n\n\n3\nFI\n1993\n8.561000e+10\n0.756193\n5071782.0\n1.132119e+11\n22321.919259\n-0.008444\n\n\n4\nFI\n1994\n9.064600e+10\n0.779373\n5097090.0\n1.163063e+11\n22818.181344\n0.022232\n\n\n\n\n\n\n\n\n# GII and GDP\n# Merge the datasets\ncombined_data = pd.merge(GII_data, GDP_data, on=[\"ref_area\", \"time_period\"], how = \"inner\")\n\n# Check the combined dataset\ncombined_data.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\npct_change\nadjusted_nominal\nadjusted_index\nadjusted_population\nReal_GDP_domestic\nReal_GDP_per_capita\nGDP_change\n\n\n\n\n0\nAR\n2004\n-0.021896\n4.851153e+11\n0.409672\n38491970.0\n1.184155e+12\n30763.687776\nNaN\n\n\n1\nAR\n2005\n-0.008581\n5.825382e+11\n0.470108\n38892924.0\n1.239159e+12\n31860.772776\n0.035662\n\n\n2\nAR\n2006\n0.007379\n7.159043e+11\n0.526535\n39289876.0\n1.359651e+12\n34605.628084\n0.086152\n\n\n3\nAR\n2007\n-0.020117\n8.969801e+11\n0.629139\n39684303.0\n1.425727e+12\n35926.734575\n0.038176\n\n\n4\nAR\n2008\n0.000733\n1.149646e+12\n0.757753\n40080159.0\n1.517178e+12\n37853.600252\n0.053633\n\n\n\n\n\n\n\n\n# GII and GDP\n# Rename columns\ncombined_data = combined_data.rename(columns = {\n \"ref_area\": \"Country\",\n \"time_period\": \"Time\",\n \"pct_change\": \"GII_change\"\n})\n\n# Display the GDP data in a web browser\n# view_dataframe_in_browser(combined_data)\n\n# Check the combined dataset\ncombined_data.head()\n\n\n\n\n\n\n\n\nCountry\nTime\nGII_change\nadjusted_nominal\nadjusted_index\nadjusted_population\nReal_GDP_domestic\nReal_GDP_per_capita\nGDP_change\n\n\n\n\n0\nAR\n2004\n-0.021896\n4.851153e+11\n0.409672\n38491970.0\n1.184155e+12\n30763.687776\nNaN\n\n\n1\nAR\n2005\n-0.008581\n5.825382e+11\n0.470108\n38892924.0\n1.239159e+12\n31860.772776\n0.035662\n\n\n2\nAR\n2006\n0.007379\n7.159043e+11\n0.526535\n39289876.0\n1.359651e+12\n34605.628084\n0.086152\n\n\n3\nAR\n2007\n-0.020117\n8.969801e+11\n0.629139\n39684303.0\n1.425727e+12\n35926.734575\n0.038176\n\n\n4\nAR\n2008\n0.000733\n1.149646e+12\n0.757753\n40080159.0\n1.517178e+12\n37853.600252\n0.053633" + }, + { + "objectID": "imfp_demo.html#data-visualization", + "href": "imfp_demo.html#data-visualization", + "title": "imfp Demo Project", + "section": "Data Visualization", + "text": "Data Visualization\n\nScatterplot\nScatterplot use dots to represent values of two numeric variables. The horizontal axis was the percent change in Real GDP per capita. The vertical axis was the percent change in Gender Inequality Index(GII). Different colors represented different countries. We used a linear regression line to display the overall pattern.\nBased on the scatterplot, it seemed like there was a slight positive relationship between GDP change and GII change as shown by the flat regression line. Gender inequality was decreasing (gender equality was improving) a little faster in country-years with low GDP growth and a little slower in country-years with high GDP growth.\n\n# Drop NAs\ncombined_data = combined_data.dropna(subset=['GDP_change', 'GII_change'])\n\n# Plot the data points\nplt.figure(figsize=(18, 10))\nfor country in combined_data['Country'].unique():\n country_data = combined_data[combined_data['Country'] == country]\n plt.scatter(country_data['GDP_change'], country_data['GII_change'],\n marker='o',linestyle='-', label=country)\nplt.title('Country-Year Analysis of GDP Change vs. GII Change')\nplt.xlabel('Percent Change in Real GDP per Capita (Country-Year)')\nplt.ylabel('Percent Change in GII (Country-Year)')\nplt.grid(True)\n\n# Prepare data for linear regression\nX = combined_data['GDP_change'].values.reshape(-1, 1)\ny = combined_data['GII_change'].values\n\n# Perform linear regression\nreg = LinearRegression().fit(X, y)\ny_pred = reg.predict(X)\n\n# Plot the regression line\nplt.plot(combined_data['GDP_change'], y_pred, color='red', linewidth=2)\n\nplt.show()\n\n\n\n\n\n\n\n\n\n\nTime Series Line Plot\nWe created separate line plots for GDP change and GII change over time for a few key countries might show the trends more clearly.\nUS: United States\nJP: Japan\nGB: United Kindom\nFR: France\nMX: Mexico\nBased on the line plots, we saw GDP change and GII change have different patterns. For example, in Mexico, when there was a big change in real GDP per captia in 1995, the change in GII was pretty stable.\n\n# Time Series Line plot for a few key countries\nselected_countries = ['US', 'JP', 'GB', 'FR', 'MX']\ncombined_data_selected = combined_data[combined_data['Country'].isin(selected_countries)]\n\n# Set up the Plot Structure\nfig, ax = plt.subplots(2, 1, figsize=(18, 10), sharex=True)\n\n# Plot change in real GDP per capita over time\nsns.lineplot(data = combined_data_selected, x = \"Time\", y = \"GDP_change\", hue = \"Country\", ax = ax[0])\nax[0].set_title(\"Percent Change in Real GDP per Capita Over Time\")\nax[0].set_ylabel(\"Percent Change in Real GDP per Capita\")\n\n# Plot change in GII over time\nsns.lineplot(data = combined_data_selected, x = \"Time\", y = \"GII_change\", hue = \"Country\", ax = ax[1])\nax[1].set_title(\"Percent Change in GII over Time\")\nax[1].set_xlabel(\"Time\")\nax[1].set_ylabel(\"GII\")\n\nplt.tight_layout\nplt.show()\n\n\n\n\n\n\n\n\n\n\nBarplot\nWe used a barplot to show average changes in GII and GDP percent change for each country to visualize regions where inequality was improving or worsening.\nThis plot supported our previous observation how GII change seemed to be not be correlated with GDP change. We also saw that, for country SI, Solvenia, there seems to be a large improvement in gender inequality.\n\n# Barplot using average GII and GDP change\n# Calculate average change for each country\ncombined_data_avg = combined_data.groupby('Country')[['GII_change','GDP_change']].mean().reset_index()\n\n# Prepare to plot structure \nplt.figure(figsize = (18,10))\n\n# Create the barplot\ncombined_data_avg.plot(kind = 'bar', x = 'Country')\nplt.ylabel('Average Change')\nplt.xlabel('Country')\nplt.legend(['GII change', 'GDP change'])\nplt.grid(axis = 'y')\n\n# Show the plot\nplt.show()\n\n<Figure size 1728x960 with 0 Axes>\n\n\n\n\n\n\n\n\n\n\n\nBoxplot\nWe used boxplot to visualize the distribution of GDP and GII change by country, providing information about spread, median, and potential outliers. To provide a more informative view, we sequenced countries in an ascending order by the median of percent change in GDP.\nThe boxplot displayed a slight upward trend with no obvious pattern between GDP and GII change. In coutries with higher GDP change median, they also tend to have a larger spread of the GDP change. The median of GII change remained stable regardless of the magnitude of GDP change, implying weak or no association between GDP and GII change. We observed a potential outlier for country SI, Solvenia, which may explained its large improvement in Gender inequality.\n\n# Box plot for GII and GDP change\n# Melt the dataframe to long format for combined boxplot\ncombined_data_melted = combined_data.melt(id_vars=['Country'], value_vars=['GII_change', 'GDP_change'], \n var_name='Change_Type', value_name='Value')\n\ngdp_medians = combined_data.groupby('Country')['GDP_change'].median().sort_values()\n\ncombined_data_melted['Country'] = pd.Categorical(combined_data_melted['Country'], categories=gdp_medians.index, ordered= True)\n\n# Prepare the plot structure\nplt.figure(figsize=(18, 10))\nsns.boxplot(data = combined_data_melted, x = \"Country\", y = 'Value', hue = 'Change_Type')\nplt.title('Distribution of GII and GDP change by Country')\nplt.xlabel('Country')\nplt.ylabel('Change')\nplt.legend(title = 'Change Type')\n\n# Show the plot\nplt.show()\n\n\n\n\n\n\n\n\n\n\nCorrelation Matrix\nWe created a heatmap to show the relationship between GII and GDP change.\nA positive correlation coefficient indicates a positive relationship: the larger the GDP change, the larger the GII change. A negative correlation coefficient indicates a negative relationship: the larger the GDP change, the smaller the GII change. A correlation coefficient closer to 0 indicates there is weak or no relationship.\nBased on the numeric values in the plot, there was a moderately strong positive correlation between GII and GDP change for country Estonia(EE) and Ireland(IE).\n\n# Calculate the correlation\ncountry_correlation = combined_data.groupby('Country')[['GII_change', 'GDP_change']].corr().iloc[0::2, -1].reset_index(name='Correlation')\n\n# Put the correlation value in a matrix format\ncorrelation_matrix = country_correlation.pivot(index='Country', columns='level_1', values='Correlation')\n\n# Check for NaN values in the correlation matrix\ncorrelation_matrix.fillna(0, inplace=True) # Replace NaNs with 0 or another value as appropriate\n\n# Set up the plot structure\nplt.figure(figsize=(18, 12)) # Adjust height to give more space for y-axis labels\n\n# Plot the heatmap\nsns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0, cbar_kws={\"shrink\": .8}, linewidths=.5)\n\n# Enhance axis labels and title\nplt.title('Heatmap for GII and GDP Change', fontsize=20)\nplt.xlabel('Variables', fontsize=16)\nplt.ylabel('Country', fontsize=16)\n\n# Improve readability of y-axis labels\nplt.yticks(fontsize=12) # Adjust the font size for y-axis labels\n\n# Show the plot\nplt.show()" + }, + { + "objectID": "imfp_demo.html#statistical-analysis", + "href": "imfp_demo.html#statistical-analysis", + "title": "imfp Demo Project", + "section": "Statistical Analysis", + "text": "Statistical Analysis\n\nDescriptive Statistics\nThere was a total of 915 data points. The mean of the GII change in -0.0314868, which indicated the overall grand mean percent change in gender inequality index is -3.15%. The mean of the GDP change was 0.0234633, showing the overall grand mean percent change in real GDP per capita was 2.35%.\n\n# Generate summary statistics\ndescriptive_stats = combined_data.describe()\nprint(tabulate(descriptive_stats, headers='keys', tablefmt='github'))\n\n| | GII_change | adjusted_nominal | adjusted_index | adjusted_population | Real_GDP_domestic | Real_GDP_per_capita | GDP_change |\n|-------|--------------|--------------------|------------------|-----------------------|---------------------|-----------------------|--------------|\n| count | 914 | 914 | 914 | 914 | 914 | 914 | 914 |\n| mean | -0.0314506 | 7.85541e+13 | 0.855504 | 4.50061e+07 | 7.60737e+13 | 1.00595e+06 | 0.023532 |\n| std | 0.054783 | 6.01311e+14 | 0.210057 | 1.24493e+08 | 5.52717e+14 | 3.88155e+06 | 0.0418127 |\n| min | -0.755003 | 2.18714e+09 | 0.0360636 | 396324 | 5.21733e+09 | 1798.42 | -0.285847 |\n| 25% | -0.0424105 | 1.07655e+11 | 0.743355 | 5.0737e+06 | 1.48206e+11 | 17176 | 0.00384809 |\n| 50% | -0.020248 | 7.50706e+11 | 0.888883 | 1.0337e+07 | 9.94933e+11 | 35401 | 0.0222486 |\n| 75% | -0.00451301 | 2.5911e+12 | 1.00175 | 4.5159e+07 | 2.9412e+12 | 120391 | 0.0427318 |\n| max | 0.156992 | 9.54613e+15 | 2.0788 | 1.28084e+09 | 7.92007e+15 | 3.14532e+07 | 0.241984 |\n\n\n\n\nRegression Analysis\nSimple linear regression as a foundational approach provide us with a basic understanding of the relationship between GDP change and GII change.\nBased on the summary, we concluded the following:\n\nBecasue p-value = 0.057, if we set alpha, the significance level, to be 0.05, we failed to reject the null hypothesis and conclude there was no significant relationship between percent change in real GDP per capita and gender inequality index.\nR-squared = 0.004. Only 0.4% of the variance in GII change could be explained by GDP change.\nWe were 95% confident that the interval from -0.003 to 0.169 captured the true slope of GDP change. Because 0 was included, we are uncertain about the effect of GDP change on GII chnage.\n\n\n# Define independent and depenent variables\nX = combined_data['GDP_change']\ny = combined_data['GII_change']\n\n# Add a constant to indepdent variable to include an intercept\nX = sm.add_constant(X)\n\n# Fit a simple linear regresion model and print out the summary\nmodel = sm.OLS(y, X).fit()\nprint(model.summary())\n\n OLS Regression Results \n==============================================================================\nDep. Variable: GII_change R-squared: 0.004\nModel: OLS Adj. R-squared: 0.003\nMethod: Least Squares F-statistic: 3.551\nDate: Wed, 25 Dec 2024 Prob (F-statistic): 0.0598\nTime: 18:00:11 Log-Likelihood: 1360.0\nNo. Observations: 914 AIC: -2716.\nDf Residuals: 912 BIC: -2706.\nDf Model: 1 \nCovariance Type: nonrobust \n==============================================================================\n coef std err t P>|t| [0.025 0.975]\n------------------------------------------------------------------------------\nconst -0.0334 0.002 -16.069 0.000 -0.037 -0.029\nGDP_change 0.0816 0.043 1.885 0.060 -0.003 0.167\n==============================================================================\nOmnibus: 841.660 Durbin-Watson: 1.384\nProb(Omnibus): 0.000 Jarque-Bera (JB): 53999.190\nSkew: -3.938 Prob(JB): 0.00\nKurtosis: 39.823 Cond. No. 23.9\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\n\n\n\nTime Series Analysis\nTime series analysis allows us to explore how the relationship between GII and GDP change vary across different time periods, accounting for lagged effects.\nHere was a quick summary of the result: * Both GII and GDP change time series were stationary.\n\nPast GII change values significantly influenced cuurent GII change values.\nVAR model had good model performance on forecasting future values based on historical data.\nChanges in GDP did not cause/precde the changes in GII.\n\n\nADF Test: Stationality Assumption Check\nWe wanted to use Augmented Dickey-Fuller (ADF) test to check whether a time series was stationary, which was the model assumption for many time series models.\nStationarity implied constant mean and variance over time, making it more predictable and stable for forecasting.\nBased on the ADF test output, both GII and GDP change time series were stationary. We proceeded to the time series modeling section.\n\n# Augmented Dickey-Fuller (ADF) test for stationarity check\n# Create melted datasets\ncombined_data_time = combined_data.melt(id_vars=['Time', 'Country'], value_vars=['GII_change','GDP_change'], var_name = 'Change_Type', value_name = 'Value')\nGII = combined_data_time[(combined_data_time['Change_Type'] == 'GII_change')] \n\nGDP = combined_data_time[(combined_data_time['Change_Type'] == 'GDP_change')]\n\n# Stationary Check\ndef adf_test(series):\n result = adfuller(series.dropna())\n print(f'ADF Statistic: {result[0]}')\n print(f'p-value: {result[1]}')\n if result[1] < 0.05:\n print(\"Series is stationary\")\n else:\n print(\"Series is not stationary\")\n\n# Output the result\nadf_test(GII['Value'])\nadf_test(GDP['Value'])\n\nADF Statistic: -16.069430375623295\np-value: 5.527565277907626e-29\nSeries is stationary\nADF Statistic: -8.535683414310181\np-value: 1.0095278443825336e-13\nSeries is stationary\n\n\n\n\nVAR model: Examine variables separately\nWe fitted a VAR (Vector Autoreression) model to see the relationship between GII and GDP change. VAR is particularly useful when dealing with multivariate time series data and allows us to examine the interdependence between variables.\nBased on summary, here were several interpretations we could make:\n\nWe used AIC as the criteria for model selection. Lower value suggests a better fit.\nGiven that we wanted to predict GII change, we focused on the first set “Results for equation GII_change.”\nPast GII_change values significantly influenced current GII_change, as shown in the small p-values of lags 1 and 2.\nLag 2 of GDP_change had a relatively low p-value but is not statistically significant.\n\n\n# Split the dataset into training and testing sets\nsplit_ratio = 0.7\nsplit_index = int(len(combined_data) * split_ratio)\n\n# Training set is used to fit the model\ntrain_data = combined_data.iloc[:split_index]\n\n# Testing set is used for validation\ntest_data = combined_data.iloc[split_index:]\n\nprint(f\"Training data: {train_data.shape}\")\nprint(f\"Test data: {test_data.shape}\")\n\nTraining data: (639, 9)\nTest data: (275, 9)\n\n\n\n# Fit a VAR model \ntime_model = VAR(train_data[['GII_change', 'GDP_change']])\ntime_model_fitted = time_model.fit(maxlags = 15, ic=\"aic\")\n\n# Print out the model summary\nprint(time_model_fitted.summary())\n\n Summary of Regression Results \n==================================\nModel: VAR\nMethod: OLS\nDate: Wed, 25, Dec, 2024\nTime: 18:00:11\n--------------------------------------------------------------------\nNo. of Equations: 2.00000 BIC: -12.2270\nNobs: 636.000 HQIC: -12.2869\nLog likelihood: 2128.47 FPE: 4.43927e-06\nAIC: -12.3250 Det(Omega_mle): 4.34314e-06\n--------------------------------------------------------------------\nResults for equation GII_change\n================================================================================\n coefficient std. error t-stat prob\n--------------------------------------------------------------------------------\nconst -0.023760 0.003176 -7.481 0.000\nL1.GII_change 0.211342 0.039900 5.297 0.000\nL1.GDP_change -0.015713 0.046080 -0.341 0.733\nL2.GII_change 0.111672 0.040402 2.764 0.006\nL2.GDP_change 0.088293 0.046452 1.901 0.057\nL3.GII_change -0.021861 0.039774 -0.550 0.583\nL3.GDP_change 0.000651 0.046141 0.014 0.989\n================================================================================\n\nResults for equation GDP_change\n================================================================================\n coefficient std. error t-stat prob\n--------------------------------------------------------------------------------\nconst 0.014585 0.002720 5.362 0.000\nL1.GII_change 0.004128 0.034171 0.121 0.904\nL1.GDP_change 0.141566 0.039464 3.587 0.000\nL2.GII_change -0.009734 0.034600 -0.281 0.778\nL2.GDP_change 0.044282 0.039782 1.113 0.266\nL3.GII_change -0.037787 0.034063 -1.109 0.267\nL3.GDP_change 0.143870 0.039516 3.641 0.000\n================================================================================\n\nCorrelation matrix of residuals\n GII_change GDP_change\nGII_change 1.000000 0.040992\nGDP_change 0.040992 1.000000\n\n\n\n\n\n/Users/sammi/Library/Caches/pypoetry/virtualenvs/imfp-0AlIckOY-py3.13/lib/python3.13/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: An unsupported index was provided. As a result, forecasts cannot be generated. To use the model for forecasting, use one of the supported classes of index.\n self._init_dates(dates, freq)\n\n\n\n\nVAR Model: Forecasting\nWe applied the model learned above to the test data. Based on the plot, the forecast values seem to follow the actual data well, indicating a good model fit caputuring the underlying trends.\n\n# Number of steps to forecast (length of the test set)\nn_steps = len(test_data)\n\n# Get the last values from the training set for forecasting\nforecast_input = train_data[['GII_change', 'GDP_change']].values[-time_model_fitted.k_ar:]\n\n# Forecasting\nforecast = time_model_fitted.forecast(y=forecast_input, steps=n_steps)\n\n# Create a DataFrame for the forecasted values\nforecast_df = pd.DataFrame(forecast, index=test_data.index, columns=['GII_forecast', 'GDP_forecast'])\n\n# Ensure the index of the forecast_df matches the test_data index\nforecast_df.index = test_data.index\n\n\nplt.figure(figsize=(12, 6))\nplt.plot(train_data['GII_change'], label='Training GII', color='blue')\nplt.plot(test_data['GII_change'], label='Actual GII', color='orange')\nplt.plot(forecast_df['GII_forecast'], label='Forecasted GII', color='green')\nplt.title('GII Change Forecast vs Actual')\nplt.legend()\nplt.show()\n\nplt.figure(figsize=(12, 6))\nplt.plot(train_data['GDP_change'], label='Training GDP', color='blue')\nplt.plot(test_data['GDP_change'], label='Actual GDP', color='orange')\nplt.plot(forecast_df['GDP_forecast'], label='Forecasted GDP', color='green')\nplt.title('GDP Change Forecast vs Actual')\nplt.legend()\nplt.show()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nVAR Model: Model Performance\nLow values of both MAE and RMSE indicate good model performance with small average errors in predictions.\n\nmae_gii = mean_absolute_error(test_data['GII_change'], forecast_df['GII_forecast'])\nmae_gdp = mean_absolute_error(test_data['GDP_change'], forecast_df['GDP_forecast'])\n\nprint(f'Mean Absolute Error for GII: {mae_gii}')\nprint(f'Mean Absolute Error for GDP: {mae_gdp}')\n\nMean Absolute Error for GII: 0.03296113056900485\nMean Absolute Error for GDP: 0.027636232780964363\n\n\n\nrmse_gii = np.sqrt(mean_squared_error(test_data['GII_change'], forecast_df['GII_forecast']))\nrmse_gdp = np.sqrt(mean_squared_error(test_data['GDP_change'], forecast_df['GDP_forecast']))\n\nprint(f'RMSE for GII: {rmse_gii}')\nprint(f'RMSE for GDP: {rmse_gdp}')\n\nRMSE for GII: 0.06284754560659174\nRMSE for GDP: 0.038429216092797085\n\n\n\n\nVAR Model: Granger causality test\nGranger causality test evaluates whether one time series can predict another.\nBased on the output, the lowest p-value is when lag = 2. However, because p-value > 0.05, we fail to reject the null hypothesis and conclude the GDP_change does not Granger-cause the GII_change.\n\n# Perform the Granger causality test\nmax_lag = 3\ntest_result = grangercausalitytests(train_data[['GII_change', 'GDP_change']], max_lag, verbose=True)\n\n\nGranger Causality\nnumber of lags (no zero) 1\nssr based F test: F=0.0000 , p=0.9972 , df_denom=635, df_num=1\nssr based chi2 test: chi2=0.0000 , p=0.9972 , df=1\nlikelihood ratio test: chi2=0.0000 , p=0.9972 , df=1\nparameter F test: F=0.0000 , p=0.9972 , df_denom=635, df_num=1\n\nGranger Causality\nnumber of lags (no zero) 2\nssr based F test: F=1.8529 , p=0.1576 , df_denom=632, df_num=2\nssr based chi2 test: chi2=3.7351 , p=0.1545 , df=2\nlikelihood ratio test: chi2=3.7242 , p=0.1553 , df=2\nparameter F test: F=1.8529 , p=0.1576 , df_denom=632, df_num=2\n\nGranger Causality\nnumber of lags (no zero) 3\nssr based F test: F=1.2359 , p=0.2958 , df_denom=629, df_num=3\nssr based chi2 test: chi2=3.7489 , p=0.2899 , df=3\nlikelihood ratio test: chi2=3.7379 , p=0.2912 , df=3\nparameter F test: F=1.2359 , p=0.2958 , df_denom=629, df_num=3\n\n\n/Users/sammi/Library/Caches/pypoetry/virtualenvs/imfp-0AlIckOY-py3.13/lib/python3.13/site-packages/statsmodels/tsa/stattools.py:1556: FutureWarning: verbose is deprecated since functions should not print results\n warnings.warn(" + }, + { + "objectID": "imfp_demo.html#conclusion", + "href": "imfp_demo.html#conclusion", + "title": "imfp Demo Project", + "section": "Conclusion", + "text": "Conclusion\nIn wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research. Check out the blog for full analysis!" + }, + { + "objectID": "imfp_demo.html#future-directions", + "href": "imfp_demo.html#future-directions", + "title": "imfp Demo Project", + "section": "Future Directions", + "text": "Future Directions\n\nAdditional Predictors: Gender Inequality Index (GII) is a composite measure shaped by a mariad of social factors. What about culture, policy, and environment?\nSpecific Country: Are there any specific historical contexts that sparked economic growth and improvement in gender equality?\nOpposite causation: Women entering workforce boost GDP?\nPredictive Power of VAR model: Our model performed well in forecasting trends for both variables independently.\n\nLet’s keep our curiosity alive and our questions flowing!" + }, + { + "objectID": "posts/blog/listing.html", + "href": "posts/blog/listing.html", + "title": "blog", + "section": "", + "text": "No matching items" + }, + { + "objectID": "About.html", + "href": "About.html", + "title": "Jenny Xu", + "section": "", + "text": "Hi there! My name is Jenny, and I’m a third-year student at University of California, Davis, double majoring in Statistics and Psychology. I’ve always been interested in becoming a data analyst working in tech, internet, or research industries. Interning at Promptly Technologies helped me learn a ton. A quick fun fact for me is that my MBTI is ISFJ (Defender)!" + }, + { + "objectID": "listing.html", + "href": "listing.html", + "title": "Blog", + "section": "", + "text": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data\n\n\n\n\n\n\nData Analysis\n\n\n\nApply imfp in a real world data-analysis project.\n\n\n\n\n\nDec 25, 2024\n\n\nJenny Xu\n\n\n\n\n\n\nNo matching items" + }, + { + "objectID": "Code/imfp_demo.html", + "href": "Code/imfp_demo.html", + "title": "imfp Demo Project", + "section": "", + "text": "This data analysis project aims to explore the relationship between economic growth and gender equality using imfp, which allows us to download data from IMF (International Monetary Fund). imfp can be integrated with other python tools to streamline the computational process. To demonstrate its functionality, the project experimented with a variety of visualization and analysis methods.\nReady for some insights about if a stronger economy mean more equal opportunities for all genders? Let’s dive in!" + }, + { + "objectID": "Code/imfp_demo.html#executive-summary", + "href": "Code/imfp_demo.html#executive-summary", + "title": "imfp Demo Project", + "section": "Executive Summary", + "text": "Executive Summary\nIn this project, we explored the following:\n\nData Fetching\n\n\nMake API call to fetch 4 datasets: GII (Gender Inequality Index), Nominal GDP, GDP Deflator Index, Population series\n\n\nFeature Engineering\n\n\nCleaning: Convert GDP Deflator Index to a yearly basis and variables to numeric\nDependent Variable: Percent Change of Gender Inequality Index\nIndependent Variable: Percent Change of Real GDP per Capita\nTransform variables to display magnitude of change\nMerge the datasets\n\n\nData Visualization\n\n\nScatterplot\nTime Series Line Plots\nBarplot\nBoxplot\nHeatmap\n\n\nStatistical Analysis\n\n\nDescriptive Statistics\nRegression Analysis\nTime Series Analysis" + }, + { + "objectID": "Code/imfp_demo.html#suggested-packages", + "href": "Code/imfp_demo.html#suggested-packages", + "title": "imfp Demo Project", + "section": "Suggested packages", + "text": "Suggested packages\npandas: view and manipulate data frame; matplotlib.pyplot: make plots; seaborn: make plots; numpy: computation; LinearRegression: implement linear regression; tabulate: format data into tables;\nA custom module is written to simplify the process of making API calls and fetching information with imfp library. load_or_fetch_databases, load_or_fetch_parameters load_or_fetch_dataset load and retreive database, parameters, and dataset from a local or remote source. view_dataframe_in_browser displays dataframe in a web browser.\nstatsmodels.api, adfuller, ARIMA,VAR,plot_acf,plot_pacf,mean_absolute_error,mean_squared_error, andgrangercausalitytests are specifically used for time series analysis.\n\nimport os\nimport pickle\nfrom tempfile import NamedTemporaryFile\nimport pandas as pd\nimport imfp\nimport webbrowser\n\n\n# Function to display a DataFrame in a web browser\ndef view_dataframe_in_browser(df):\n html = df.to_html()\n with NamedTemporaryFile(delete=False, mode=\"w\", suffix=\".html\") as f:\n url = \"file://\" + f.name\n f.write(html)\n webbrowser.open(url)\n\n\n# Function to load databases from CSV or fetch from API\ndef load_or_fetch_databases():\n csv_path = os.path.join(\"data\", \"databases.csv\")\n\n # Try to load from CSV\n if os.path.exists(csv_path):\n try:\n return pd.read_csv(csv_path)\n except Exception as e:\n print(f\"Error loading CSV: {e}\")\n\n # If CSV doesn't exist or couldn't be loaded, fetch from API\n print(\"Fetching databases from IMF API...\")\n databases = imfp.imf_databases()\n\n # Save to CSV for future use\n databases.to_csv(csv_path, index=False)\n print(f\"Databases saved to {csv_path}\")\n\n return databases\n\n\ndef load_or_fetch_parameters(database_name):\n pickle_path = os.path.join(\"data\", f\"{database_name}.pickle\")\n\n # Try to load from pickle file\n if os.path.exists(pickle_path):\n try:\n with open(pickle_path, \"rb\") as f:\n return pickle.load(f)\n except Exception as e:\n print(f\"Error loading pickle file: {e}\")\n\n # If pickle doesn't exist or couldn't be loaded, fetch from API\n print(f\"Fetching parameters for {database_name} from IMF API...\")\n parameters = imfp.imf_parameters(database_name)\n\n # Save to pickle file for future use\n os.makedirs(\"data\", exist_ok=True) # Ensure the data directory exists\n with open(pickle_path, \"wb\") as f:\n pickle.dump(parameters, f)\n print(f\"Parameters saved to {pickle_path}\")\n\n return parameters\n\n\ndef load_or_fetch_dataset(database_id, indicator):\n file_name = f\"{database_id}.{indicator}.csv\"\n csv_path = os.path.join(\"data\", file_name)\n\n # Try to load from CSV file\n if os.path.exists(csv_path):\n try:\n return pd.read_csv(csv_path)\n except Exception as e:\n print(f\"Error loading CSV file: {e}\")\n\n # If CSV doesn't exist or couldn't be loaded, fetch from API\n print(f\"Fetching dataset for {database_id}.{indicator} from IMF API...\")\n dataset = imfp.imf_dataset(database_id=database_id, indicator=[indicator])\n\n # Save to CSV file for future use\n os.makedirs(\"data\", exist_ok=True) # Ensure the data directory exists\n dataset.to_csv(csv_path, index=False)\n print(f\"Dataset saved to {csv_path}\")\n\n return dataset\n\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\nfrom tabulate import tabulate\nimport statsmodels.api as sm\nfrom statsmodels.tsa.stattools import adfuller\nfrom statsmodels.tsa.arima.model import ARIMA\nfrom statsmodels.tsa.vector_ar.var_model import VAR\nfrom statsmodels.graphics.tsaplots import plot_acf, plot_pacf\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.metrics import mean_squared_error\nfrom statsmodels.tsa.stattools import grangercausalitytests" + }, + { + "objectID": "Code/imfp_demo.html#data-fetching", + "href": "Code/imfp_demo.html#data-fetching", + "title": "imfp Demo Project", + "section": "Data Fetching", + "text": "Data Fetching\nIn this section, we extracted four datasets through API calls: Gender Inequality Index(GII), GDP Deflator, Nominal GDP, and Population. Two databases were used: Gender Equality and International Financial Statistics (IFS).\n\nfrom pathlib import Path\nPath(\"data\").mkdir(exist_ok=True)\n\n\n# Load or fetch databases\ndatabases = load_or_fetch_databases()\n\n# Filter out databases that contain a year in the description\ndatabases[\n ~databases['description'].str.contains(r\"[\\d]{4}\", regex=True)\n]\n\n# view_dataframe_in_browser(databases)\n\n\n\n\n\n\n\n\ndatabase_id\ndescription\n\n\n\n\n25\nHPDD\nHistorical Public Debt (HPDD)\n\n\n34\nRAFIT2AGG\nRevenue Administration Fiscal Information Tool...\n\n\n47\nGENDER_EQUALITY\nGender Equality\n\n\n63\nPGCS\nPrivate and Public Capital Stock Dataset\n\n\n69\nGENDER_BUDGETING\nGender Budgeting\n\n\n125\nCPI\nConsumer Price Index (CPI)\n\n\n153\nIRFCL\nInternational Reserves and Foreign Currency Li...\n\n\n191\nIFS_DISCONTINUED\nInternational Financial Statistics (IFS), Disc...\n\n\n192\nEQ\nExport Quality\n\n\n193\nED\nExport Diversification\n\n\n226\nFISCALDECENTRALIZATION\nFiscal Decentralization\n\n\n291\nFDI\nFinancial Development Index\n\n\n292\nPSBSFAD\nPublic Sector Balance Sheet (PSBS)(FAD)\n\n\n293\nUNSDG_IMF_INPUTS\nSustainable Development Goals, IMF Inputs\n\n\n294\nCPIS\nCoordinated Portfolio Investment Survey (CPIS)\n\n\n295\nPCTOT\nCommodity Terms of Trade\n\n\n296\nFM\nFiscal Monitor (FM)\n\n\n297\nAFRREO\nSub-Saharan Africa Regional Economic Outlook (...\n\n\n298\nWHDREO\nWestern Hemisphere Regional Economic Outlook (...\n\n\n299\nMCDREO\nMiddle East and Central Asia Regional Economic...\n\n\n300\nAPDREO\nAsia and Pacific Regional Economic Outlook (AP...\n\n\n301\nBOPAGG\nBalance of Payments (BOP), World and Regional ...\n\n\n302\nPCPS\nPrimary Commodity Price System (PCPS)\n\n\n303\nCDIS\nCoordinated Direct Investment Survey (CDIS)\n\n\n304\nBOP\nBalance of Payments (BOP)\n\n\n305\nCOFER\nCurrency Composition of Official Foreign Excha...\n\n\n306\nDOT\nDirection of Trade Statistics (DOTS)\n\n\n307\nRAFIT3P\nRA-FIT Round3 Completion and Participation Rates\n\n\n308\nFAS\nFinancial Access Survey (FAS)\n\n\n309\nFSI\nFinancial Soundness Indicators (FSIs)\n\n\n310\nFSIRE\nFinancial Soundness Indicators: Reporting enti...\n\n\n311\nBOPSDMXUSD\nBalance of Payments (BOP), Global SDMX (US Dol...\n\n\n312\nNAMAIN_IDC_N\nSystem of National Accounts (SNA), NA_MAIN\n\n\n313\nGFSR\nGovernment Finance Statistics (GFS), Revenue\n\n\n314\nGFSSSUC\nGovernment Finance Statistics (GFS), Statement...\n\n\n315\nGFSCOFOG\nGovernment Finance Statistics (GFS), Expenditu...\n\n\n316\nGFSFALCS\nGovernment Finance Statistics (GFS), Financial...\n\n\n317\nGFSIBS\nGovernment Finance Statistics (GFS), Integrate...\n\n\n318\nGFSMAB\nGovernment Finance Statistics (GFS), Main Aggr...\n\n\n319\nGFSE\nGovernment Finance Statistics (GFS), Expense\n\n\n320\nIFS\nInternational Financial Statistics (IFS)\n\n\n321\nMFS\nMonetary and Financial Statistics (MFS)\n\n\n\n\n\n\n\n\ndatasets = [\"GENDER_EQUALITY\", \"IFS\"]\nparams = {}\n\n# Fetch valid parameters for two datasets\nfor dataset in datasets:\n params[dataset] = load_or_fetch_parameters(dataset)\n\n valid_keys = list(params[dataset].keys())\n print(f\"Parameters for {dataset}: \", valid_keys)\n\n# view_dataframe_in_browser(params.get(\"IFS\").get(\"indicator\"))\n\nParameters for GENDER_EQUALITY: ['freq', 'ref_area', 'indicator']\nParameters for IFS: ['freq', 'ref_area', 'indicator']\n\n\n\ndatasets = {}\ndsets = [(\"GENDER_EQUALITY\", \"GE_GII\"), (\"IFS\", \"NGDP_D_SA_IX\"), (\"IFS\", \"NGDP_XDC\"), (\"IFS\", \"LP_PE_NUM\")]\n\nfor dset in dsets:\n datasets[dset[0] + \".\" + dset[1]] = load_or_fetch_dataset(dset[0], dset[1])\n\n\n# \"Gender Inequality Index\"\nGII = \"GENDER_EQUALITY.GE_GII\"\n\n# \"Gross Domestic Product, Deflator, Seasonally Adjusted, Index\"\nGDP_deflator = \"IFS.NGDP_D_SA_IX\"\n\n# \"Gross Domestic Product, Nominal, Domestic Currency\"\nGDP_nominal = \"IFS.NGDP_XDC\"\n\n# \"Population, Persons, Number of\"\nGDP_population = \"IFS.LP_PE_NUM\"\n\n# Assign the datasets to new variables so we don't change the originals\nGII_data = datasets[GII]\nGDP_deflator_data = datasets[GDP_deflator]\nGDP_nominal_data = datasets[GDP_nominal]\nGDP_population_data = datasets[GDP_population]" + }, + { + "objectID": "Code/imfp_demo.html#feature-engineering", + "href": "Code/imfp_demo.html#feature-engineering", + "title": "imfp Demo Project", + "section": "Feature Engineering", + "text": "Feature Engineering\n\nData Cleaning\nSince the GDP deflator is reported on a quarterly basis, we need to convert it to a yearly basis.\n\n# Filter to keep only rows with a partial string match for \"Q4\" in the time_period column\nGDP_deflator_data = GDP_deflator_data[GDP_deflator_data['time_period'].str.contains(\"Q4\")]\n\n\n# Split the time_period into year and quarter and keep the year only\nGDP_deflator_data.loc[:, 'time_period'] = GDP_deflator_data['time_period'].str[0:4]\n\n\ndatasets = [GII_data, GDP_deflator_data, GDP_nominal_data, GDP_population_data]\n\nfor i, dataset in enumerate(datasets): \n # Use .loc to modify the columns\n datasets[i].loc[:, 'obs_value'] = pd.to_numeric(datasets[i]['obs_value'], errors='coerce')\n datasets[i].loc[:, 'time_period'] = pd.to_numeric(datasets[i]['time_period'], errors='coerce')\n datasets[i].loc[:, 'unit_mult'] = pd.to_numeric(datasets[i]['unit_mult'], errors='coerce')\n\n\n# Calculate percent change for each ref_area\nGII_data[f'pct_change'] = GII_data.sort_values(['ref_area', 'time_period']).groupby('ref_area')['obs_value'].pct_change()\n\n# Display the first few rows of the updated dataset\nGII_data.head()\n\n\n\n\n\n\n\n\nfreq\nref_area\nindicator\nunit_mult\ntime_format\ntime_period\nobs_value\npct_change\n\n\n\n\n0\nA\nAF\nGE_GII\n0\nP1Y\n1990\n0.828244\nNaN\n\n\n1\nA\nAF\nGE_GII\n0\nP1Y\n1991\n0.817706\n-0.012723\n\n\n2\nA\nAF\nGE_GII\n0\nP1Y\n1992\n0.809806\n-0.009662\n\n\n3\nA\nAF\nGE_GII\n0\nP1Y\n1993\n0.803078\n-0.008308\n\n\n4\nA\nAF\nGE_GII\n0\nP1Y\n1994\n0.797028\n-0.007533\n\n\n\n\n\n\n\n\n# Create a new dataframe with only the required columns\nGII_data = GII_data[['ref_area', 'time_period', 'pct_change']].copy()\n\n# Display the first few rows of the new dataset\nGII_data.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\npct_change\n\n\n\n\n0\nAF\n1990\nNaN\n\n\n1\nAF\n1991\n-0.012723\n\n\n2\nAF\n1992\n-0.009662\n\n\n3\nAF\n1993\n-0.008308\n\n\n4\nAF\n1994\n-0.007533\n\n\n\n\n\n\n\n\n# GDP Deflator Dataset\n# Create a new dataframe with only the required columns\nGDP_deflator_data = GDP_deflator_data[['ref_area', 'time_period', 'unit_mult', 'obs_value']].copy()\n\n# Divide obs_value by 100\nGDP_deflator_data['obs_value'] = GDP_deflator_data['obs_value'] / 100\n\n# Display the first few rows of the new dataset\nGDP_deflator_data.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nunit_mult\nobs_value\n\n\n\n\n3\nFI\n1990\n0\n0.732006\n\n\n7\nFI\n1991\n0\n0.739841\n\n\n11\nFI\n1992\n0\n0.746543\n\n\n15\nFI\n1993\n0\n0.756193\n\n\n19\nFI\n1994\n0\n0.779373\n\n\n\n\n\n\n\n\n# GDP Nominal Data\n# Create a new dataframe with only the required columns\nGDP_nominal_data = GDP_nominal_data[['ref_area', 'time_period', 'unit_mult','obs_value']].copy()\n\n# Display the first few rows of the new dataset\nGDP_nominal_data.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nunit_mult\nobs_value\n\n\n\n\n0\nNE\n2005\n6\n2418864.0\n\n\n1\nNE\n2006\n6\n2596972.0\n\n\n2\nNE\n2007\n6\n2762961.0\n\n\n3\nNE\n2008\n6\n3247835.0\n\n\n4\nNE\n2009\n6\n3403683.0\n\n\n\n\n\n\n\n\n# GDP Population Data \n# Create a new dataframe with only the required columns\nGDP_population_data = GDP_population_data[['ref_area', 'time_period', 'unit_mult','obs_value']].copy()\n\n# Display the first few rows of the new dataset\nGDP_population_data.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nunit_mult\nobs_value\n\n\n\n\n0\nGA\n1950\n3\n473.296\n\n\n1\nGA\n1951\n3\n476.381\n\n\n2\nGA\n1952\n3\n478.655\n\n\n3\nGA\n1953\n3\n480.536\n\n\n4\nGA\n1954\n3\n482.332\n\n\n\n\n\n\n\n\n# Combine all the datasets above for further calculation\nmerged_df = pd.merge(pd.merge(GDP_deflator_data,GDP_nominal_data, on=['time_period', 'ref_area'], suffixes=('_index', '_nominal'), how='inner'), GDP_population_data, on=['time_period', 'ref_area'], how='inner')\n\n# Display the GDP data in a web browser\n# view_dataframe_in_browser(merged_df)\n\n# Display the first few rows of the dataset\nmerged_df.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nunit_mult_index\nobs_value_index\nunit_mult_nominal\nobs_value_nominal\nunit_mult\nobs_value\n\n\n\n\n0\nFI\n1990\n0\n0.732006\n6\n90964.0\n3\n4996.220\n\n\n1\nFI\n1991\n0\n0.739841\n6\n86913.0\n3\n5019.134\n\n\n2\nFI\n1992\n0\n0.746543\n6\n84786.0\n3\n5044.928\n\n\n3\nFI\n1993\n0\n0.756193\n6\n85610.0\n3\n5071.782\n\n\n4\nFI\n1994\n0\n0.779373\n6\n90646.0\n3\n5097.090\n\n\n\n\n\n\n\n\nmerged_df['adjusted_index'] = merged_df['obs_value_index'] * (10 ** (merged_df['unit_mult_index']))\nmerged_df['adjusted_nominal'] = merged_df['obs_value_nominal'] * (10 ** (merged_df['unit_mult_nominal']))\nmerged_df['adjusted_population'] = merged_df['obs_value'] * (10 ** (merged_df['unit_mult']))\n\n\n# Merged dataset\n# Create a new dataframe with only the required columns\nmerged_df = merged_df[['ref_area', 'time_period','adjusted_nominal', 'adjusted_index', 'adjusted_population']].copy()\n\n# Display the first few rows of the dataset\nmerged_df.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nadjusted_nominal\nadjusted_index\nadjusted_population\n\n\n\n\n0\nFI\n1990\n9.096400e+10\n0.732006\n4996220.0\n\n\n1\nFI\n1991\n8.691300e+10\n0.739841\n5019134.0\n\n\n2\nFI\n1992\n8.478600e+10\n0.746543\n5044928.0\n\n\n3\nFI\n1993\n8.561000e+10\n0.756193\n5071782.0\n\n\n4\nFI\n1994\n9.064600e+10\n0.779373\n5097090.0\n\n\n\n\n\n\n\n\n# Step 1: Real GDP = (Nominal GDP / GDP Deflator Index)\nmerged_df['Real_GDP_domestic'] = (merged_df['adjusted_nominal'] / merged_df['adjusted_index'])\n\n# Step 2: Real GDP per Capita = Real GDP / Population\nmerged_df['Real_GDP_per_capita'] = merged_df['Real_GDP_domestic'] / merged_df['adjusted_population']\n\n# Check the results\nmerged_df.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nadjusted_nominal\nadjusted_index\nadjusted_population\nReal_GDP_domestic\nReal_GDP_per_capita\n\n\n\n\n0\nFI\n1990\n9.096400e+10\n0.732006\n4996220.0\n1.242667e+11\n24872.143699\n\n\n1\nFI\n1991\n8.691300e+10\n0.739841\n5019134.0\n1.174753e+11\n23405.490395\n\n\n2\nFI\n1992\n8.478600e+10\n0.746543\n5044928.0\n1.135715e+11\n22512.011198\n\n\n3\nFI\n1993\n8.561000e+10\n0.756193\n5071782.0\n1.132119e+11\n22321.919259\n\n\n4\nFI\n1994\n9.064600e+10\n0.779373\n5097090.0\n1.163063e+11\n22818.181344\n\n\n\n\n\n\n\n\n# Calculate percent change for each ref_area\nmerged_df[f'GDP_change'] = merged_df.sort_values(['ref_area', 'time_period']).groupby('ref_area')['Real_GDP_per_capita'].pct_change()\n\n# Rename dataset\nGDP_data = merged_df\n\n# Display the first few rows of the dataset\nGDP_data.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nadjusted_nominal\nadjusted_index\nadjusted_population\nReal_GDP_domestic\nReal_GDP_per_capita\nGDP_change\n\n\n\n\n0\nFI\n1990\n9.096400e+10\n0.732006\n4996220.0\n1.242667e+11\n24872.143699\nNaN\n\n\n1\nFI\n1991\n8.691300e+10\n0.739841\n5019134.0\n1.174753e+11\n23405.490395\n-0.058968\n\n\n2\nFI\n1992\n8.478600e+10\n0.746543\n5044928.0\n1.135715e+11\n22512.011198\n-0.038174\n\n\n3\nFI\n1993\n8.561000e+10\n0.756193\n5071782.0\n1.132119e+11\n22321.919259\n-0.008444\n\n\n4\nFI\n1994\n9.064600e+10\n0.779373\n5097090.0\n1.163063e+11\n22818.181344\n0.022232\n\n\n\n\n\n\n\n\n# GII and GDP\n# Merge the datasets\ncombined_data = pd.merge(GII_data, GDP_data, on=[\"ref_area\", \"time_period\"], how = \"inner\")\n\n# Check the combined dataset\ncombined_data.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\npct_change\nadjusted_nominal\nadjusted_index\nadjusted_population\nReal_GDP_domestic\nReal_GDP_per_capita\nGDP_change\n\n\n\n\n0\nAR\n2004\n-0.021896\n4.851153e+11\n0.409672\n38491970.0\n1.184155e+12\n30763.687776\nNaN\n\n\n1\nAR\n2005\n-0.008581\n5.825382e+11\n0.470108\n38892924.0\n1.239159e+12\n31860.772776\n0.035662\n\n\n2\nAR\n2006\n0.007379\n7.159043e+11\n0.526535\n39289876.0\n1.359651e+12\n34605.628084\n0.086152\n\n\n3\nAR\n2007\n-0.020117\n8.969801e+11\n0.629139\n39684303.0\n1.425727e+12\n35926.734575\n0.038176\n\n\n4\nAR\n2008\n0.000733\n1.149646e+12\n0.757753\n40080159.0\n1.517178e+12\n37853.600252\n0.053633\n\n\n\n\n\n\n\n\n# GII and GDP\n# Rename columns\ncombined_data = combined_data.rename(columns = {\n \"ref_area\": \"Country\",\n \"time_period\": \"Time\",\n \"pct_change\": \"GII_change\"\n})\n\n# Display the GDP data in a web browser\n# view_dataframe_in_browser(combined_data)\n\n# Check the combined dataset\ncombined_data.head()\n\n\n\n\n\n\n\n\nCountry\nTime\nGII_change\nadjusted_nominal\nadjusted_index\nadjusted_population\nReal_GDP_domestic\nReal_GDP_per_capita\nGDP_change\n\n\n\n\n0\nAR\n2004\n-0.021896\n4.851153e+11\n0.409672\n38491970.0\n1.184155e+12\n30763.687776\nNaN\n\n\n1\nAR\n2005\n-0.008581\n5.825382e+11\n0.470108\n38892924.0\n1.239159e+12\n31860.772776\n0.035662\n\n\n2\nAR\n2006\n0.007379\n7.159043e+11\n0.526535\n39289876.0\n1.359651e+12\n34605.628084\n0.086152\n\n\n3\nAR\n2007\n-0.020117\n8.969801e+11\n0.629139\n39684303.0\n1.425727e+12\n35926.734575\n0.038176\n\n\n4\nAR\n2008\n0.000733\n1.149646e+12\n0.757753\n40080159.0\n1.517178e+12\n37853.600252\n0.053633" + }, + { + "objectID": "Code/imfp_demo.html#data-visualization", + "href": "Code/imfp_demo.html#data-visualization", + "title": "imfp Demo Project", + "section": "Data Visualization", + "text": "Data Visualization\n\nScatterplot\nScatterplot use dots to represent values of two numeric variables. The horizontal axis is the percent change in Real GDP per capita. The vertical axis is the percent change in Gender Inequality Index(GII). Different colors represent different countries. We used a linear regression line to display the overall pattern.\nBased on the scatterplot, it seems like there is a slight positive relationship between GDP change and GII change as shown by the flat regression line. Gender inequality is decreasing (gender equality is improving) a little faster in country-years with low GDP growth and a little slower in country-years with high GDP growth.\n\n# Drop NAs\ncombined_data = combined_data.dropna(subset=['GDP_change', 'GII_change'])\n\n# Plot the data points\nplt.figure(figsize=(18, 10))\nfor country in combined_data['Country'].unique():\n country_data = combined_data[combined_data['Country'] == country]\n plt.scatter(country_data['GDP_change'], country_data['GII_change'],\n marker='o',linestyle='-', label=country)\nplt.title('Country-Year Analysis of GDP Change vs. GII Change')\nplt.xlabel('Percent Change in Real GDP per Capita (Country-Year)')\nplt.ylabel('Percent Change in GII (Country-Year)')\nplt.grid(True)\n\n# Prepare data for linear regression\nX = combined_data['GDP_change'].values.reshape(-1, 1)\ny = combined_data['GII_change'].values\n\n# Perform linear regression\nreg = LinearRegression().fit(X, y)\ny_pred = reg.predict(X)\n\n# Plot the regression line\nplt.plot(combined_data['GDP_change'], y_pred, color='red', linewidth=2)\n\nplt.show()\n\n\n\n\n\n\n\n\n\n\nTime Series Line Plot\nCreate separate line plots for GDP change and GII change over time for a few key countries might show the trends more clearly.\nUS: United States JP: Japan GB: United Kindom FR: France MX: Mexico\nBased on the line plots, we see GDP change and GII change have different patterns. For example, in Mexico, when there is a big change in real GDP per captia in 1995, the change in GII is pretty stable.\n\n# Time Series Line plot for a few key countries\nselected_countries = ['US', 'JP', 'GB', 'FR', 'MX']\ncombined_data_selected = combined_data[combined_data['Country'].isin(selected_countries)]\n\n# Set up the Plot Structure\nfig, ax = plt.subplots(2, 1, figsize=(18, 10), sharex=True)\n\n# Plot change in real GDP per capita over time\nsns.lineplot(data = combined_data_selected, x = \"Time\", y = \"GDP_change\", hue = \"Country\", ax = ax[0])\nax[0].set_title(\"Percent Change in Real GDP per Capita Over Time\")\nax[0].set_ylabel(\"Percent Change in Real GDP per Capita\")\n\n# Plot change in GII over time\nsns.lineplot(data = combined_data_selected, x = \"Time\", y = \"GII_change\", hue = \"Country\", ax = ax[1])\nax[1].set_title(\"Percent Change in GII over Time\")\nax[1].set_xlabel(\"Time\")\nax[1].set_ylabel(\"GII\")\n\nplt.tight_layout\nplt.show()\n\n\n\n\n\n\n\n\n\n\nBarplot\nUse a barplot to show average changes in GII and GDP percent change for each country to visualize regions where inequality is improving or worsening.\nThis plot supports our previous observation how GII change seems to be not be correlated with GDP change. We also saw that, for country SI, Solvenia, there seems to be a large improvement in gender inequality.\n\n# Barplot using average GII and GDP change\n# Calculate average change for each country\ncombined_data_avg = combined_data.groupby('Country')[['GII_change','GDP_change']].mean().reset_index()\n\n# Prepare to plot structure \nplt.figure(figsize = (18,10))\n\n# Create the barplot\ncombined_data_avg.plot(kind = 'bar', x = 'Country')\nplt.ylabel('Average Change')\nplt.xlabel('Country')\nplt.legend(['GII change', 'GDP change'])\nplt.grid(axis = 'y')\n\n# Show the plot\nplt.show()\n\n<Figure size 1728x960 with 0 Axes>\n\n\n\n\n\n\n\n\n\n\n\nBoxplot\nUse boxplot to visualize the distribution of GDP and GII change by country, providing information about spread, median, and potential outliers. To provide a more informative view, we sequence countries in an ascending order by the median of percent change in GDP.\nThe boxplot displays a slight upward trend with no obvious pattern between GDP and GII change. In coutries with higher GDP change median, they also tend to have a larger spread of the GDP change. The median of GII change remains stable regardless of the magnitude of GDP change, implying weak or no association between GDP and GII change. We observe a potential outlier for country SI, Solvenia, which may explains its large improvement in Gender inequality.\n\n# Box plot for GII and GDP change\n# Melt the dataframe to long format for combined boxplot\ncombined_data_melted = combined_data.melt(id_vars=['Country'], value_vars=['GII_change', 'GDP_change'], \n var_name='Change_Type', value_name='Value')\n\ngdp_medians = combined_data.groupby('Country')['GDP_change'].median().sort_values()\n\ncombined_data_melted['Country'] = pd.Categorical(combined_data_melted['Country'], categories=gdp_medians.index, ordered= True)\n\n# Prepare the plot structure\nplt.figure(figsize=(18, 10))\nsns.boxplot(data = combined_data_melted, x = \"Country\", y = 'Value', hue = 'Change_Type')\nplt.title('Distribution of GII and GDP change by Country')\nplt.xlabel('Country')\nplt.ylabel('Change')\nplt.legend(title = 'Change Type')\n\n# Show the plot\nplt.show()\n\n\n\n\n\n\n\n\n\n\nCorrelation Matrix\nCreate a heatmap to show the relationship between GII and GDP change.\nA positive correlation coefficient indicates a positive relationship: the larger the GDP change, the larger the GII change. A negative correlation coefficient indicates a negative relationship: the larger the GDP change, the smaller the GII change. A correlation coefficient closer to 0 indicates there is weak or no relationship.\nBased on the numeric values in the plot, there is a moderately strong positive correlation between GII and GDP change for country Estonia(EE) and Ireland(IE).\n\n# Calculate the correlation\ncountry_correlation = combined_data.groupby('Country')[['GII_change', 'GDP_change']].corr().iloc[0::2, -1].reset_index(name='Correlation')\n\n# Put the correlation value in a matrix format\ncorrelation_matrix = country_correlation.pivot(index='Country', columns='level_1', values='Correlation')\n\n# Check for NaN values in the correlation matrix\ncorrelation_matrix.fillna(0, inplace=True) # Replace NaNs with 0 or another value as appropriate\n\n# Set up the plot structure\nplt.figure(figsize=(18, 12)) # Adjust height to give more space for y-axis labels\n\n# Plot the heatmap\nsns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0, cbar_kws={\"shrink\": .8}, linewidths=.5)\n\n# Enhance axis labels and title\nplt.title('Heatmap for GII and GDP Change', fontsize=20)\nplt.xlabel('Variables', fontsize=16)\nplt.ylabel('Country', fontsize=16)\n\n# Improve readability of y-axis labels\nplt.yticks(fontsize=12) # Adjust the font size for y-axis labels\n\n# Show the plot\nplt.show()" + }, + { + "objectID": "Code/imfp_demo.html#statistical-analysis", + "href": "Code/imfp_demo.html#statistical-analysis", + "title": "imfp Demo Project", + "section": "Statistical Analysis", + "text": "Statistical Analysis\n\nDescriptive Statistics\nThere is a total of 915 data points. The mean of the GII change in -0.0314868, which indicates the overall grand mean percent change in gender inequality index is -3.15%. The mean of the GDP change is 0.0234633, showing the overall grand mean percent change in real GDP per capita is 2.35%.\n\n# Generate summary statistics\ndescriptive_stats = combined_data.describe()\nprint(tabulate(descriptive_stats, headers='keys', tablefmt='github'))\n\n| | GII_change | adjusted_nominal | adjusted_index | adjusted_population | Real_GDP_domestic | Real_GDP_per_capita | GDP_change |\n|-------|--------------|--------------------|------------------|-----------------------|---------------------|-----------------------|--------------|\n| count | 914 | 914 | 914 | 914 | 914 | 914 | 914 |\n| mean | -0.0314506 | 7.85541e+13 | 0.855504 | 4.50061e+07 | 7.60737e+13 | 1.00595e+06 | 0.023532 |\n| std | 0.054783 | 6.01311e+14 | 0.210057 | 1.24493e+08 | 5.52717e+14 | 3.88155e+06 | 0.0418127 |\n| min | -0.755003 | 2.18714e+09 | 0.0360636 | 396324 | 5.21733e+09 | 1798.42 | -0.285847 |\n| 25% | -0.0424105 | 1.07655e+11 | 0.743355 | 5.0737e+06 | 1.48206e+11 | 17176 | 0.00384809 |\n| 50% | -0.020248 | 7.50706e+11 | 0.888883 | 1.0337e+07 | 9.94933e+11 | 35401 | 0.0222486 |\n| 75% | -0.00451301 | 2.5911e+12 | 1.00175 | 4.5159e+07 | 2.9412e+12 | 120391 | 0.0427318 |\n| max | 0.156992 | 9.54613e+15 | 2.0788 | 1.28084e+09 | 7.92007e+15 | 3.14532e+07 | 0.241984 |\n\n\n\n\nRegression Analysis\nSimple linear regression as a foundational approach provide us with a basic understanding of the relationship between GDP change and GII change.\nBased on the summary, we can conclude the following: * Becasue p-value = 0.057, if we set alpha, the significance level, to be 0.05, we fail to reject the null hypothesis and conclude there is no significant relationship between percent change in real GDP per capita and gender inequality index.\n\nR-squared = 0.004. Only 0.4% of the variance in GII change can be explained by GDP change.\nWe are 95% confident that the interval from -0.003 to 0.169 captures the true slope of GDP change. Because 0 is included, we are uncertain about the effect of GDP change on GII chnage.\n\n\n# Define independent and depenent variables\nX = combined_data['GDP_change']\ny = combined_data['GII_change']\n\n# Add a constant to indepdent variable to include an intercept\nX = sm.add_constant(X)\n\n# Fit a simple linear regresion model and print out the summary\nmodel = sm.OLS(y, X).fit()\nprint(model.summary())\n\n OLS Regression Results \n==============================================================================\nDep. Variable: GII_change R-squared: 0.004\nModel: OLS Adj. R-squared: 0.003\nMethod: Least Squares F-statistic: 3.551\nDate: Wed, 25 Dec 2024 Prob (F-statistic): 0.0598\nTime: 16:32:05 Log-Likelihood: 1360.0\nNo. Observations: 914 AIC: -2716.\nDf Residuals: 912 BIC: -2706.\nDf Model: 1 \nCovariance Type: nonrobust \n==============================================================================\n coef std err t P>|t| [0.025 0.975]\n------------------------------------------------------------------------------\nconst -0.0334 0.002 -16.069 0.000 -0.037 -0.029\nGDP_change 0.0816 0.043 1.885 0.060 -0.003 0.167\n==============================================================================\nOmnibus: 841.660 Durbin-Watson: 1.384\nProb(Omnibus): 0.000 Jarque-Bera (JB): 53999.190\nSkew: -3.938 Prob(JB): 0.00\nKurtosis: 39.823 Cond. No. 23.9\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\n\n\n\nTime Series Analysis\nTime series analysis allows us to explore how the relationship between GII and GDP change vary across different time periods, accounting for lagged effects.\nHere is a quick summary of the result: * Both GII and GDP change time series are stationary.\n\nPast GII change values significantly influence cuurent GII change values.\nVAR model has good model performance on forecasting future values based on historical data.\nChanges in GDP does not cause/precde the changes in GII.\n\n\nADF Test: Stationality Assumption Check\nWe want to use Augmented Dickey-Fuller (ADF) test to check whether a time series is stationary, which is the model assumption for many time series models.\nStationarity implies constant mean and variance over time, making it more predictable and stable for forecasting.\nBased on the ADF test output, both GII and GDP change time series are stationary. We can proceed to the time series modeling section.\n\n# Augmented Dickey-Fuller (ADF) test for stationarity check\n# Create melted datasets\ncombined_data_time = combined_data.melt(id_vars=['Time', 'Country'], value_vars=['GII_change','GDP_change'], var_name = 'Change_Type', value_name = 'Value')\nGII = combined_data_time[(combined_data_time['Change_Type'] == 'GII_change')] \n\nGDP = combined_data_time[(combined_data_time['Change_Type'] == 'GDP_change')]\n\n# Stationary Check\ndef adf_test(series):\n result = adfuller(series.dropna())\n print(f'ADF Statistic: {result[0]}')\n print(f'p-value: {result[1]}')\n if result[1] < 0.05:\n print(\"Series is stationary\")\n else:\n print(\"Series is not stationary\")\n\n# Output the result\nadf_test(GII['Value'])\nadf_test(GDP['Value'])\n\nADF Statistic: -16.069430375623295\np-value: 5.527565277907626e-29\nSeries is stationary\nADF Statistic: -8.535683414310181\np-value: 1.0095278443825336e-13\nSeries is stationary\n\n\n\n\nVAR model: Examine variables separately\nWe fit a VAR (Vector Autoreression) model to see the relationship between GII and GDP change. VAR is particularly useful when dealing with multivariate time series data and allows us to examine the interdependence between variables.\nBased on summary, here are several interpretations we can make: - We used AIC as the criteria for model selection. Lower value suggests a better fit. - Given that we want to predict GII change, we will focus on the first set “Results for equation GII_change.” - Past GII_change values significantly influence current GII_change, as shown in the small p-values of lags 1 and 2. - Lag 2 of GDP_change has a relatively low p-value but is not statistically significant.\n\n# Split the dataset into training and testing sets\nsplit_ratio = 0.7\nsplit_index = int(len(combined_data) * split_ratio)\n\n# Training set is used to fit the model\ntrain_data = combined_data.iloc[:split_index]\n\n# Testing set is used for validation\ntest_data = combined_data.iloc[split_index:]\n\nprint(f\"Training data: {train_data.shape}\")\nprint(f\"Test data: {test_data.shape}\")\n\nTraining data: (639, 9)\nTest data: (275, 9)\n\n\n\n# Fit a VAR model \ntime_model = VAR(train_data[['GII_change', 'GDP_change']])\ntime_model_fitted = time_model.fit(maxlags = 15, ic=\"aic\")\n\n# Print out the model summary\nprint(time_model_fitted.summary())\n\n Summary of Regression Results \n==================================\nModel: VAR\nMethod: OLS\nDate: Wed, 25, Dec, 2024\nTime: 16:32:06\n--------------------------------------------------------------------\nNo. of Equations: 2.00000 BIC: -12.2270\nNobs: 636.000 HQIC: -12.2869\nLog likelihood: 2128.47 FPE: 4.43927e-06\nAIC: -12.3250 Det(Omega_mle): 4.34314e-06\n--------------------------------------------------------------------\nResults for equation GII_change\n================================================================================\n coefficient std. error t-stat prob\n--------------------------------------------------------------------------------\nconst -0.023760 0.003176 -7.481 0.000\nL1.GII_change 0.211342 0.039900 5.297 0.000\nL1.GDP_change -0.015713 0.046080 -0.341 0.733\nL2.GII_change 0.111672 0.040402 2.764 0.006\nL2.GDP_change 0.088293 0.046452 1.901 0.057\nL3.GII_change -0.021861 0.039774 -0.550 0.583\nL3.GDP_change 0.000651 0.046141 0.014 0.989\n================================================================================\n\nResults for equation GDP_change\n================================================================================\n coefficient std. error t-stat prob\n--------------------------------------------------------------------------------\nconst 0.014585 0.002720 5.362 0.000\nL1.GII_change 0.004128 0.034171 0.121 0.904\nL1.GDP_change 0.141566 0.039464 3.587 0.000\nL2.GII_change -0.009734 0.034600 -0.281 0.778\nL2.GDP_change 0.044282 0.039782 1.113 0.266\nL3.GII_change -0.037787 0.034063 -1.109 0.267\nL3.GDP_change 0.143870 0.039516 3.641 0.000\n================================================================================\n\nCorrelation matrix of residuals\n GII_change GDP_change\nGII_change 1.000000 0.040992\nGDP_change 0.040992 1.000000\n\n\n\n\n\n/Users/sammi/Library/Caches/pypoetry/virtualenvs/imfp-0AlIckOY-py3.13/lib/python3.13/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: An unsupported index was provided. As a result, forecasts cannot be generated. To use the model for forecasting, use one of the supported classes of index.\n self._init_dates(dates, freq)\n\n\n\n\nVAR Model: Forecasting\nWe apply the model learned above to the test data. Based on the plot, the forecast values seem to follow the actual data well, indicating a good model fit caputuring the underlying trends.\n\n# Number of steps to forecast (length of the test set)\nn_steps = len(test_data)\n\n# Get the last values from the training set for forecasting\nforecast_input = train_data[['GII_change', 'GDP_change']].values[-time_model_fitted.k_ar:]\n\n# Forecasting\nforecast = time_model_fitted.forecast(y=forecast_input, steps=n_steps)\n\n# Create a DataFrame for the forecasted values\nforecast_df = pd.DataFrame(forecast, index=test_data.index, columns=['GII_forecast', 'GDP_forecast'])\n\n# Ensure the index of the forecast_df matches the test_data index\nforecast_df.index = test_data.index\n\n\nplt.figure(figsize=(12, 6))\nplt.plot(train_data['GII_change'], label='Training GII', color='blue')\nplt.plot(test_data['GII_change'], label='Actual GII', color='orange')\nplt.plot(forecast_df['GII_forecast'], label='Forecasted GII', color='green')\nplt.title('GII Change Forecast vs Actual')\nplt.legend()\nplt.show()\n\nplt.figure(figsize=(12, 6))\nplt.plot(train_data['GDP_change'], label='Training GDP', color='blue')\nplt.plot(test_data['GDP_change'], label='Actual GDP', color='orange')\nplt.plot(forecast_df['GDP_forecast'], label='Forecasted GDP', color='green')\nplt.title('GDP Change Forecast vs Actual')\nplt.legend()\nplt.show()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nVAR Model: Model Performance\nLow values of both MAE and RMSE indicate good model performance with small average errors in predictions.\n\nmae_gii = mean_absolute_error(test_data['GII_change'], forecast_df['GII_forecast'])\nmae_gdp = mean_absolute_error(test_data['GDP_change'], forecast_df['GDP_forecast'])\n\nprint(f'Mean Absolute Error for GII: {mae_gii}')\nprint(f'Mean Absolute Error for GDP: {mae_gdp}')\n\nMean Absolute Error for GII: 0.03296113056900485\nMean Absolute Error for GDP: 0.027636232780964363\n\n\n\nrmse_gii = np.sqrt(mean_squared_error(test_data['GII_change'], forecast_df['GII_forecast']))\nrmse_gdp = np.sqrt(mean_squared_error(test_data['GDP_change'], forecast_df['GDP_forecast']))\n\nprint(f'RMSE for GII: {rmse_gii}')\nprint(f'RMSE for GDP: {rmse_gdp}')\n\nRMSE for GII: 0.06284754560659174\nRMSE for GDP: 0.038429216092797085\n\n\n\n\nVAR Model: Granger causality test\nGranger causality test evaluates whether one time series can predict another.\nBased on the output, the lowest p-value is when lag = 2. However, because p-value > 0.05, we fail to reject the null hypothesis and conclude the GDP_change does not Granger-cause the GII_change.\n\n# Perform the Granger causality test\nmax_lag = 3\ntest_result = grangercausalitytests(train_data[['GII_change', 'GDP_change']], max_lag, verbose=True)\n\n\nGranger Causality\nnumber of lags (no zero) 1\nssr based F test: F=0.0000 , p=0.9972 , df_denom=635, df_num=1\nssr based chi2 test: chi2=0.0000 , p=0.9972 , df=1\nlikelihood ratio test: chi2=0.0000 , p=0.9972 , df=1\nparameter F test: F=0.0000 , p=0.9972 , df_denom=635, df_num=1\n\nGranger Causality\nnumber of lags (no zero) 2\nssr based F test: F=1.8529 , p=0.1576 , df_denom=632, df_num=2\nssr based chi2 test: chi2=3.7351 , p=0.1545 , df=2\nlikelihood ratio test: chi2=3.7242 , p=0.1553 , df=2\nparameter F test: F=1.8529 , p=0.1576 , df_denom=632, df_num=2\n\nGranger Causality\nnumber of lags (no zero) 3\nssr based F test: F=1.2359 , p=0.2958 , df_denom=629, df_num=3\nssr based chi2 test: chi2=3.7489 , p=0.2899 , df=3\nlikelihood ratio test: chi2=3.7379 , p=0.2912 , df=3\nparameter F test: F=1.2359 , p=0.2958 , df_denom=629, df_num=3\n\n\n/Users/sammi/Library/Caches/pypoetry/virtualenvs/imfp-0AlIckOY-py3.13/lib/python3.13/site-packages/statsmodels/tsa/stattools.py:1556: FutureWarning: verbose is deprecated since functions should not print results\n warnings.warn(" + }, + { + "objectID": "Code/imfp_demo.html#conclusion", + "href": "Code/imfp_demo.html#conclusion", + "title": "imfp Demo Project", + "section": "Conclusion", + "text": "Conclusion\nIn wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research." + }, + { + "objectID": "Code/imfp_demo.html#future-directions", + "href": "Code/imfp_demo.html#future-directions", + "title": "imfp Demo Project", + "section": "Future Directions", + "text": "Future Directions\n\nAdditional Predictors: Gender Inequality Index (GII) is a composite measure shaped by a mariad of social factors. What about culture, policy, and environment?\nSpecific Country: Are there any specific historical contexts that sparked economic growth and improvement in gender equality?\nOpposite causation: Women entering workforce boost GDP?\nPredictive Power of VAR model: Our model performed well in forecasting trends for both variables independently.\n\nLet’s keep our curiosity alive and our questions flowing!" + }, + { + "objectID": "Blog/blog/Blog.html", + "href": "Blog/blog/Blog.html", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "", + "text": "Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders?\nOur main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables.\nReady for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!" + }, + { + "objectID": "Blog/blog/Blog.html#introduction", + "href": "Blog/blog/Blog.html#introduction", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "", + "text": "Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders?\nOur main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables.\nReady for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!" + }, + { + "objectID": "Blog/blog/Blog.html#data-preparation", + "href": "Blog/blog/Blog.html#data-preparation", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "Data Preparation", + "text": "Data Preparation\n​​To explore how economic growth connects with gender equality, we turned to the Imfp library to access a wealth of global economic data, ready to analyze through some straightforward API calls. Let’s break down our two main variables of interest. Here’s the lowdown on the 4 key data series we pulled together:\n\n\nDependent Variable: Percent Change in Gender Inequality Index (GII)\nOur gender equality measure, the Gender Inequality Index (GII), is a composite score that reflects inequality in three key areas: reproductive health, empowerment, and labor market, ranging from 0 (equality) to 1 (inequality). We calculated the percent change in GII over time to make it easier to compare GII change across countries with different baseline levels of inequality.\n\n\nIndependent Variable: Percent Change in Real GDP per capita\nOur economic growth measure is real GDP per capita, an indicator of a country’s economic output per person, adjusted for inflation. The higher the real GDP per capita, the wealthier and more prosperous a country is. Here is a peek at the formula:" + }, + { + "objectID": "Blog/blog/Blog.html#data-visualization", + "href": "Blog/blog/Blog.html#data-visualization", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "Data Visualization", + "text": "Data Visualization\n\nScatterplot\nNothing like a good scatterplot to help us understand the relationship between two numbers, right? For our project, the scatterplot lets us visually explore how Percent Change in Real GDP per capita relates to Percent Change in GII. Each dot on the plot represents a country-year combo. The gentle upward slope suggests that in years of low GDP growth, gender inequality tends to decrease a bit faster, while in years of high GDP growth, the inequality drops at a slightly slower rate. It’s a subtle clue, but slower-growing economies might be making slightly quicker strides in gender equality.\n \n\n\nTime Series Line plot\nThe time series line plot gives us the big picture of GDP and GII changes over time across a few select countries, including the United States, Japan, the United Kingdom, France, and Mexico. Mexico, for example, saw great GDP swings in 1995, yet its GII stayed relatively stable. Meanwhile, Japan and France saw some dramatic GII shifts. These fluctuations leave us wondering: what historical events sparked these changes? We may need to take a deeper look into each country’s unique timeline to find the answers.\n \n\n\nBoxplot\nNext, we have the boxplot to help us visualize the distribution. By sequencing countries in ascending order of median GDP change, we see an interesting trend: countries with higher median GDP growth also seem to show more variability in their GDP changes. The GII change, on the other hand, keeps a relatively stable median, hinting at a weaker association with GDP growth.\n \n\n\nHeatmap\nFinally, our heatmap wraps it all up with a quick, intuitive look at correlations between GDP and GII across countries. In countries like Estonia (EE) and Ireland (IE), Australia (AT), South Africa (ZA), Thailand (TH), Slovenia (SI), we see positive correlations in warm colors, indicating that as GDP growth rates increase, GII growth rates tend to rise as well. For countries like Cyprus (CY), Romania (RO), and the United States (US), we see cooler colors, representing negative correlations: as GDP growth rate rises, GII growth rates decrease, suggesting an improvement in gender equality alongside economic growth. This color-coded comparison leaves us curious—could the distinction between developed and developing countries play a role in these relationships? It’s a question that might inspire future research directions!" + }, + { + "objectID": "Blog/blog/Blog.html#statistical-analysis", + "href": "Blog/blog/Blog.html#statistical-analysis", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "Statistical Analysis", + "text": "Statistical Analysis\n\nDescriptive Statistics\nLet’s start with a quick overview of our descriptive statistics: our dataset includes 915 data points. On average, GII change is -3.15%, indicating an overall reduction in gender inequality over time. Meanwhile, GDP change averages 2.35%, hinting at modest economic growth. A full breakdown of these values can be found in the table below for more detail.\n\n\n\nRegression Analysis\nTo get a sense of any relationship between GDP change and GII change, we ran a simple linear regression. Here’s what we found:\n\nP-value: With a p-value of 0.057, we fail to reject the null hypothesis. There is no statistically significant relationship. In other words, we don’t find strong evidence of a link between GDP and GII changes.\nR-squared: GDP change explains only 0.4% of the variation in GII change—a pretty minimal effect.\n95% Confidence Interval: We’re 95% confident the interval from -0.003 to 0.169 captures the true slope. The effect of GDP change on GII is uncertain as 0 is within this range.\n\n\n\nTime Series Analysis\nTime series analysis allows us to dig deeper into how GII and GDP changes play out over time—and potentially influence one another. Here’s the rundown:\n\nStationarity check: Both GII and GDP change series are stationary, meaning their means and variances are stable over time, setting us up for reliable forecasting.\nVAR Model (Vector Autoregression): This multivariate model lets us explore how past values in both series relate to current values. Notably, past GII changes significantly impact current GII changes.\nModel Performance: The VAR model has good fit, as shown by the forecast closely tracking actual values. Low MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) confirm that our model predicts GII and GDP change with minimal error. Check out the plots and table below!\nGranger Causality Test: Curious whether GDP changes can predict changes in GII? The Granger causality test says no. With p-values above 0.05, we fail to find evidence that GDP changes drive GII changes. This suggests that while both variables evolve over time, they don’t seem to directly influence each other." + }, + { + "objectID": "Blog/blog/Blog.html#conclusion", + "href": "Blog/blog/Blog.html#conclusion", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "Conclusion", + "text": "Conclusion\nIn wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research.\nIn terms of the Imfp library, we gained valuable experience working with real-world economic indicators like GDP and the GII. The library’s function in fetching databases, parameters, and datasets made it straightforward to access IMF’s data, allowing us to focus on analysis instead. The integration of other Python tools—such as seaborn, matplotlib, and numpy—not only streamlined our computational processes but also ensured consistency across the project." + }, + { + "objectID": "Blog/blog/Blog.html#future-directions", + "href": "Blog/blog/Blog.html#future-directions", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "Future Directions", + "text": "Future Directions\nFirst, we must consider what other factors might influence the relationship between GDP and GII change. The GII is a composite index, shaped by a myriad of social factors, including cultural norms, legal frameworks, and environmental shifts. Future studies could benefit from incorporating additional predictors into the analysis and exploring the interaction between economic growth and gender equality within specific country contexts.\nSecond, there’s potential to enhance the predictive power of our Vector Autoregression (VAR) time series model. While we established that GDP change does not cause GII change, our model performed well in forecasting trends for both variables independently. In practice, policymakers may want to forecast GII trends independently of GDP if they are implementing gender-focused policies. Future research could investigate time series modeling to further unravel the dynamics of GII and GDP changes.\nSo, as we wrap up this chapter, let’s keep our curiosity alive and our questions flowing. After all, every end is just a new beginning in the quest for knowledge!" + }, + { + "objectID": "About/About.html", + "href": "About/About.html", + "title": "Jenny Xu", + "section": "", + "text": "Hi there! My name is Jenny, and I’m a third-year student at University of California, Davis, double majoring in Statistics and Psychology. I’ve always been interested in becoming a data analyst working in tech, internet, or research industries. Interning at Promptly Technologies helped me learn a ton. A quick fun fact for me is that my MBTI is ISFJ (Defender)!" + }, + { + "objectID": "Blog/blog/listing.html", + "href": "Blog/blog/listing.html", + "title": "Blog", + "section": "", + "text": "No matching items" + }, + { + "objectID": "Blog/post/listing.html", + "href": "Blog/post/listing.html", + "title": "Blog", + "section": "", + "text": "No matching items" + }, + { + "objectID": "Blog/post/Blog.html", + "href": "Blog/post/Blog.html", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "", + "text": "Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders?\nOur main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables.\nReady for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!" + }, + { + "objectID": "Blog/post/Blog.html#introduction", + "href": "Blog/post/Blog.html#introduction", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "", + "text": "Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders?\nOur main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables.\nReady for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!" + }, + { + "objectID": "Blog/post/Blog.html#data-preparation", + "href": "Blog/post/Blog.html#data-preparation", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "Data Preparation", + "text": "Data Preparation\n​​To explore how economic growth connects with gender equality, we turned to the Imfp library to access a wealth of global economic data, ready to analyze through some straightforward API calls. Let’s break down our two main variables of interest. Here’s the lowdown on the 4 key data series we pulled together:\n\n\nDependent Variable: Percent Change in Gender Inequality Index (GII)\nOur gender equality measure, the Gender Inequality Index (GII), is a composite score that reflects inequality in three key areas: reproductive health, empowerment, and labor market, ranging from 0 (equality) to 1 (inequality). We calculated the percent change in GII over time to make it easier to compare GII change across countries with different baseline levels of inequality.\n\n\nIndependent Variable: Percent Change in Real GDP per capita\nOur economic growth measure is real GDP per capita, an indicator of a country’s economic output per person, adjusted for inflation. The higher the real GDP per capita, the wealthier and more prosperous a country is. Here is a peek at the formula:" + }, + { + "objectID": "Blog/post/Blog.html#data-visualization", + "href": "Blog/post/Blog.html#data-visualization", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "Data Visualization", + "text": "Data Visualization\n\nScatterplot\nNothing like a good scatterplot to help us understand the relationship between two numbers, right? For our project, the scatterplot lets us visually explore how Percent Change in Real GDP per capita relates to Percent Change in GII. Each dot on the plot represents a country-year combo. The gentle upward slope suggests that in years of low GDP growth, gender inequality tends to decrease a bit faster, while in years of high GDP growth, the inequality drops at a slightly slower rate. It’s a subtle clue, but slower-growing economies might be making slightly quicker strides in gender equality.\n \n\n\nTime Series Line plot\nThe time series line plot gives us the big picture of GDP and GII changes over time across a few select countries, including the United States, Japan, the United Kingdom, France, and Mexico. Mexico, for example, saw great GDP swings in 1995, yet its GII stayed relatively stable. Meanwhile, Japan and France saw some dramatic GII shifts. These fluctuations leave us wondering: what historical events sparked these changes? We may need to take a deeper look into each country’s unique timeline to find the answers.\n \n\n\nBoxplot\nNext, we have the boxplot to help us visualize the distribution. By sequencing countries in ascending order of median GDP change, we see an interesting trend: countries with higher median GDP growth also seem to show more variability in their GDP changes. The GII change, on the other hand, keeps a relatively stable median, hinting at a weaker association with GDP growth.\n \n\n\nHeatmap\nFinally, our heatmap wraps it all up with a quick, intuitive look at correlations between GDP and GII across countries. In countries like Estonia (EE) and Ireland (IE), Australia (AT), South Africa (ZA), Thailand (TH), Slovenia (SI), we see positive correlations in warm colors, indicating that as GDP growth rates increase, GII growth rates tend to rise as well. For countries like Cyprus (CY), Romania (RO), and the United States (US), we see cooler colors, representing negative correlations: as GDP growth rate rises, GII growth rates decrease, suggesting an improvement in gender equality alongside economic growth. This color-coded comparison leaves us curious—could the distinction between developed and developing countries play a role in these relationships? It’s a question that might inspire future research directions!" + }, + { + "objectID": "Blog/post/Blog.html#statistical-analysis", + "href": "Blog/post/Blog.html#statistical-analysis", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "Statistical Analysis", + "text": "Statistical Analysis\n\nDescriptive Statistics\nLet’s start with a quick overview of our descriptive statistics: our dataset includes 915 data points. On average, GII change is -3.15%, indicating an overall reduction in gender inequality over time. Meanwhile, GDP change averages 2.35%, hinting at modest economic growth. A full breakdown of these values can be found in the table below for more detail.\n\n\n\nRegression Analysis\nTo get a sense of any relationship between GDP change and GII change, we ran a simple linear regression. Here’s what we found:\n\nP-value: With a p-value of 0.057, we fail to reject the null hypothesis. There is no statistically significant relationship. In other words, we don’t find strong evidence of a link between GDP and GII changes.\nR-squared: GDP change explains only 0.4% of the variation in GII change—a pretty minimal effect.\n95% Confidence Interval: We’re 95% confident the interval from -0.003 to 0.169 captures the true slope. The effect of GDP change on GII is uncertain as 0 is within this range.\n\n\n\nTime Series Analysis\nTime series analysis allows us to dig deeper into how GII and GDP changes play out over time—and potentially influence one another. Here’s the rundown:\n\nStationarity check: Both GII and GDP change series are stationary, meaning their means and variances are stable over time, setting us up for reliable forecasting.\nVAR Model (Vector Autoregression): This multivariate model lets us explore how past values in both series relate to current values. Notably, past GII changes significantly impact current GII changes.\nModel Performance: The VAR model has good fit, as shown by the forecast closely tracking actual values. Low MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) confirm that our model predicts GII and GDP change with minimal error. Check out the plots and table below!\nGranger Causality Test: Curious whether GDP changes can predict changes in GII? The Granger causality test says no. With p-values above 0.05, we fail to find evidence that GDP changes drive GII changes. This suggests that while both variables evolve over time, they don’t seem to directly influence each other." + }, + { + "objectID": "Blog/post/Blog.html#conclusion", + "href": "Blog/post/Blog.html#conclusion", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "Conclusion", + "text": "Conclusion\nIn wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research.\nIn terms of the Imfp library, we gained valuable experience working with real-world economic indicators like GDP and the GII. The library’s function in fetching databases, parameters, and datasets made it straightforward to access IMF’s data, allowing us to focus on analysis instead. The integration of other Python tools—such as seaborn, matplotlib, and numpy—not only streamlined our computational processes but also ensured consistency across the project." + }, + { + "objectID": "Blog/post/Blog.html#future-directions", + "href": "Blog/post/Blog.html#future-directions", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "Future Directions", + "text": "Future Directions\nFirst, we must consider what other factors might influence the relationship between GDP and GII change. The GII is a composite index, shaped by a myriad of social factors, including cultural norms, legal frameworks, and environmental shifts. Future studies could benefit from incorporating additional predictors into the analysis and exploring the interaction between economic growth and gender equality within specific country contexts.\nSecond, there’s potential to enhance the predictive power of our Vector Autoregression (VAR) time series model. While we established that GDP change does not cause GII change, our model performed well in forecasting trends for both variables independently. In practice, policymakers may want to forecast GII trends independently of GDP if they are implementing gender-focused policies. Future research could investigate time series modeling to further unravel the dynamics of GII and GDP changes.\nSo, as we wrap up this chapter, let’s keep our curiosity alive and our questions flowing. After all, every end is just a new beginning in the quest for knowledge!" + }, + { + "objectID": "posts/posts/Blog.html", + "href": "posts/posts/Blog.html", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "", + "text": "Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders?\nOur main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables.\nReady for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!" + }, + { + "objectID": "posts/posts/Blog.html#introduction", + "href": "posts/posts/Blog.html#introduction", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "", + "text": "Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders?\nOur main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables.\nReady for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!" + }, + { + "objectID": "posts/posts/Blog.html#data-preparation", + "href": "posts/posts/Blog.html#data-preparation", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "Data Preparation", + "text": "Data Preparation\n​​To explore how economic growth connects with gender equality, we turned to the Imfp library to access a wealth of global economic data, ready to analyze through some straightforward API calls. Let’s break down our two main variables of interest. Here’s the lowdown on the 4 key data series we pulled together:\n\n\nDependent Variable: Percent Change in Gender Inequality Index (GII)\nOur gender equality measure, the Gender Inequality Index (GII), is a composite score that reflects inequality in three key areas: reproductive health, empowerment, and labor market, ranging from 0 (equality) to 1 (inequality). We calculated the percent change in GII over time to make it easier to compare GII change across countries with different baseline levels of inequality.\n\n\nIndependent Variable: Percent Change in Real GDP per capita\nOur economic growth measure is real GDP per capita, an indicator of a country’s economic output per person, adjusted for inflation. The higher the real GDP per capita, the wealthier and more prosperous a country is. Here is a peek at the formula:" + }, + { + "objectID": "posts/posts/Blog.html#data-visualization", + "href": "posts/posts/Blog.html#data-visualization", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "Data Visualization", + "text": "Data Visualization\n\nScatterplot\nNothing like a good scatterplot to help us understand the relationship between two numbers, right? For our project, the scatterplot lets us visually explore how Percent Change in Real GDP per capita relates to Percent Change in GII. Each dot on the plot represents a country-year combo. The gentle upward slope suggests that in years of low GDP growth, gender inequality tends to decrease a bit faster, while in years of high GDP growth, the inequality drops at a slightly slower rate. It’s a subtle clue, but slower-growing economies might be making slightly quicker strides in gender equality.\n \n\n\nTime Series Line plot\nThe time series line plot gives us the big picture of GDP and GII changes over time across a few select countries, including the United States, Japan, the United Kingdom, France, and Mexico. Mexico, for example, saw great GDP swings in 1995, yet its GII stayed relatively stable. Meanwhile, Japan and France saw some dramatic GII shifts. These fluctuations leave us wondering: what historical events sparked these changes? We may need to take a deeper look into each country’s unique timeline to find the answers.\n \n\n\nBoxplot\nNext, we have the boxplot to help us visualize the distribution. By sequencing countries in ascending order of median GDP change, we see an interesting trend: countries with higher median GDP growth also seem to show more variability in their GDP changes. The GII change, on the other hand, keeps a relatively stable median, hinting at a weaker association with GDP growth.\n \n\n\nHeatmap\nFinally, our heatmap wraps it all up with a quick, intuitive look at correlations between GDP and GII across countries. In countries like Estonia (EE) and Ireland (IE), Australia (AT), South Africa (ZA), Thailand (TH), Slovenia (SI), we see positive correlations in warm colors, indicating that as GDP growth rates increase, GII growth rates tend to rise as well. For countries like Cyprus (CY), Romania (RO), and the United States (US), we see cooler colors, representing negative correlations: as GDP growth rate rises, GII growth rates decrease, suggesting an improvement in gender equality alongside economic growth. This color-coded comparison leaves us curious—could the distinction between developed and developing countries play a role in these relationships? It’s a question that might inspire future research directions!" + }, + { + "objectID": "posts/posts/Blog.html#statistical-analysis", + "href": "posts/posts/Blog.html#statistical-analysis", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "Statistical Analysis", + "text": "Statistical Analysis\n\nDescriptive Statistics\nLet’s start with a quick overview of our descriptive statistics: our dataset includes 915 data points. On average, GII change is -3.15%, indicating an overall reduction in gender inequality over time. Meanwhile, GDP change averages 2.35%, hinting at modest economic growth. A full breakdown of these values can be found in the table below for more detail.\n\n\n\nRegression Analysis\nTo get a sense of any relationship between GDP change and GII change, we ran a simple linear regression. Here’s what we found:\n\nP-value: With a p-value of 0.057, we fail to reject the null hypothesis. There is no statistically significant relationship. In other words, we don’t find strong evidence of a link between GDP and GII changes.\nR-squared: GDP change explains only 0.4% of the variation in GII change—a pretty minimal effect.\n95% Confidence Interval: We’re 95% confident the interval from -0.003 to 0.169 captures the true slope. The effect of GDP change on GII is uncertain as 0 is within this range.\n\n\n\nTime Series Analysis\nTime series analysis allows us to dig deeper into how GII and GDP changes play out over time—and potentially influence one another. Here’s the rundown:\n\nStationarity check: Both GII and GDP change series are stationary, meaning their means and variances are stable over time, setting us up for reliable forecasting.\nVAR Model (Vector Autoregression): This multivariate model lets us explore how past values in both series relate to current values. Notably, past GII changes significantly impact current GII changes.\nModel Performance: The VAR model has good fit, as shown by the forecast closely tracking actual values. Low MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) confirm that our model predicts GII and GDP change with minimal error. Check out the plots and table below!\nGranger Causality Test: Curious whether GDP changes can predict changes in GII? The Granger causality test says no. With p-values above 0.05, we fail to find evidence that GDP changes drive GII changes. This suggests that while both variables evolve over time, they don’t seem to directly influence each other." + }, + { + "objectID": "posts/posts/Blog.html#conclusion", + "href": "posts/posts/Blog.html#conclusion", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "Conclusion", + "text": "Conclusion\nIn wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research.\nIn terms of the Imfp library, we gained valuable experience working with real-world economic indicators like GDP and the GII. The library’s function in fetching databases, parameters, and datasets made it straightforward to access IMF’s data, allowing us to focus on analysis instead. The integration of other Python tools—such as seaborn, matplotlib, and numpy—not only streamlined our computational processes but also ensured consistency across the project." + }, + { + "objectID": "posts/posts/Blog.html#future-directions", + "href": "posts/posts/Blog.html#future-directions", + "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", + "section": "Future Directions", + "text": "Future Directions\nFirst, we must consider what other factors might influence the relationship between GDP and GII change. The GII is a composite index, shaped by a myriad of social factors, including cultural norms, legal frameworks, and environmental shifts. Future studies could benefit from incorporating additional predictors into the analysis and exploring the interaction between economic growth and gender equality within specific country contexts.\nSecond, there’s potential to enhance the predictive power of our Vector Autoregression (VAR) time series model. While we established that GDP change does not cause GII change, our model performed well in forecasting trends for both variables independently. In practice, policymakers may want to forecast GII trends independently of GDP if they are implementing gender-focused policies. Future research could investigate time series modeling to further unravel the dynamics of GII and GDP changes.\nSo, as we wrap up this chapter, let’s keep our curiosity alive and our questions flowing. After all, every end is just a new beginning in the quest for knowledge!" + }, + { + "objectID": "posts/posts/listing.html", + "href": "posts/posts/listing.html", + "title": "Blog", + "section": "", + "text": "No matching items" + } +] \ No newline at end of file diff --git a/demo/demo/_site/site_libs/bootstrap/bootstrap-8bda806db8b623a4dafb352339cca83f.min.css b/demo/demo/_site/site_libs/bootstrap/bootstrap-8bda806db8b623a4dafb352339cca83f.min.css new file mode 100644 index 0000000..65ea065 --- /dev/null +++ b/demo/demo/_site/site_libs/bootstrap/bootstrap-8bda806db8b623a4dafb352339cca83f.min.css @@ -0,0 +1,12 @@ +/*! + * Bootstrap v5.3.1 (https://getbootstrap.com/) + * Copyright 2011-2023 The Bootstrap Authors + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) + */@import"https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@300;400;700&display=swap";:root,[data-bs-theme=light]{--bs-blue: #2780e3;--bs-indigo: #6610f2;--bs-purple: #613d7c;--bs-pink: 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0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0}.card>.list-group:last-child{border-bottom-width:0}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #343a40;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #343a40;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23343a40'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%2310335b'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #93c0f1;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #10335b;--bs-accordion-active-bg: #d4e6f9}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #2761e3;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #1f4eb6;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #1f4eb6;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(52, 58, 64, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #343a40;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(52, 58, 64, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #343a40;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(52, 58, 64, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(52, 58, 64, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color)}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media(max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #fff;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:"Source Sans Pro",-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:rgba(0,0,0,0);border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow{bottom:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-top .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow::before{top:-1px;border-width:var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-top-color:var(--bs-tooltip-bg)}.bs-tooltip-end .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow{left:calc(-1*var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-end .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow::before{right:-1px;border-width:calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-right-color:var(--bs-tooltip-bg)}.bs-tooltip-bottom .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow{top:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-bottom .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow::before{bottom:-1px;border-width:0 calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height);border-bottom-color:var(--bs-tooltip-bg)}.bs-tooltip-start .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow{right:calc(-1*var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-start .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow::before{left:-1px;border-width:calc(var(--bs-tooltip-arrow-width)*.5) 0 calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height);border-left-color:var(--bs-tooltip-bg)}.tooltip-inner{max-width:var(--bs-tooltip-max-width);padding:var(--bs-tooltip-padding-y) var(--bs-tooltip-padding-x);color:var(--bs-tooltip-color);text-align:center;background-color:var(--bs-tooltip-bg)}.popover{--bs-popover-zindex: 1070;--bs-popover-max-width: 276px;--bs-popover-font-size:0.875rem;--bs-popover-bg: #fff;--bs-popover-border-width: 1px;--bs-popover-border-color: rgba(0, 0, 0, 0.175);--bs-popover-border-radius: 0.5rem;--bs-popover-inner-border-radius: calc(0.5rem - 1px);--bs-popover-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-popover-header-padding-x: 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#fff);--bslib-value-box-border-color-default: var(--bs-card-border-color, rgba(0, 0, 0, 0.175));color:var(--bslib-value-box-color);background-color:var(--bslib-value-box-bg, var(--bslib-value-box-bg-default));border-color:var(--bslib-value-box-border-color, var(--bslib-value-box-border-color-default))}.bslib-value-box .value-box-grid{display:grid;grid-template-areas:"left right";align-items:center;overflow:hidden}.bslib-value-box .value-box-showcase{height:100%;max-height:var(---bslib-value-box-showcase-max-h, 100%)}.bslib-value-box .value-box-showcase,.bslib-value-box .value-box-showcase>.html-fill-item{width:100%}.bslib-value-box[data-full-screen=true] .value-box-showcase{max-height:var(---bslib-value-box-showcase-max-h-fs, 100%)}@media screen and (min-width: 575.98px){@container bslib-value-box (max-width: 300px){.bslib-value-box:not(.showcase-bottom) .value-box-grid{grid-template-columns:1fr !important;grid-template-rows:auto auto;grid-template-areas:"top" 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;--quarto-scss-export-hr-height: ;--quarto-scss-export-hr-border-color: ;--quarto-scss-export-legend-font-weight: ;--quarto-scss-export-mark-bg: #ffe3d1;--quarto-scss-export-table-color: #343a40;--quarto-scss-export-table-bg: #fff;--quarto-scss-export-table-accent-bg: transparent;--quarto-scss-export-table-th-font-weight: ;--quarto-scss-export-table-striped-color: #343a40;--quarto-scss-export-table-striped-bg: rgba(0, 0, 0, 0.05);--quarto-scss-export-table-active-color: #343a40;--quarto-scss-export-table-active-bg: rgba(0, 0, 0, 0.1);--quarto-scss-export-table-hover-color: #343a40;--quarto-scss-export-table-hover-bg: rgba(0, 0, 0, 0.075);--quarto-scss-export-table-caption-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-input-btn-font-family: ;--quarto-scss-export-input-btn-focus-color: rgba(39, 128, 227, 0.25);--quarto-scss-export-btn-color: #343a40;--quarto-scss-export-btn-font-family: ;--quarto-scss-export-btn-white-space: ;--quarto-scss-export-btn-link-color: #2761e3;--quarto-scss-export-btn-link-hover-color: #1f4eb6;--quarto-scss-export-btn-link-disabled-color: #6c757d;--quarto-scss-export-form-text-font-style: ;--quarto-scss-export-form-text-font-weight: ;--quarto-scss-export-form-text-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-form-label-font-size: ;--quarto-scss-export-form-label-font-style: ;--quarto-scss-export-form-label-font-weight: ;--quarto-scss-export-form-label-color: ;--quarto-scss-export-input-font-family: ;--quarto-scss-export-input-disabled-color: ;--quarto-scss-export-input-disabled-bg: #e9ecef;--quarto-scss-export-input-disabled-border-color: ;--quarto-scss-export-input-color: #343a40;--quarto-scss-export-input-focus-bg: #fff;--quarto-scss-export-input-focus-border-color: #93c0f1;--quarto-scss-export-input-focus-color: #343a40;--quarto-scss-export-input-placeholder-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-input-plaintext-color: #343a40;--quarto-scss-export-form-check-label-color: ;--quarto-scss-export-form-check-transition: ;--quarto-scss-export-form-check-input-bg: #fff;--quarto-scss-export-form-check-input-focus-border: #93c0f1;--quarto-scss-export-form-check-input-checked-color: #fff;--quarto-scss-export-form-check-input-checked-bg-color: #2780e3;--quarto-scss-export-form-check-input-checked-border-color: #2780e3;--quarto-scss-export-form-check-input-indeterminate-color: #fff;--quarto-scss-export-form-check-input-indeterminate-bg-color: #2780e3;--quarto-scss-export-form-check-input-indeterminate-border-color: #2780e3;--quarto-scss-export-form-switch-color: rgba(0, 0, 0, 0.25);--quarto-scss-export-form-switch-focus-color: #93c0f1;--quarto-scss-export-form-switch-checked-color: #fff;--quarto-scss-export-input-group-addon-color: #343a40;--quarto-scss-export-input-group-addon-bg: #f8f9fa;--quarto-scss-export-input-group-addon-border-color: #dee2e6;--quarto-scss-export-form-select-font-family: ;--quarto-scss-export-form-select-color: #343a40;--quarto-scss-export-form-select-bg: #fff;--quarto-scss-export-form-select-disabled-color: ;--quarto-scss-export-form-select-disabled-bg: #e9ecef;--quarto-scss-export-form-select-disabled-border-color: ;--quarto-scss-export-form-select-indicator-color: #343a40;--quarto-scss-export-form-select-border-color: #dee2e6;--quarto-scss-export-form-select-focus-border-color: #93c0f1;--quarto-scss-export-form-range-track-bg: #f8f9fa;--quarto-scss-export-form-range-thumb-bg: #2780e3;--quarto-scss-export-form-range-thumb-active-bg: #bed9f7;--quarto-scss-export-form-range-thumb-disabled-bg: rgba(52, 58, 64, 0.75);--quarto-scss-export-form-file-button-color: #343a40;--quarto-scss-export-form-file-button-bg: #f8f9fa;--quarto-scss-export-form-file-button-hover-bg: #e9ecef;--quarto-scss-export-form-floating-label-disabled-color: #6c757d;--quarto-scss-export-form-feedback-font-style: ;--quarto-scss-export-form-feedback-valid-color: #3fb618;--quarto-scss-export-form-feedback-invalid-color: #ff0039;--quarto-scss-export-form-feedback-icon-valid-color: #3fb618;--quarto-scss-export-form-feedback-icon-invalid-color: #ff0039;--quarto-scss-export-form-valid-color: #3fb618;--quarto-scss-export-form-valid-border-color: #3fb618;--quarto-scss-export-form-invalid-color: #ff0039;--quarto-scss-export-form-invalid-border-color: #ff0039;--quarto-scss-export-nav-link-font-size: ;--quarto-scss-export-nav-link-font-weight: ;--quarto-scss-export-nav-link-color: #2761e3;--quarto-scss-export-nav-link-hover-color: #1f4eb6;--quarto-scss-export-nav-link-disabled-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-nav-tabs-border-color: #dee2e6;--quarto-scss-export-nav-tabs-link-hover-border-color: #e9ecef #e9ecef #dee2e6;--quarto-scss-export-nav-tabs-link-active-color: #000;--quarto-scss-export-nav-tabs-link-active-bg: #fff;--quarto-scss-export-nav-pills-link-active-bg: #2780e3;--quarto-scss-export-nav-pills-link-active-color: #fff;--quarto-scss-export-nav-underline-link-active-color: #000;--quarto-scss-export-navbar-padding-x: ;--quarto-scss-export-navbar-light-contrast: #000;--quarto-scss-export-navbar-dark-contrast: #000;--quarto-scss-export-navbar-light-icon-color: rgba(0, 0, 0, 0.75);--quarto-scss-export-navbar-dark-icon-color: rgba(0, 0, 0, 0.75);--quarto-scss-export-dropdown-color: #343a40;--quarto-scss-export-dropdown-bg: #fff;--quarto-scss-export-dropdown-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-link-color: #343a40;--quarto-scss-export-dropdown-link-hover-color: #343a40;--quarto-scss-export-dropdown-link-hover-bg: #f8f9fa;--quarto-scss-export-dropdown-link-active-bg: #2780e3;--quarto-scss-export-dropdown-link-active-color: #fff;--quarto-scss-export-dropdown-link-disabled-color: rgba(52, 58, 64, 0.5);--quarto-scss-export-dropdown-header-color: #6c757d;--quarto-scss-export-dropdown-dark-color: #dee2e6;--quarto-scss-export-dropdown-dark-bg: #343a40;--quarto-scss-export-dropdown-dark-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-box-shadow: ;--quarto-scss-export-dropdown-dark-link-color: #dee2e6;--quarto-scss-export-dropdown-dark-link-hover-color: #fff;--quarto-scss-export-dropdown-dark-link-hover-bg: rgba(255, 255, 255, 0.15);--quarto-scss-export-dropdown-dark-link-active-color: #fff;--quarto-scss-export-dropdown-dark-link-active-bg: #2780e3;--quarto-scss-export-dropdown-dark-link-disabled-color: #adb5bd;--quarto-scss-export-dropdown-dark-header-color: #adb5bd;--quarto-scss-export-pagination-color: #2761e3;--quarto-scss-export-pagination-bg: #fff;--quarto-scss-export-pagination-border-color: #dee2e6;--quarto-scss-export-pagination-focus-color: #1f4eb6;--quarto-scss-export-pagination-focus-bg: #e9ecef;--quarto-scss-export-pagination-hover-color: #1f4eb6;--quarto-scss-export-pagination-hover-bg: #f8f9fa;--quarto-scss-export-pagination-hover-border-color: #dee2e6;--quarto-scss-export-pagination-active-color: #fff;--quarto-scss-export-pagination-active-bg: #2780e3;--quarto-scss-export-pagination-active-border-color: #2780e3;--quarto-scss-export-pagination-disabled-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-pagination-disabled-bg: #e9ecef;--quarto-scss-export-pagination-disabled-border-color: #dee2e6;--quarto-scss-export-card-title-color: ;--quarto-scss-export-card-subtitle-color: ;--quarto-scss-export-card-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-card-box-shadow: ;--quarto-scss-export-card-cap-color: ;--quarto-scss-export-card-height: ;--quarto-scss-export-card-color: ;--quarto-scss-export-card-bg: #fff;--quarto-scss-export-accordion-color: #343a40;--quarto-scss-export-accordion-bg: #fff;--quarto-scss-export-accordion-border-color: #dee2e6;--quarto-scss-export-accordion-button-color: #343a40;--quarto-scss-export-accordion-button-bg: #fff;--quarto-scss-export-accordion-button-active-bg: #d4e6f9;--quarto-scss-export-accordion-button-active-color: #10335b;--quarto-scss-export-accordion-button-focus-border-color: #93c0f1;--quarto-scss-export-accordion-icon-color: #343a40;--quarto-scss-export-accordion-icon-active-color: #10335b;--quarto-scss-export-tooltip-color: #fff;--quarto-scss-export-tooltip-bg: #000;--quarto-scss-export-tooltip-margin: ;--quarto-scss-export-tooltip-arrow-color: ;--quarto-scss-export-form-feedback-tooltip-line-height: ;--quarto-scss-export-popover-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-popover-header-bg: #e9ecef;--quarto-scss-export-popover-body-color: #343a40;--quarto-scss-export-popover-arrow-color: #fff;--quarto-scss-export-popover-arrow-outer-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-color: ;--quarto-scss-export-toast-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-header-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-toast-header-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-header-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-badge-color: #fff;--quarto-scss-export-modal-content-color: ;--quarto-scss-export-modal-content-bg: #fff;--quarto-scss-export-modal-content-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-modal-backdrop-bg: #000;--quarto-scss-export-modal-header-border-color: #dee2e6;--quarto-scss-export-modal-footer-bg: ;--quarto-scss-export-modal-footer-border-color: #dee2e6;--quarto-scss-export-progress-bg: #e9ecef;--quarto-scss-export-progress-bar-color: #fff;--quarto-scss-export-progress-bar-bg: #2780e3;--quarto-scss-export-list-group-color: #343a40;--quarto-scss-export-list-group-bg: #fff;--quarto-scss-export-list-group-border-color: #dee2e6;--quarto-scss-export-list-group-hover-bg: #f8f9fa;--quarto-scss-export-list-group-active-bg: #2780e3;--quarto-scss-export-list-group-active-color: #fff;--quarto-scss-export-list-group-active-border-color: #2780e3;--quarto-scss-export-list-group-disabled-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-list-group-disabled-bg: #fff;--quarto-scss-export-list-group-action-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-list-group-action-hover-color: #000;--quarto-scss-export-list-group-action-active-color: #343a40;--quarto-scss-export-list-group-action-active-bg: #e9ecef;--quarto-scss-export-thumbnail-bg: #fff;--quarto-scss-export-thumbnail-border-color: #dee2e6;--quarto-scss-export-figure-caption-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-breadcrumb-font-size: ;--quarto-scss-export-breadcrumb-bg: ;--quarto-scss-export-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-breadcrumb-active-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-breadcrumb-border-radius: ;--quarto-scss-export-carousel-control-color: #fff;--quarto-scss-export-carousel-indicator-active-bg: #fff;--quarto-scss-export-carousel-caption-color: #fff;--quarto-scss-export-carousel-dark-indicator-active-bg: #000;--quarto-scss-export-carousel-dark-caption-color: #000;--quarto-scss-export-btn-close-color: #000;--quarto-scss-export-offcanvas-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-offcanvas-bg-color: #fff;--quarto-scss-export-offcanvas-color: #343a40;--quarto-scss-export-offcanvas-backdrop-bg: #000;--quarto-scss-export-code-color-dark: white;--quarto-scss-export-kbd-color: #fff;--quarto-scss-export-kbd-bg: #343a40;--quarto-scss-export-nested-kbd-font-weight: ;--quarto-scss-export-pre-bg: #f8f9fa;--quarto-scss-export-pre-color: #000;--quarto-scss-export-bslib-page-sidebar-title-bg: #f8f9fa;--quarto-scss-export-bslib-page-sidebar-title-color: #000;--quarto-scss-export-bslib-sidebar-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.05);--quarto-scss-export-bslib-sidebar-toggle-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.1);--quarto-scss-export-sidebar-color: #595959;--quarto-scss-export-sidebar-hover-color: rgba(33, 81, 191, 0.8);--quarto-scss-export-sidebar-disabled-color: rgba(89, 89, 89, 0.75);--quarto-scss-export-valuebox-bg-primary: #5397e9;--quarto-scss-export-valuebox-bg-secondary: #343a40;--quarto-scss-export-valuebox-bg-success: #3aa716;--quarto-scss-export-valuebox-bg-info: rgba(153, 84, 187, 0.7019607843);--quarto-scss-export-valuebox-bg-warning: #fa6400;--quarto-scss-export-valuebox-bg-danger: rgba(255, 0, 57, 0.7019607843);--quarto-scss-export-valuebox-bg-light: #f8f9fa;--quarto-scss-export-valuebox-bg-dark: #343a40;--quarto-scss-export-mermaid-bg-color: #fff;--quarto-scss-export-mermaid-edge-color: #343a40;--quarto-scss-export-mermaid-node-fg-color: #343a40;--quarto-scss-export-mermaid-fg-color: #343a40;--quarto-scss-export-mermaid-fg-color--lighter: #4b545c;--quarto-scss-export-mermaid-fg-color--lightest: #626d78;--quarto-scss-export-mermaid-label-bg-color: #fff;--quarto-scss-export-mermaid-label-fg-color: #2780e3;--quarto-scss-export-mermaid-node-bg-color: rgba(39, 128, 227, 0.1);--quarto-scss-export-code-block-border-left-color: #dee2e6;--quarto-scss-export-callout-color-note: #2780e3;--quarto-scss-export-callout-color-tip: #3fb618;--quarto-scss-export-callout-color-important: #ff0039;--quarto-scss-export-callout-color-caution: #f0ad4e;--quarto-scss-export-callout-color-warning: #ff7518} diff --git a/demo/demo/_site/site_libs/bootstrap/bootstrap-icons.css b/demo/demo/_site/site_libs/bootstrap/bootstrap-icons.css new file mode 100644 index 0000000..285e444 --- /dev/null +++ b/demo/demo/_site/site_libs/bootstrap/bootstrap-icons.css @@ -0,0 +1,2078 @@ +/*! + * Bootstrap Icons v1.11.1 (https://icons.getbootstrap.com/) + * Copyright 2019-2023 The Bootstrap Authors + * Licensed under MIT (https://github.com/twbs/icons/blob/main/LICENSE) + */ + +@font-face { + font-display: block; + font-family: "bootstrap-icons"; + src: +url("./bootstrap-icons.woff?2820a3852bdb9a5832199cc61cec4e65") format("woff"); +} + +.bi::before, +[class^="bi-"]::before, +[class*=" bi-"]::before { + display: inline-block; + font-family: bootstrap-icons !important; + font-style: normal; + font-weight: normal !important; + font-variant: normal; + text-transform: none; + line-height: 1; + vertical-align: -.125em; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +.bi-123::before { content: "\f67f"; } +.bi-alarm-fill::before { content: "\f101"; } +.bi-alarm::before { content: "\f102"; } +.bi-align-bottom::before { content: "\f103"; } +.bi-align-center::before { content: "\f104"; } +.bi-align-end::before { content: "\f105"; } +.bi-align-middle::before { content: "\f106"; } +.bi-align-start::before { content: "\f107"; } +.bi-align-top::before { content: "\f108"; } +.bi-alt::before { content: "\f109"; } +.bi-app-indicator::before { content: "\f10a"; } +.bi-app::before { content: "\f10b"; } +.bi-archive-fill::before { content: "\f10c"; } +.bi-archive::before { content: "\f10d"; } +.bi-arrow-90deg-down::before { content: "\f10e"; } +.bi-arrow-90deg-left::before { content: "\f10f"; } +.bi-arrow-90deg-right::before { content: "\f110"; } +.bi-arrow-90deg-up::before { content: "\f111"; } +.bi-arrow-bar-down::before { content: "\f112"; } +.bi-arrow-bar-left::before { content: "\f113"; } +.bi-arrow-bar-right::before { content: "\f114"; } +.bi-arrow-bar-up::before { content: "\f115"; } +.bi-arrow-clockwise::before { content: "\f116"; } +.bi-arrow-counterclockwise::before { content: "\f117"; } +.bi-arrow-down-circle-fill::before { content: "\f118"; } +.bi-arrow-down-circle::before { content: "\f119"; } +.bi-arrow-down-left-circle-fill::before { content: "\f11a"; } +.bi-arrow-down-left-circle::before { content: "\f11b"; } +.bi-arrow-down-left-square-fill::before { content: "\f11c"; } +.bi-arrow-down-left-square::before { content: "\f11d"; } +.bi-arrow-down-left::before { content: "\f11e"; } +.bi-arrow-down-right-circle-fill::before { content: "\f11f"; } 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s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;iNumber.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu 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e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"

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")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get 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e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new 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b/demo/demo/_site/site_libs/quarto-html/quarto-syntax-highlighting-07ba0ad10f5680c660e360ac31d2f3b6.css new file mode 100644 index 0000000..254faed --- /dev/null +++ b/demo/demo/_site/site_libs/quarto-html/quarto-syntax-highlighting-07ba0ad10f5680c660e360ac31d2f3b6.css @@ -0,0 +1,205 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > *, .margin-caption, .aside" + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id="${anchor}"]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + // This is the no-scroll case where last section should be the active one + sectionIndex = 0; + } else { + // This finds the last section visible on screen that should be made active + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + // Categories search with listing only use path without query + const currentPagePath = offsetAbsoluteUrl( + window.location.origin + window.location.pathname + ); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + child.style.pointerEvents = "none"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.pointerEvents = null; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + child.style.pointerEvents = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + // toc-expand: false + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/demo/demo/_site/site_libs/quarto-html/tippy.css b/demo/demo/_site/site_libs/quarto-html/tippy.css new file mode 100644 index 0000000..e6ae635 --- /dev/null +++ b/demo/demo/_site/site_libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 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activateCategory(hash.category); + } + // Paginate a specific listing + const listingIds = Object.keys(window["quarto-listings"]); + for (const listingId of listingIds) { + const page = hash[getListingPageKey(listingId)]; + if (page) { + showPage(listingId, page); + } + } + } + + const listingIds = Object.keys(window["quarto-listings"]); + for (const listingId of listingIds) { + // The actual list + const list = window["quarto-listings"][listingId]; + + // Update the handlers for pagination events + refreshPaginationHandlers(listingId); + + // Render any visible items that need it + renderVisibleProgressiveImages(list); + + // Whenever the list is updated, we also need to + // attach handlers to the new pagination elements + // and refresh any newly visible items. + list.on("updated", function () { + renderVisibleProgressiveImages(list); + setTimeout(() => refreshPaginationHandlers(listingId)); + + // Show or hide the no matching message + toggleNoMatchingMessage(list); + }); + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Attach click handlers to categories + const categoryEls = window.document.querySelectorAll( + ".quarto-listing-category .category" + ); + + for (const categoryEl of categoryEls) { + const category = atob(categoryEl.getAttribute("data-category")); + categoryEl.onclick = () => { + activateCategory(category); + setCategoryHash(category); + }; + } + + // Attach a click handler to the category title + // (there should be only one, but since it is a class name, handle N) + const categoryTitleEls = window.document.querySelectorAll( + ".quarto-listing-category-title" + ); + for (const categoryTitleEl of categoryTitleEls) { + categoryTitleEl.onclick = () => { + activateCategory(""); + setCategoryHash(""); + }; + } + + categoriesLoaded = true; +}); + +function toggleNoMatchingMessage(list) { + const selector = `#${list.listContainer.id} .listing-no-matching`; + const noMatchingEl = window.document.querySelector(selector); + if (noMatchingEl) { + if (list.visibleItems.length === 0) { + noMatchingEl.classList.remove("d-none"); + } else { + if (!noMatchingEl.classList.contains("d-none")) { + noMatchingEl.classList.add("d-none"); + } + } + } +} + +function setCategoryHash(category) { + setHash({ category }); +} + +function setPageHash(listingId, page) { + const currentHash = getHash() || {}; + currentHash[getListingPageKey(listingId)] = page; + setHash(currentHash); +} + +function getListingPageKey(listingId) { + return `${listingId}-page`; +} + +function refreshPaginationHandlers(listingId) { + const listingEl = window.document.getElementById(listingId); + const paginationEls = listingEl.querySelectorAll( + ".pagination li.page-item:not(.disabled) .page.page-link" + ); + for (const paginationEl of paginationEls) { + paginationEl.onclick = (sender) => { + setPageHash(listingId, sender.target.getAttribute("data-i")); + showPage(listingId, sender.target.getAttribute("data-i")); + return false; + }; + } +} + +function renderVisibleProgressiveImages(list) { + // Run through the visible items and render any progressive images + for (const item of list.visibleItems) { + const itemEl = item.elm; + if (itemEl) { + const progressiveImgs = itemEl.querySelectorAll( + `img[${kProgressiveAttr}]` + ); + for (const progressiveImg of progressiveImgs) { + const srcValue = progressiveImg.getAttribute(kProgressiveAttr); + if (srcValue) { + progressiveImg.setAttribute("src", srcValue); + } + progressiveImg.removeAttribute(kProgressiveAttr); + } + } + } +} + +function getHash() { + // Hashes are of the form + // #name:value|name1:value1|name2:value2 + const currentUrl = new URL(window.location); + const hashRaw = currentUrl.hash ? currentUrl.hash.slice(1) : undefined; + return parseHash(hashRaw); +} + +const kAnd = "&"; +const kEquals = "="; + +function parseHash(hash) { + if (!hash) { + return undefined; + } + const hasValuesStrs = hash.split(kAnd); + const hashValues = hasValuesStrs + .map((hashValueStr) => { + const vals = hashValueStr.split(kEquals); + if (vals.length === 2) { + return { name: vals[0], value: vals[1] }; + } else { + return undefined; + } + }) + .filter((value) => { + return value !== undefined; + }); + + const hashObj = {}; + hashValues.forEach((hashValue) => { + hashObj[hashValue.name] = decodeURIComponent(hashValue.value); + }); + return hashObj; +} + +function makeHash(obj) { + return Object.keys(obj) + .map((key) => { + return `${key}${kEquals}${obj[key]}`; + }) + .join(kAnd); +} + +function setHash(obj) { + const hash = makeHash(obj); + window.history.pushState(null, null, `#${hash}`); +} + +function showPage(listingId, page) { + const list = window["quarto-listings"][listingId]; + if (list) { + list.show((page - 1) * list.page + 1, list.page); + } +} + +function activateCategory(category) { + // Deactivate existing categories + const activeEls = window.document.querySelectorAll( + ".quarto-listing-category .category.active" + ); + for (const activeEl of activeEls) { + activeEl.classList.remove("active"); + } + + // Activate this category + const categoryEl = window.document.querySelector( + `.quarto-listing-category .category[data-category='${btoa(category)}']` + ); + if (categoryEl) { + categoryEl.classList.add("active"); + } + + // Filter the listings to this category + filterListingCategory(category); +} + +function filterListingCategory(category) { + const listingIds = Object.keys(window["quarto-listings"]); + for (const listingId of listingIds) { + const list = window["quarto-listings"][listingId]; + if (list) { + if (category === "") { + // resets the filter + list.filter(); + } else { + // filter to this category + list.filter(function (item) { + const itemValues = item.values(); + if (itemValues.categories !== null) { + const categories = atob(itemValues.categories).split(","); + return categories.includes(category); + } else { + return false; + } + }); + } + } + } +} diff --git a/demo/demo/_site/site_libs/quarto-nav/headroom.min.js b/demo/demo/_site/site_libs/quarto-nav/headroom.min.js new file mode 100644 index 0000000..b08f1df --- /dev/null +++ b/demo/demo/_site/site_libs/quarto-nav/headroom.min.js @@ -0,0 +1,7 @@ +/*! + * headroom.js v0.12.0 - Give your page some headroom. 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0000000..38cc430 --- /dev/null +++ b/demo/demo/_site/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,325 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + composed: false, +}); + +const announceDismiss = () => { + const annEl = window.document.getElementById("quarto-announcement"); + if (annEl) { + annEl.remove(); + + const annId = annEl.getAttribute("data-announcement-id"); + window.localStorage.setItem(`quarto-announce-${annId}`, "true"); + } +}; + +const announceRegister = () => { + const annEl = window.document.getElementById("quarto-announcement"); + if (annEl) { + const annId = annEl.getAttribute("data-announcement-id"); + const isDismissed = + window.localStorage.getItem(`quarto-announce-${annId}`) || false; + if (isDismissed) { + announceDismiss(); + return; + } else { + annEl.classList.remove("hidden"); + } + + const actionEl = annEl.querySelector(".quarto-announcement-action"); + if (actionEl) { + actionEl.addEventListener("click", function (e) { + e.preventDefault(); + // Hide the bar immediately + announceDismiss(); + }); + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function () { + let init = false; + + announceRegister(); + + // Manage the back to top button, if one is present. + let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop; + const scrollDownBuffer = 5; + const scrollUpBuffer = 35; + const btn = document.getElementById("quarto-back-to-top"); + const hideBackToTop = () => { + btn.style.display = "none"; + }; + const showBackToTop = () => { + btn.style.display = "inline-block"; + }; + if (btn) { + window.document.addEventListener( + "scroll", + function () { + const currentScrollTop = + window.pageYOffset || document.documentElement.scrollTop; + + // Shows and hides the button 'intelligently' as the user scrolls + if (currentScrollTop - scrollDownBuffer > lastScrollTop) { + hideBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) { + showBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } + + // Show the button at the bottom, hides it at the top + if (currentScrollTop <= 0) { + hideBackToTop(); + } else if ( + window.innerHeight + currentScrollTop >= + document.body.offsetHeight + ) { + showBackToTop(); + } + }, + false + ); + } + + function throttle(func, wait) { + var timeout; + return function () { + const context = this; + const args = arguments; + const later = function () { + clearTimeout(timeout); + timeout = null; + func.apply(context, args); + }; + + if (!timeout) { + timeout = setTimeout(later, wait); + } + }; + } + + function headerOffset() { + // Set an offset if there is are fixed top navbar + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl) { + return headerEl.clientHeight; + } else { + return 0; + } + } + + function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + return 0; + } + } + + function dashboardOffset() { + const dashboardNavEl = window.document.getElementById( + "quarto-dashboard-header" + ); + if (dashboardNavEl !== null) { + return dashboardNavEl.clientHeight; + } else { + return 0; + } + } + + function updateDocumentOffsetWithoutAnimation() { + updateDocumentOffset(false); + } + + function updateDocumentOffset(animated) { + // set body offset + const topOffset = headerOffset(); + const bodyOffset = topOffset + footerOffset() + dashboardOffset(); + const bodyEl = window.document.body; + bodyEl.setAttribute("data-bs-offset", topOffset); + bodyEl.style.paddingTop = topOffset + "px"; + + // deal with sidebar offsets + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + if (!animated) { + sidebar.classList.add("notransition"); + // Remove the no transition class after the animation has time to complete + setTimeout(function () { + sidebar.classList.remove("notransition"); + }, 201); + } + + if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) { + sidebar.style.top = "0"; + sidebar.style.maxHeight = "100vh"; + } else { + sidebar.style.top = topOffset + "px"; + sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)"; + } + }); + + // allow space for footer + const mainContainer = window.document.querySelector(".quarto-container"); + if (mainContainer) { + mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)"; + } + + // link offset + let linkStyle = window.document.querySelector("#quarto-target-style"); + if (!linkStyle) { + linkStyle = window.document.createElement("style"); + linkStyle.setAttribute("id", "quarto-target-style"); + window.document.head.appendChild(linkStyle); + } + while (linkStyle.firstChild) { + linkStyle.removeChild(linkStyle.firstChild); + } + if (topOffset > 0) { + linkStyle.appendChild( + window.document.createTextNode(` + section:target::before { + content: ""; + display: block; + height: ${topOffset}px; + margin: -${topOffset}px 0 0; + }`) + ); + } + if (init) { + window.dispatchEvent(headroomChanged); + } + init = true; + } + + // initialize headroom + var header = window.document.querySelector("#quarto-header"); + if (header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); + headroom.init(); + + let frozen = false; + window.quartoToggleHeadroom = function () { + if (frozen) { + headroom.unfreeze(); + frozen = false; + } else { + headroom.freeze(); + frozen = true; + } + }; + } + + window.addEventListener( + "hashchange", + function (e) { + if ( + getComputedStyle(document.documentElement).scrollBehavior !== "smooth" + ) { + window.scrollTo(0, window.pageYOffset - headerOffset()); + } + }, + false + ); + + // Observe size changed for the header + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl && window.ResizeObserver) { + const observer = new window.ResizeObserver(() => { + setTimeout(updateDocumentOffsetWithoutAnimation, 0); + }); + observer.observe(headerEl, { + attributes: true, + childList: true, + characterData: true, + }); + } else { + window.addEventListener( + "resize", + throttle(updateDocumentOffsetWithoutAnimation, 50) + ); + } + setTimeout(updateDocumentOffsetWithoutAnimation, 250); + + // fixup index.html links if we aren't on the filesystem + if (window.location.protocol !== "file:") { + const links = window.document.querySelectorAll("a"); + for (let i = 0; i < links.length; i++) { + if (links[i].href) { + links[i].dataset.originalHref = links[i].href; + links[i].href = links[i].href.replace(/\/index\.html/, "/"); + } + } + + // Fixup any sharing links that require urls + // Append url to any sharing urls + const sharingLinks = window.document.querySelectorAll( + "a.sidebar-tools-main-item, a.quarto-navigation-tool, a.quarto-navbar-tools, a.quarto-navbar-tools-item" + ); + for (let i = 0; i < sharingLinks.length; i++) { + const sharingLink = sharingLinks[i]; + const href = sharingLink.getAttribute("href"); + if (href) { + sharingLink.setAttribute( + "href", + href.replace("|url|", window.location.href) + ); + } + } + + // Scroll the active navigation item into view, if necessary + const navSidebar = window.document.querySelector("nav#quarto-sidebar"); + if (navSidebar) { + // Find the active item + const activeItem = navSidebar.querySelector("li.sidebar-item a.active"); + if (activeItem) { + // Wait for the scroll height and height to resolve by observing size changes on the + // nav element that is scrollable + const resizeObserver = new ResizeObserver((_entries) => { + // The bottom of the element + const elBottom = activeItem.offsetTop; + const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight; + + // The element height and scroll height are the same, then we are still loading + if (viewBottom !== navSidebar.scrollHeight) { + // Determine if the item isn't visible and scroll to it + if (elBottom >= viewBottom) { + navSidebar.scrollTop = elBottom; + } + + // stop observing now since we've completed the scroll + resizeObserver.unobserve(navSidebar); + } + }); + resizeObserver.observe(navSidebar); + } + } + } +}); diff --git a/demo/demo/_site/site_libs/quarto-search/autocomplete.umd.js b/demo/demo/_site/site_libs/quarto-search/autocomplete.umd.js new file 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Apache Software License 2.0 + * + * http://www.apache.org/licenses/LICENSE-2.0 + */ +var e,t;e=this,t=function(){"use strict";function e(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function t(t){for(var n=1;ne.length)&&(t=e.length);for(var n=0,r=new Array(t);n0&&void 0!==arguments[0]?arguments[0]:1,t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:3,n=new Map,r=Math.pow(10,t);return{get:function(t){var i=t.match(C).length;if(n.has(i))return n.get(i);var o=1/Math.pow(i,.5*e),c=parseFloat(Math.round(o*r)/r);return n.set(i,c),c},clear:function(){n.clear()}}}var $=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},n=t.getFn,i=void 0===n?I.getFn:n,o=t.fieldNormWeight,c=void 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a/demo/demo/_site/site_libs/quarto-search/quarto-search.js b/demo/demo/_site/site_libs/quarto-search/quarto-search.js new file mode 100644 index 0000000..d788a95 --- /dev/null +++ b/demo/demo/_site/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1290 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const currentUrl = new URL(window.location); + const query = currentUrl.searchParams.get(kQueryArg); + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query && mainEl) { + // perform any highlighting + highlight(escapeRegExp(query), mainEl); + + // fix up the URL to remove the q query param + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Clear search highlighting when the user scrolls sufficiently + const resetFn = () => { + resetHighlighting(""); + window.removeEventListener("quarto-hrChanged", resetFn); + window.removeEventListener("quarto-sectionChanged", resetFn); + }; + + // Register this event after the initial scrolling and settling of events + // on the page + window.addEventListener("quarto-hrChanged", resetFn); + window.addEventListener("quarto-sectionChanged", resetFn); + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen, setQuery, setCollections } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + openOnFocus: true, + plugins, + classNames: { + form: "d-flex", + }, + placeholder: language["search-text-placeholder"], + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // If this is a file URL, note that + + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + if (query === null || query === "") { + return []; + } + + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + const hasQuery = lastState.query; + + return createElement( + "div", + { + class: `quarto-search-no-results${ + hasQuery ? "" : " no-query" + }`, + }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions + ); + }, + }, + }, + ]; + }, + }); + + window.quartoOpenSearch = () => { + setIsOpen(false); + setIsOpen(true); + focusSearchInput(); + }; + + document.addEventListener("keyup", (event) => { + const { key } = event; + const kbds = quartoSearchOptions["keyboard-shortcut"]; + const focusedEl = document.activeElement; + + const isFormElFocused = [ + "input", + "select", + "textarea", + "button", + "option", + ].find((tag) => { + return focusedEl.tagName.toLowerCase() === tag; + }); + + if ( + kbds && + kbds.includes(key) && + !isFormElFocused && + !document.activeElement.isContentEditable + ) { + event.preventDefault(); + window.quartoOpenSearch(); + } + }); + + // Remove the labeleledby attribute since it is pointing + // to a non-existent label + if (quartoSearchOptions.type === "overlay") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; + } + + // If the main document scrolls dismiss the search results + // (otherwise, since they're floating in the document they can scroll with the document) + window.document.body.onscroll = throttle(() => { + // Only do this if we're not detached + // Bug #7117 + // This will happen when the keyboard is shown on ios (resulting in a scroll) + // which then closed the search UI + if (!window.matchMedia(detachedMediaQuery).matches) { + setIsOpen(false); + } + }, 50); + + if (showSearchResults) { + setIsOpen(true); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + window.aa("init", { + appId, + apiKey, + useCookie: true, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + const events = insightsEvents.flatMap((event) => { + // This API limits the number of items per event to 20 + const chunkSize = 20; + const itemChunks = []; + const eventItems = event.items; + for (let i = 0; i < eventItems.length; i += chunkSize) { + itemChunks.push(eventItems.slice(i, i + chunkSize)); + } + // Split the items into multiple events that can be sent + const events = itemChunks.map((items) => { + return { + ...event, + items, + }; + }); + return events; + }); + + for (const event of events) { + insights.viewedObjectIDs(event); + } + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* Search Index Handling */ +// create the index +var fuseIndex = undefined; +var shownWarning = false; + +// fuse index options +const kFuseIndexOptions = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, +}; + +async function readSearchData() { + // Initialize the search index on demand + if (fuseIndex === undefined) { + if (window.location.protocol === "file:" && !shownWarning) { + window.alert( + "Search requires JavaScript features disabled when running in file://... URLs. In order to use search, please run this document in a web server." + ); + shownWarning = true; + return; + } + const fuse = new window.Fuse([], kFuseIndexOptions); + + // fetch the main search.json + const response = await fetch(offsetURL("search.json")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href, + item.crumbs, + quartoSearchOptions + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +function createDocumentCard( + createElement, + icon, + title, + section, + text, + href, + crumbs, + quartoSearchOptions +) { + const iconEl = createElement("i", { + class: `bi bi-${icon} search-result-icon`, + }); + const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContents = [iconEl, titleEl]; + const showParent = quartoSearchOptions["show-item-context"]; + if (crumbs && showParent) { + let crumbsOut = undefined; + const crumbClz = ["search-result-crumbs"]; + if (showParent === "root") { + crumbsOut = crumbs.length > 1 ? crumbs[0] : undefined; + } else if (showParent === "parent") { + crumbsOut = crumbs.length > 1 ? crumbs[crumbs.length - 2] : undefined; + } else { + crumbsOut = crumbs.length > 1 ? crumbs.join(" > ") : undefined; + crumbClz.push("search-result-crumbs-wrap"); + } + + const crumbEl = createElement( + "p", + { class: crumbClz.join(" ") }, + crumbsOut + ); + titleContents.push(crumbEl); + } + + const titleContainerEl = createElement( + "div", + { class: "search-result-title-container" }, + titleContents + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? 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In our case this + // means that long search terms mixed with large index gets very slow + // + // This injects a subIndex that will be used once the terms get long enough + // Usually making this subindex is cheap since there will typically be + // a subset of results matching the existing query + if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) { + // Use the existing subSearchFuse + index = subSearchFuse; + } else if (subSearchFuse !== undefined) { + // The term changed, discard the existing fuse + subSearchFuse = undefined; + subSearchTerm = undefined; + } + + // Search using the active fuse + const then = performance.now(); + const resultsRaw = await index.search(query, fuseOptions); + const now = performance.now(); + + const results = resultsRaw.map((result) => { + const addParam = (url, name, value) => { + const anchorParts = url.split("#"); + const baseUrl = anchorParts[0]; + const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; + anchorParts[0] = baseUrl + sep + name + "=" + value; + return anchorParts.join("#"); + }; + + return { + title: result.item.title, + section: result.item.section, + href: addParam(result.item.href, kQueryArg, query), + text: highlightMatch(query, result.item.text), + crumbs: result.item.crumbs, + }; + }); + + // If we don't have a subfuse and the query is long enough, go ahead + // and create a subfuse to use for subsequent queries + if ( + now - then > kFuseMaxWait && + subSearchFuse === undefined && + resultsRaw.length < fuseOptions.limit + ) { + subSearchTerm = query; + subSearchFuse = new window.Fuse([], kFuseIndexOptions); + resultsRaw.forEach((rr) => { + subSearchFuse.add(rr.item); + }); + } + return results; +} diff --git a/demo/demo/_site/styles.css b/demo/demo/_site/styles.css new file mode 100644 index 0000000..2ddf50c --- /dev/null +++ b/demo/demo/_site/styles.css @@ -0,0 +1 @@ +/* css styles */ diff --git a/demo/imfp_demo.qmd b/demo/demo/imfp_demo.qmd similarity index 75% rename from demo/imfp_demo.qmd rename to demo/demo/imfp_demo.qmd index 5c5d460..e27d613 100644 --- a/demo/imfp_demo.qmd +++ b/demo/demo/imfp_demo.qmd @@ -1,11 +1,9 @@ --- -title: "imfp demo" +title: "imfp Demo Project" --- This data analysis project aims to explore the relationship between economic growth and gender equality using `imfp`, which allows us to download data from IMF (International Monetary Fund). `imfp` can be integrated with other python tools to streamline the computational process. To demonstrate its functionality, the project experimented with a variety of visualization and analysis methods. -Ready for some insights about if a stronger economy mean more equal opportunities for all genders? Let’s dive in! - ## Executive Summary In this project, we explored the following: @@ -33,17 +31,10 @@ In this project, we explored the following: * Time Series Analysis ## Suggested packages -`pandas`: view and manipulate data frame; -`matplotlib.pyplot`: make plots; -`seaborn`: make plots; -`numpy`: computation; -`LinearRegression`: implement linear regression; -`tabulate`: format data into tables; +The integration of other Python tools not only streamlined our computational processes but also ensured consistency across the project. A custom module is written to simplify the process of making API calls and fetching information with imfp library. `load_or_fetch_databases`, `load_or_fetch_parameters` `load_or_fetch_dataset` load and retreive database, parameters, and dataset from a local or remote source. `view_dataframe_in_browser` displays dataframe in a web browser. -`statsmodels.api`, `adfuller`, `ARIMA`,`VAR`,`plot_acf`,`plot_pacf`,`mean_absolute_error`,`mean_squared_error`, and`grangercausalitytests` are specifically used for time series analysis. - ```{python} import os import pickle @@ -132,6 +123,22 @@ def load_or_fetch_dataset(database_id, indicator): ``` +Here is a brief introduction about the packages used: + +`pandas`: view and manipulate data frame + +`matplotlib.pyplot`: make plots + +`seaborn`: make plots + +`numpy`: computation + +`LinearRegression`: implement linear regression + +`tabulate`: format data into tables + +`statsmodels.api`, `adfuller`, `ARIMA`,`VAR`,`plot_acf`,`plot_pacf`,`mean_absolute_error`,`mean_squared_error`, and`grangercausalitytests` are specifically used for time series analysis. + ```{python} import pandas as pd import matplotlib.pyplot as plt @@ -150,7 +157,7 @@ from statsmodels.tsa.stattools import grangercausalitytests ``` ## Data Fetching -In this section, we extracted four datasets through API calls: Gender Inequality Index(GII), GDP Deflator, Nominal GDP, and Population. Two databases were used: Gender Equality and International Financial Statistics (IFS). +In this section, we extracted four datasets through API calls: Gender Inequality Index(GII), GDP Deflator, Nominal GDP, and Population. ```{python} from pathlib import Path @@ -169,6 +176,7 @@ databases[ # view_dataframe_in_browser(databases) ``` +Two databases were used: Gender Equality and International Financial Statistics (IFS). ```{python} datasets = ["GENDER_EQUALITY", "IFS"] @@ -184,6 +192,9 @@ for dataset in datasets: # view_dataframe_in_browser(params.get("IFS").get("indicator")) ``` +“freq” stands for Frequency, such as Annual, Monthly, or Quarterly. "ref_area" stands for Geogrpahical Area, such as US (United States), JP (Japan), and GB (United Kindom). “indicator" refers to the specific dataset in the database, including input code and description. For example, if we display all the indicators for IFS database, the GDP deflator dataset has an input code of "NGDP_D_SA_IX" with a full name description of Gross Domestic Product, Deflator, Seasonally Adjusted, Index. + +We paired the database with the specific dataset indicator to read and store the csv file. ```{python} datasets = {} @@ -216,9 +227,7 @@ GDP_population_data = datasets[GDP_population] ## Feature Engineering ### Data Cleaning -Since the GDP deflator is reported on a quarterly basis, we need to convert it to a yearly basis. - - +Since the GDP deflator was reported on a quarterly basis, we converted it to a yearly basis. ```{python} # Filter to keep only rows with a partial string match for "Q4" in the time_period column GDP_deflator_data = GDP_deflator_data[GDP_deflator_data['time_period'].str.contains("Q4")] @@ -228,10 +237,9 @@ GDP_deflator_data = GDP_deflator_data[GDP_deflator_data['time_period'].str.conta ```{python} # Split the time_period into year and quarter and keep the year only GDP_deflator_data.loc[:, 'time_period'] = GDP_deflator_data['time_period'].str[0:4] - ``` - +We made all the variables numeric. ```{python} datasets = [GII_data, GDP_deflator_data, GDP_nominal_data, GDP_population_data] @@ -242,6 +250,9 @@ for i, dataset in enumerate(datasets): datasets[i].loc[:, 'unit_mult'] = pd.to_numeric(datasets[i]['unit_mult'], errors='coerce') ``` +### GII Percent Change: Dependent Variable +We kept percents as decimals to make them easy to work with for calculation. Different countries have different baseline level of economic growth and gender equality. We calculated the percent change to make them comparable. + ```{python} # Calculate percent change for each ref_area GII_data[f'pct_change'] = GII_data.sort_values(['ref_area', 'time_period']).groupby('ref_area')['obs_value'].pct_change() @@ -250,6 +261,7 @@ GII_data[f'pct_change'] = GII_data.sort_values(['ref_area', 'time_period']).grou GII_data.head() ``` +We subset the data frame to keep only the columns we want: ```{python} # Create a new dataframe with only the required columns GII_data = GII_data[['ref_area', 'time_period', 'pct_change']].copy() @@ -259,6 +271,8 @@ GII_data.head() ``` +### GDP: Independent Variable +We kept the columns we want only for GDP-related datasets for easier table merging. ```{python} # GDP Deflator Dataset @@ -302,6 +316,7 @@ merged_df = pd.merge(pd.merge(GDP_deflator_data,GDP_nominal_data, on=['time_peri merged_df.head() ``` +We want to adjust GDP data based on unit multiplier. Unit multiplier stands for the number of zeroes we need to add to the value column. For example, in 1950, the observed population data for country GA (Georgia) was 473.296. With a unit muliplier of 3, the adjusted population would be 473296. ```{python} merged_df['adjusted_index'] = merged_df['obs_value_index'] * (10 ** (merged_df['unit_mult_index'])) @@ -319,7 +334,7 @@ merged_df = merged_df[['ref_area', 'time_period','adjusted_nominal', 'adjusted_i merged_df.head() ``` - +We wanted to compute the Real GDP per capita. ```{python} # Step 1: Real GDP = (Nominal GDP / GDP Deflator Index) merged_df['Real_GDP_domestic'] = (merged_df['adjusted_nominal'] / merged_df['adjusted_index']) @@ -331,6 +346,7 @@ merged_df['Real_GDP_per_capita'] = merged_df['Real_GDP_domestic'] / merged_df['a merged_df.head() ``` +We calculated the percentage change in Real GDP per capita and put it in a new column. ```{python} # Calculate percent change for each ref_area @@ -372,9 +388,9 @@ combined_data.head() ## Data Visualization ### Scatterplot -Scatterplot use dots to represent values of two numeric variables. The horizontal axis is the percent change in Real GDP per capita. The vertical axis is the percent change in Gender Inequality Index(GII). Different colors represent different countries. We used a linear regression line to display the overall pattern. +Scatterplot use dots to represent values of two numeric variables. The horizontal axis was the percent change in Real GDP per capita. The vertical axis was the percent change in Gender Inequality Index(GII). Different colors represented different countries. We used a linear regression line to display the overall pattern. -Based on the scatterplot, it seems like there is a slight positive relationship between GDP change and GII change as shown by the flat regression line. Gender inequality is decreasing (gender equality is improving) a little faster in country-years with low GDP growth and a little slower in country-years with high GDP growth. +Based on the scatterplot, it seemed like there was a slight positive relationship between GDP change and GII change as shown by the flat regression line. Gender inequality was decreasing (gender equality was improving) a little faster in country-years with low GDP growth and a little slower in country-years with high GDP growth. ```{python} # Drop NAs @@ -407,15 +423,19 @@ plt.show() ``` ### Time Series Line Plot -Create separate line plots for GDP change and GII change over time for a few key countries might show the trends more clearly. +We created separate line plots for GDP change and GII change over time for a few key countries might show the trends more clearly. US: United States + JP: Japan + GB: United Kindom + FR: France + MX: Mexico -Based on the line plots, we see GDP change and GII change have different patterns. For example, in Mexico, when there is a big change in real GDP per captia in 1995, the change in GII is pretty stable. +Based on the line plots, we saw GDP change and GII change have different patterns. For example, in Mexico, when there was a big change in real GDP per captia in 1995, the change in GII was pretty stable. ```{python} # Time Series Line plot for a few key countries selected_countries = ['US', 'JP', 'GB', 'FR', 'MX'] @@ -440,9 +460,9 @@ plt.show() ``` ### Barplot -Use a barplot to show average changes in GII and GDP percent change for each country to visualize regions where inequality is improving or worsening. +We used a barplot to show average changes in GII and GDP percent change for each country to visualize regions where inequality was improving or worsening. -This plot supports our previous observation how GII change seems to be not be correlated with GDP change. We also saw that, for country SI, Solvenia, there seems to be a large improvement in gender inequality. +This plot supported our previous observation how GII change seemed to be not be correlated with GDP change. We also saw that, for country SI, Solvenia, there seems to be a large improvement in gender inequality. ```{python} # Barplot using average GII and GDP change @@ -464,9 +484,9 @@ plt.show() ``` ### Boxplot -Use boxplot to visualize the distribution of GDP and GII change by country, providing information about spread, median, and potential outliers. To provide a more informative view, we sequence countries in an ascending order by the median of percent change in GDP. +We used boxplot to visualize the distribution of GDP and GII change by country, providing information about spread, median, and potential outliers. To provide a more informative view, we sequenced countries in an ascending order by the median of percent change in GDP. -The boxplot displays a slight upward trend with no obvious pattern between GDP and GII change. In coutries with higher GDP change median, they also tend to have a larger spread of the GDP change. The median of GII change remains stable regardless of the magnitude of GDP change, implying weak or no association between GDP and GII change. We observe a potential outlier for country SI, Solvenia, which may explains its large improvement in Gender inequality. +The boxplot displayed a slight upward trend with no obvious pattern between GDP and GII change. In coutries with higher GDP change median, they also tend to have a larger spread of the GDP change. The median of GII change remained stable regardless of the magnitude of GDP change, implying weak or no association between GDP and GII change. We observed a potential outlier for country SI, Solvenia, which may explained its large improvement in Gender inequality. ```{python} # Box plot for GII and GDP change @@ -491,11 +511,11 @@ plt.show() ``` ### Correlation Matrix -Create a heatmap to show the relationship between GII and GDP change. +We created a heatmap to show the relationship between GII and GDP change. A positive correlation coefficient indicates a positive relationship: the larger the GDP change, the larger the GII change. A negative correlation coefficient indicates a negative relationship: the larger the GDP change, the smaller the GII change. A correlation coefficient closer to 0 indicates there is weak or no relationship. -Based on the numeric values in the plot, there is a moderately strong positive correlation between GII and GDP change for country Estonia(EE) and Ireland(IE). +Based on the numeric values in the plot, there was a moderately strong positive correlation between GII and GDP change for country Estonia(EE) and Ireland(IE). ```{python} # Calculate the correlation @@ -527,7 +547,7 @@ plt.show() ## Statistical Analysis ### Descriptive Statistics -There is a total of 915 data points. The mean of the GII change in -0.0314868, which indicates the overall grand mean percent change in gender inequality index is -3.15%. The mean of the GDP change is 0.0234633, showing the overall grand mean percent change in real GDP per capita is 2.35%. +There was a total of 915 data points. The mean of the GII change in -0.0314868, which indicated the overall grand mean percent change in gender inequality index is -3.15%. The mean of the GDP change was 0.0234633, showing the overall grand mean percent change in real GDP per capita was 2.35%. ```{python} # Generate summary statistics descriptive_stats = combined_data.describe() @@ -537,12 +557,13 @@ print(tabulate(descriptive_stats, headers='keys', tablefmt='github')) ### Regression Analysis Simple linear regression as a foundational approach provide us with a basic understanding of the relationship between GDP change and GII change. -Based on the summary, we can conclude the following: -* Becasue p-value = 0.057, if we set alpha, the significance level, to be 0.05, we fail to reject the null hypothesis and conclude there is no significant relationship between percent change in real GDP per capita and gender inequality index. +Based on the summary, we concluded the following: -* R-squared = 0.004. Only 0.4% of the variance in GII change can be explained by GDP change. +* Becasue p-value = 0.057, if we set alpha, the significance level, to be 0.05, we failed to reject the null hypothesis and conclude there was no significant relationship between percent change in real GDP per capita and gender inequality index. -* We are 95% confident that the interval from -0.003 to 0.169 captures the true slope of GDP change. Because 0 is included, we are uncertain about the effect of GDP change on GII chnage. +* R-squared = 0.004. Only 0.4% of the variance in GII change could be explained by GDP change. + +* We were 95% confident that the interval from -0.003 to 0.169 captured the true slope of GDP change. Because 0 was included, we are uncertain about the effect of GDP change on GII chnage. ```{python} # Define independent and depenent variables @@ -560,21 +581,21 @@ print(model.summary()) ### Time Series Analysis Time series analysis allows us to explore how the relationship between GII and GDP change vary across different time periods, accounting for lagged effects. -Here is a quick summary of the result: -* Both GII and GDP change time series are stationary. +Here was a quick summary of the result: +* Both GII and GDP change time series were stationary. -* Past GII change values significantly influence cuurent GII change values. +* Past GII change values significantly influenced cuurent GII change values. -* VAR model has good model performance on forecasting future values based on historical data. +* VAR model had good model performance on forecasting future values based on historical data. -* Changes in GDP does not cause/precde the changes in GII. +* Changes in GDP did not cause/precde the changes in GII. #### ADF Test: Stationality Assumption Check -We want to use Augmented Dickey-Fuller (ADF) test to check whether a time series is stationary, which is the model assumption for many time series models. +We wanted to use Augmented Dickey-Fuller (ADF) test to check whether a time series was stationary, which was the model assumption for many time series models. -Stationarity implies constant mean and variance over time, making it more predictable and stable for forecasting. +Stationarity implied constant mean and variance over time, making it more predictable and stable for forecasting. -Based on the ADF test output, both GII and GDP change time series are stationary. We can proceed to the time series modeling section. +Based on the ADF test output, both GII and GDP change time series were stationary. We proceeded to the time series modeling section. ```{python} # Augmented Dickey-Fuller (ADF) test for stationarity check # Create melted datasets @@ -599,13 +620,17 @@ adf_test(GDP['Value']) ``` #### VAR model: Examine variables separately -We fit a VAR (Vector Autoreression) model to see the relationship between GII and GDP change. VAR is particularly useful when dealing with multivariate time series data and allows us to examine the interdependence between variables. +We fitted a VAR (Vector Autoreression) model to see the relationship between GII and GDP change. VAR is particularly useful when dealing with multivariate time series data and allows us to examine the interdependence between variables. + +Based on summary, here were several interpretations we could make: + +* We used AIC as the criteria for model selection. Lower value suggests a better fit. + +* Given that we wanted to predict GII change, we focused on the first set "Results for equation GII_change." + +* Past GII_change values significantly influenced current GII_change, as shown in the small p-values of lags 1 and 2. -Based on summary, here are several interpretations we can make: -- We used AIC as the criteria for model selection. Lower value suggests a better fit. -- Given that we want to predict GII change, we will focus on the first set "Results for equation GII_change." -- Past GII_change values significantly influence current GII_change, as shown in the small p-values of lags 1 and 2. -- Lag 2 of GDP_change has a relatively low p-value but is not statistically significant. +* Lag 2 of GDP_change had a relatively low p-value but is not statistically significant. ```{python} # Split the dataset into training and testing sets @@ -632,7 +657,7 @@ print(time_model_fitted.summary()) ``` #### VAR Model: Forecasting -We apply the model learned above to the test data. Based on the plot, the forecast values seem to follow the actual data well, indicating a good model fit caputuring the underlying trends. +We applied the model learned above to the test data. Based on the plot, the forecast values seem to follow the actual data well, indicating a good model fit caputuring the underlying trends. ```{python} # Number of steps to forecast (length of the test set) n_steps = len(test_data) @@ -699,12 +724,6 @@ test_result = grangercausalitytests(train_data[['GII_change', 'GDP_change']], ma ``` ## Conclusion -In wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research. +In wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research. Check out the blog for full analysis! -## Future Directions -* Additional Predictors: Gender Inequality Index (GII) is a composite measure shaped by a mariad of social factors. What about culture, policy, and environment? -* Specific Country: Are there any specific historical contexts that sparked economic growth and improvement in gender equality? -* Opposite causation: Women entering workforce boost GDP? -* Predictive Power of VAR model: Our model performed well in forecasting trends for both variables independently. -Let's keep our curiosity alive and our questions flowing! diff --git a/demo/demo/listing.qmd b/demo/demo/listing.qmd new file mode 100644 index 0000000..7373b15 --- /dev/null +++ b/demo/demo/listing.qmd @@ -0,0 +1,14 @@ +--- +title: "Blog" +listing: + contents: posts + sort: "date desc" + type: default + categories: true + sort-ui: false + filter-ui: false +page-layout: full +title-block-banner: true +--- + + diff --git a/demo/demo/posts/_metadata.yml b/demo/demo/posts/_metadata.yml new file mode 100644 index 0000000..3e9dd01 --- /dev/null +++ b/demo/demo/posts/_metadata.yml @@ -0,0 +1,8 @@ +# options specified here will apply to all posts in this folder + +# freeze computational output +# (see https://quarto.org/docs/projects/code-execution.html#freeze) +freeze: true + +# Enable banner style title blocks +title-block-banner: true diff --git a/demo/demo/posts/posts/Blog.qmd b/demo/demo/posts/posts/Blog.qmd new file mode 100644 index 0000000..5448b44 --- /dev/null +++ b/demo/demo/posts/posts/Blog.qmd @@ -0,0 +1,106 @@ +--- +title: "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data" +description: Apply imfp in a real world data-analysis project. +author: "Jenny Xu" +date: "2024-12-25" +categories: [Data Analysis] +image: "Pictures/pic.png" +--- + +## Introduction + +Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders? + +Our main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables. + +Ready for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in! + +## Data Preparation +​​To explore how economic growth connects with gender equality, we turned to the Imfp library to access a wealth of global economic data, ready to analyze through some straightforward API calls. Let’s break down our two main variables of interest. Here’s the lowdown on the 4 key data series we pulled together: + +![](Pictures/Table 1.png) + +### Dependent Variable: Percent Change in Gender Inequality Index (GII) +Our gender equality measure, the Gender Inequality Index (GII), is a composite score that reflects inequality in three key areas: reproductive health, empowerment, and labor market, ranging from 0 (equality) to 1 (inequality). We calculated the percent change in GII over time to make it easier to compare GII change across countries with different baseline levels of inequality. + +### Independent Variable: Percent Change in Real GDP per capita +Our economic growth measure is real GDP per capita, an indicator of a country’s economic output per person, adjusted for inflation. The higher the real GDP per capita, the wealthier and more prosperous a country is. Here is a peek at the formula: + +![](Pictures/Formula.png) + +## Data Visualization +### Scatterplot + +Nothing like a good scatterplot to help us understand the relationship between two numbers, right? For our project, the scatterplot lets us visually explore how Percent Change in Real GDP per capita relates to Percent Change in GII. Each dot on the plot represents a country-year combo. The gentle upward slope suggests that in years of low GDP growth, gender inequality tends to decrease a bit faster, while in years of high GDP growth, the inequality drops at a slightly slower rate. It’s a subtle clue, but slower-growing economies might be making slightly quicker strides in gender equality. + +![](Pictures/Scatterplot copy.png) +![](Pictures/Scatterplot title.png) + + +### Time Series Line plot + + +The time series line plot gives us the big picture of GDP and GII changes over time across a few select countries, including the United States, Japan, the United Kingdom, France, and Mexico. Mexico, for example, saw great GDP swings in 1995, yet its GII stayed relatively stable. Meanwhile, Japan and France saw some dramatic GII shifts. These fluctuations leave us wondering: what historical events sparked these changes? We may need to take a deeper look into each country’s unique timeline to find the answers. + +![](Pictures/Time.png) +![](Pictures/Time%20title.png) + +### Boxplot + + +Next, we have the boxplot to help us visualize the distribution. By sequencing countries in ascending order of median GDP change, we see an interesting trend: countries with higher median GDP growth also seem to show more variability in their GDP changes. The GII change, on the other hand, keeps a relatively stable median, hinting at a weaker association with GDP growth. + +![](Pictures/boxplot.png) +![](Pictures/box%20title.png) + +### Heatmap + + +Finally, our heatmap wraps it all up with a quick, intuitive look at correlations between GDP and GII across countries. In countries like Estonia (EE) and Ireland (IE), Australia (AT), South Africa (ZA), Thailand (TH), Slovenia (SI), we see positive correlations in warm colors, indicating that as GDP growth rates increase, GII growth rates tend to rise as well. For countries like Cyprus (CY), Romania (RO), and the United States (US), we see cooler colors, representing negative correlations: as GDP growth rate rises, GII growth rates decrease, suggesting an improvement in gender equality alongside economic growth. This color-coded comparison leaves us curious—could the distinction between developed and developing countries play a role in these relationships? It’s a question that might inspire future research directions! + +![](Pictures/heatmap.png) +![](Pictures/heatmap%20title.png) + +## Statistical Analysis +### Descriptive Statistics +Let’s start with a quick overview of our descriptive statistics: our dataset includes 915 data points. On average, GII change is -3.15%, indicating an overall reduction in gender inequality over time. Meanwhile, GDP change averages 2.35%, hinting at modest economic growth. A full breakdown of these values can be found in the table below for more detail. + +![](Pictures/Table%202.png) + +### Regression Analysis +To get a sense of any relationship between GDP change and GII change, we ran a simple linear regression. Here’s what we found: + +* P-value: With a p-value of 0.057, we fail to reject the null hypothesis. There is no statistically significant relationship. In other words, we don't find strong evidence of a link between GDP and GII changes. + +* R-squared: GDP change explains only 0.4% of the variation in GII change—a pretty minimal effect. + +* 95% Confidence Interval: We’re 95% confident the interval from -0.003 to 0.169 captures the true slope. The effect of GDP change on GII is uncertain as 0 is within this range. + +### Time Series Analysis +Time series analysis allows us to dig deeper into how GII and GDP changes play out over time—and potentially influence one another. Here’s the rundown: + +* Stationarity check: Both GII and GDP change series are stationary, meaning their means and variances are stable over time, setting us up for reliable forecasting. + +* VAR Model (Vector Autoregression): This multivariate model lets us explore how past values in both series relate to current values. Notably, past GII changes significantly impact current GII changes. + +* Model Performance: The VAR model has good fit, as shown by the forecast closely tracking actual values. Low MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) confirm that our model predicts GII and GDP change with minimal error. Check out the plots and table below! + +* Granger Causality Test: Curious whether GDP changes can predict changes in GII? The Granger causality test says no. With p-values above 0.05, we fail to find evidence that GDP changes drive GII changes. This suggests that while both variables evolve over time, they don’t seem to directly influence each other. + +![](Pictures/Table%203.png) +![](Pictures/Forecast.png) + +## Conclusion +In wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research. + +In terms of the Imfp library, we gained valuable experience working with real-world economic indicators like GDP and the GII. The library’s function in fetching databases, parameters, and datasets made it straightforward to access IMF’s data, allowing us to focus on analysis instead. The integration of other Python tools—such as seaborn, matplotlib, and numpy—not only streamlined our computational processes but also ensured consistency across the project. + +## Future Directions +First, we must consider what other factors might influence the relationship between GDP and GII change. The GII is a composite index, shaped by a myriad of social factors, including cultural norms, legal frameworks, and environmental shifts. Future studies could benefit from incorporating additional predictors into the analysis and exploring the interaction between economic growth and gender equality within specific country contexts. + +Second, there's potential to enhance the predictive power of our Vector Autoregression (VAR) time series model. While we established that GDP change does not cause GII change, our model performed well in forecasting trends for both variables independently. In practice, policymakers may want to forecast GII trends independently of GDP if they are implementing gender-focused policies. Future research could investigate time series modeling to further unravel the dynamics of GII and GDP changes. + +So, as we wrap up this chapter, let's keep our curiosity alive and our questions flowing. 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From: "Christopher C. Smith" Date: Fri, 27 Dec 2024 17:56:08 -0500 Subject: [PATCH 4/4] Cleaned up demo, updated NEWS, bumped to 1.1.4 --- .github/workflows/dev.yml | 52 +- .gitignore | 9 +- News | 8 + _quarto.yml | 12 +- demo/.DS_Store | Bin 6148 -> 0 bytes demo/demo/.gitignore | 1 - demo/demo/About.qmd | 19 - demo/demo/_quarto.yml | 21 - demo/demo/_site/About.html | 564 ---- demo/demo/_site/About/About.html | 564 ---- demo/demo/_site/About/Headshot.jpg | Bin 585650 -> 0 bytes demo/demo/_site/Blog/blog/Blog.html | 672 ----- .../_site/Blog/blog/Pictures/Forecast.png | Bin 205047 -> 0 bytes .../demo/_site/Blog/blog/Pictures/Formula.png | Bin 39183 -> 0 bytes .../Blog/blog/Pictures/Scatterplot copy.png | Bin 100579 -> 0 bytes .../Blog/blog/Pictures/Scatterplot title.png | Bin 22094 -> 0 bytes .../demo/_site/Blog/blog/Pictures/Table 1.png | Bin 104496 -> 0 bytes .../demo/_site/Blog/blog/Pictures/Table 2.png | Bin 119458 -> 0 bytes .../demo/_site/Blog/blog/Pictures/Table 3.png | Bin 44910 -> 0 bytes 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demo/demo/_site/imfp_demo.html | 2462 ----------------- .../figure-html/cell-24-output-1.png | Bin 214819 -> 0 bytes .../figure-html/cell-25-output-1.png | Bin 339340 -> 0 bytes .../figure-html/cell-26-output-2.png | Bin 68675 -> 0 bytes .../figure-html/cell-27-output-1.png | Bin 145804 -> 0 bytes .../figure-html/cell-28-output-1.png | Bin 213473 -> 0 bytes .../figure-html/cell-35-output-1.png | Bin 114720 -> 0 bytes .../figure-html/cell-35-output-2.png | Bin 133720 -> 0 bytes demo/demo/_site/index.html | 669 ----- demo/demo/_site/listing.html | 669 ----- demo/demo/_site/listings.json | 30 - demo/demo/_site/posts/blog/Blog.html | 672 ----- demo/demo/_site/posts/blog/Formula copy.png | Bin 26284 -> 0 bytes demo/demo/_site/posts/blog/Formula.png | Bin 39183 -> 0 bytes .../_site/posts/blog/Pictures/Forecast.png | Bin 205047 -> 0 bytes .../_site/posts/blog/Pictures/Formula.png | Bin 39183 -> 0 bytes .../posts/blog/Pictures/Scatterplot copy.png | Bin 100579 -> 0 bytes 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demo/demo/_site/posts/posts/listing.html | 637 ----- demo/demo/_site/posts/welcome/index.html | 590 ---- demo/demo/_site/posts/welcome/thumbnail.jpg | Bin 39949 -> 0 bytes demo/demo/_site/search.json | 394 --- ...p-8bda806db8b623a4dafb352339cca83f.min.css | 12 - .../site_libs/bootstrap/bootstrap-icons.css | 2078 -------------- .../site_libs/bootstrap/bootstrap-icons.woff | Bin 176200 -> 0 bytes .../site_libs/bootstrap/bootstrap.min.js | 7 - .../site_libs/clipboard/clipboard.min.js | 7 - .../_site/site_libs/quarto-html/anchor.min.js | 9 - .../_site/site_libs/quarto-html/popper.min.js | 6 - ...hting-07ba0ad10f5680c660e360ac31d2f3b6.css | 205 -- .../_site/site_libs/quarto-html/quarto.js | 911 ------ .../_site/site_libs/quarto-html/tippy.css | 1 - .../site_libs/quarto-html/tippy.umd.min.js | 2 - .../site_libs/quarto-listing/list.min.js | 2 - .../quarto-listing/quarto-listing.js | 244 -- .../site_libs/quarto-nav/headroom.min.js | 7 - .../_site/site_libs/quarto-nav/quarto-nav.js | 325 --- .../quarto-search/autocomplete.umd.js | 3 - .../_site/site_libs/quarto-search/fuse.min.js | 9 - .../site_libs/quarto-search/quarto-search.js | 1290 --------- demo/demo/_site/styles.css | 1 - demo/demo/listing.qmd | 14 - demo/demo/posts/_metadata.yml | 8 - demo/demo/posts/posts/Blog.qmd | 106 - demo/demo/posts/posts/Pictures/Forecast.png | Bin 205047 -> 0 bytes demo/demo/posts/posts/Pictures/Formula.png | Bin 39183 -> 0 bytes .../posts/posts/Pictures/Scatterplot copy.png | Bin 100579 -> 0 bytes .../posts/Pictures/Scatterplot title.png | Bin 22094 -> 0 bytes demo/demo/posts/posts/Pictures/Table 1.png | Bin 104496 -> 0 bytes demo/demo/posts/posts/Pictures/Table 2.png | Bin 119458 -> 0 bytes demo/demo/posts/posts/Pictures/Table 3.png | Bin 44910 -> 0 bytes demo/demo/posts/posts/Pictures/Time title.png | Bin 21008 -> 0 bytes demo/demo/posts/posts/Pictures/Time.png | Bin 160254 -> 0 bytes demo/demo/posts/posts/Pictures/box title.png | Bin 20279 -> 0 bytes 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+7,34 @@ on: branches: [dev] jobs: + format: + needs: test + runs-on: ubuntu-latest + permissions: + contents: write + steps: + - uses: actions/checkout@v3 + - uses: actions/setup-python@v4 + with: + python-version: '3.12' + - name: Install and configure Poetry + uses: snok/install-poetry@v1 + - name: Install project + run: | + poetry install + - name: Format code with black + run: | + poetry run black . + - name: Commit and push changes to repository + uses: stefanzweifel/git-auto-commit-action@v5 + with: + commit_message: 'Automated code format' + test: strategy: fail-fast: false matrix: - python-version: ["3.10", "3.12"] + python-version: ["3.10", "3.12", "3.13"] poetry-version: [latest] os: [ubuntu-latest] defaults: @@ -32,27 +55,4 @@ jobs: run: | poetry run pytest tests outputs: - matrix: ${{ toJson(matrix) }} - - render: - needs: test - runs-on: ubuntu-latest - permissions: - contents: write - steps: - - uses: actions/checkout@v3 - - uses: actions/setup-python@v4 - with: - python-version: '3.12' - - name: Install and configure Poetry - uses: snok/install-poetry@v1 - - name: Install project - run: | - poetry install - - name: Format code with black - run: | - poetry run black . - - name: Commit and push changes to repository - uses: stefanzweifel/git-auto-commit-action@v5 - with: - commit_message: 'Automated code format' \ No newline at end of file + matrix: ${{ toJson(matrix) }} \ No newline at end of file diff --git a/.gitignore b/.gitignore index 6edcba1..c503960 100644 --- a/.gitignore +++ b/.gitignore @@ -1,11 +1,8 @@ -# Temporarily ignoring docs folder until I learn how to automate Sphinx doc build -docs - # VSCode files .vscode # demo files -data/ +docs/data/ # Byte-compiled / optimized / DLL files *__pycache__/ @@ -117,8 +114,8 @@ venv.bak/ # Rope project settings .ropeproject -# mkdocs documentation -/site +# rendered documentation +_site/ # mypy .mypy_cache/ diff --git a/News b/News index 103c710..16cf82d 100644 --- a/News +++ b/News @@ -1,5 +1,13 @@ # All updates will be detailed here +## Version 1.1.4 + +- Added a demo project by Jenny Xu + +## Version 1.1.3 + +- Migrated from Sphinx to Quarto documentation + ## Version 1.1.2 - Updated dependencies diff --git a/_quarto.yml b/_quarto.yml index d492b78..550675c 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -1,6 +1,6 @@ project: type: website - output-dir: docs + output-dir: _site website: title: "imfp" @@ -8,6 +8,16 @@ website: left: - href: index.qmd text: Home + - href: docs/demo.qmd + text: Demo + right: + - icon: github + href: https://github.com/Promptly-Technologies-LLC/imfp + page-footer: + left: "Copyright 2024, Promptly Technologies, LLC. MIT License." + right: + - icon: github + href: https://github.com/Promptly-Technologies-LLC/imfp format: html: diff --git a/demo/.DS_Store b/demo/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ zLs35+`xjp>T0 - - - - - - - - -Jenny Xu – imfp Demo - - - - - - - - - - - - - - - - - - - - - - - - - - -

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    Jenny Xu

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    Hi there! My name is Jenny, and I’m a third-year student at University of California, Davis, double majoring in Statistics and Psychology. I’ve always been interested in becoming a data analyst working in tech, internet, or research industries. Interning at Promptly Technologies helped me learn a ton. A quick fun fact for me is that my MBTI is ISFJ (Defender)!

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    Jenny Xu

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    Hi there! My name is Jenny, and I’m a third-year student at University of California, Davis, double majoring in Statistics and Psychology. I’ve always been interested in becoming a data analyst working in tech, internet, or research industries. Interning at Promptly Technologies helped me learn a ton. A quick fun fact for me is that my MBTI is ISFJ (Defender)!

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647d216..0000000 --- a/demo/demo/_site/Blog/blog/Blog.html +++ /dev/null @@ -1,672 +0,0 @@ - - - - - - - - - - - - -Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data – imfp Demo - - - - - - - - - - - - - - - - - - - - - - - - - - - -

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    Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data

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    Data Analysis
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    Author
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    Jenny Xu

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    December 25, 2024

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    Introduction

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    Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders?

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    Our main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables.

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    Ready for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!

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    Data Preparation

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    ​​To explore how economic growth connects with gender equality, we turned to the Imfp library to access a wealth of global economic data, ready to analyze through some straightforward API calls. Let’s break down our two main variables of interest. Here’s the lowdown on the 4 key data series we pulled together:

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    Dependent Variable: Percent Change in Gender Inequality Index (GII)

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    Our gender equality measure, the Gender Inequality Index (GII), is a composite score that reflects inequality in three key areas: reproductive health, empowerment, and labor market, ranging from 0 (equality) to 1 (inequality). We calculated the percent change in GII over time to make it easier to compare GII change across countries with different baseline levels of inequality.

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    Independent Variable: Percent Change in Real GDP per capita

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    Our economic growth measure is real GDP per capita, an indicator of a country’s economic output per person, adjusted for inflation. The higher the real GDP per capita, the wealthier and more prosperous a country is. Here is a peek at the formula:

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    Data Visualization

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    Scatterplot

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    Nothing like a good scatterplot to help us understand the relationship between two numbers, right? For our project, the scatterplot lets us visually explore how Percent Change in Real GDP per capita relates to Percent Change in GII. Each dot on the plot represents a country-year combo. The gentle upward slope suggests that in years of low GDP growth, gender inequality tends to decrease a bit faster, while in years of high GDP growth, the inequality drops at a slightly slower rate. It’s a subtle clue, but slower-growing economies might be making slightly quicker strides in gender equality.

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    Time Series Line plot

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    The time series line plot gives us the big picture of GDP and GII changes over time across a few select countries, including the United States, Japan, the United Kingdom, France, and Mexico. Mexico, for example, saw great GDP swings in 1995, yet its GII stayed relatively stable. Meanwhile, Japan and France saw some dramatic GII shifts. These fluctuations leave us wondering: what historical events sparked these changes? We may need to take a deeper look into each country’s unique timeline to find the answers.

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    Boxplot

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    Next, we have the boxplot to help us visualize the distribution. By sequencing countries in ascending order of median GDP change, we see an interesting trend: countries with higher median GDP growth also seem to show more variability in their GDP changes. The GII change, on the other hand, keeps a relatively stable median, hinting at a weaker association with GDP growth.

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    Heatmap

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    Finally, our heatmap wraps it all up with a quick, intuitive look at correlations between GDP and GII across countries. In countries like Estonia (EE) and Ireland (IE), Australia (AT), South Africa (ZA), Thailand (TH), Slovenia (SI), we see positive correlations in warm colors, indicating that as GDP growth rates increase, GII growth rates tend to rise as well. For countries like Cyprus (CY), Romania (RO), and the United States (US), we see cooler colors, representing negative correlations: as GDP growth rate rises, GII growth rates decrease, suggesting an improvement in gender equality alongside economic growth. This color-coded comparison leaves us curious—could the distinction between developed and developing countries play a role in these relationships? It’s a question that might inspire future research directions!

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    Statistical Analysis

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    Descriptive Statistics

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    Let’s start with a quick overview of our descriptive statistics: our dataset includes 915 data points. On average, GII change is -3.15%, indicating an overall reduction in gender inequality over time. Meanwhile, GDP change averages 2.35%, hinting at modest economic growth. A full breakdown of these values can be found in the table below for more detail.

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    Regression Analysis

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    To get a sense of any relationship between GDP change and GII change, we ran a simple linear regression. Here’s what we found:

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    • P-value: With a p-value of 0.057, we fail to reject the null hypothesis. There is no statistically significant relationship. In other words, we don’t find strong evidence of a link between GDP and GII changes.

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    • R-squared: GDP change explains only 0.4% of the variation in GII change—a pretty minimal effect.

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    • 95% Confidence Interval: We’re 95% confident the interval from -0.003 to 0.169 captures the true slope. The effect of GDP change on GII is uncertain as 0 is within this range.

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    Time Series Analysis

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    Time series analysis allows us to dig deeper into how GII and GDP changes play out over time—and potentially influence one another. Here’s the rundown:

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    • Stationarity check: Both GII and GDP change series are stationary, meaning their means and variances are stable over time, setting us up for reliable forecasting.

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    • VAR Model (Vector Autoregression): This multivariate model lets us explore how past values in both series relate to current values. Notably, past GII changes significantly impact current GII changes.

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    • Model Performance: The VAR model has good fit, as shown by the forecast closely tracking actual values. Low MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) confirm that our model predicts GII and GDP change with minimal error. Check out the plots and table below!

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    • Granger Causality Test: Curious whether GDP changes can predict changes in GII? The Granger causality test says no. With p-values above 0.05, we fail to find evidence that GDP changes drive GII changes. This suggests that while both variables evolve over time, they don’t seem to directly influence each other.

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    Conclusion

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    In wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research.

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    In terms of the Imfp library, we gained valuable experience working with real-world economic indicators like GDP and the GII. The library’s function in fetching databases, parameters, and datasets made it straightforward to access IMF’s data, allowing us to focus on analysis instead. The integration of other Python tools—such as seaborn, matplotlib, and numpy—not only streamlined our computational processes but also ensured consistency across the project.

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    Future Directions

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    First, we must consider what other factors might influence the relationship between GDP and GII change. The GII is a composite index, shaped by a myriad of social factors, including cultural norms, legal frameworks, and environmental shifts. Future studies could benefit from incorporating additional predictors into the analysis and exploring the interaction between economic growth and gender equality within specific country contexts.

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    Second, there’s potential to enhance the predictive power of our Vector Autoregression (VAR) time series model. While we established that GDP change does not cause GII change, our model performed well in forecasting trends for both variables independently. In practice, policymakers may want to forecast GII trends independently of GDP if they are implementing gender-focused policies. Future research could investigate time series modeling to further unravel the dynamics of GII and GDP changes.

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    So, as we wrap up this chapter, let’s keep our curiosity alive and our questions flowing. After all, every end is just a new beginning in the quest for knowledge!

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    - - - - - \ No newline at end of file diff --git a/demo/demo/_site/Blog/post/Blog.html b/demo/demo/_site/Blog/post/Blog.html deleted file mode 100644 index 647d216..0000000 --- a/demo/demo/_site/Blog/post/Blog.html +++ /dev/null @@ -1,672 +0,0 @@ - - - - - - - - - - - - -Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data – imfp Demo - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data

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    Jenny Xu

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    December 25, 2024

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    Introduction

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    Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders?

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    Our main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables.

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    Ready for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!

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    Data Preparation

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    ​​To explore how economic growth connects with gender equality, we turned to the Imfp library to access a wealth of global economic data, ready to analyze through some straightforward API calls. Let’s break down our two main variables of interest. Here’s the lowdown on the 4 key data series we pulled together:

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    Dependent Variable: Percent Change in Gender Inequality Index (GII)

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    Our gender equality measure, the Gender Inequality Index (GII), is a composite score that reflects inequality in three key areas: reproductive health, empowerment, and labor market, ranging from 0 (equality) to 1 (inequality). We calculated the percent change in GII over time to make it easier to compare GII change across countries with different baseline levels of inequality.

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    Independent Variable: Percent Change in Real GDP per capita

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    Our economic growth measure is real GDP per capita, an indicator of a country’s economic output per person, adjusted for inflation. The higher the real GDP per capita, the wealthier and more prosperous a country is. Here is a peek at the formula:

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    Data Visualization

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    Scatterplot

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    Nothing like a good scatterplot to help us understand the relationship between two numbers, right? For our project, the scatterplot lets us visually explore how Percent Change in Real GDP per capita relates to Percent Change in GII. Each dot on the plot represents a country-year combo. The gentle upward slope suggests that in years of low GDP growth, gender inequality tends to decrease a bit faster, while in years of high GDP growth, the inequality drops at a slightly slower rate. It’s a subtle clue, but slower-growing economies might be making slightly quicker strides in gender equality.

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    Time Series Line plot

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    The time series line plot gives us the big picture of GDP and GII changes over time across a few select countries, including the United States, Japan, the United Kingdom, France, and Mexico. Mexico, for example, saw great GDP swings in 1995, yet its GII stayed relatively stable. Meanwhile, Japan and France saw some dramatic GII shifts. These fluctuations leave us wondering: what historical events sparked these changes? We may need to take a deeper look into each country’s unique timeline to find the answers.

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    Boxplot

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    Next, we have the boxplot to help us visualize the distribution. By sequencing countries in ascending order of median GDP change, we see an interesting trend: countries with higher median GDP growth also seem to show more variability in their GDP changes. The GII change, on the other hand, keeps a relatively stable median, hinting at a weaker association with GDP growth.

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    Heatmap

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    Finally, our heatmap wraps it all up with a quick, intuitive look at correlations between GDP and GII across countries. In countries like Estonia (EE) and Ireland (IE), Australia (AT), South Africa (ZA), Thailand (TH), Slovenia (SI), we see positive correlations in warm colors, indicating that as GDP growth rates increase, GII growth rates tend to rise as well. For countries like Cyprus (CY), Romania (RO), and the United States (US), we see cooler colors, representing negative correlations: as GDP growth rate rises, GII growth rates decrease, suggesting an improvement in gender equality alongside economic growth. This color-coded comparison leaves us curious—could the distinction between developed and developing countries play a role in these relationships? It’s a question that might inspire future research directions!

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    Statistical Analysis

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    Descriptive Statistics

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    Let’s start with a quick overview of our descriptive statistics: our dataset includes 915 data points. On average, GII change is -3.15%, indicating an overall reduction in gender inequality over time. Meanwhile, GDP change averages 2.35%, hinting at modest economic growth. A full breakdown of these values can be found in the table below for more detail.

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    Regression Analysis

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    To get a sense of any relationship between GDP change and GII change, we ran a simple linear regression. Here’s what we found:

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    • P-value: With a p-value of 0.057, we fail to reject the null hypothesis. There is no statistically significant relationship. In other words, we don’t find strong evidence of a link between GDP and GII changes.

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    • R-squared: GDP change explains only 0.4% of the variation in GII change—a pretty minimal effect.

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    • 95% Confidence Interval: We’re 95% confident the interval from -0.003 to 0.169 captures the true slope. The effect of GDP change on GII is uncertain as 0 is within this range.

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    Time Series Analysis

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    Time series analysis allows us to dig deeper into how GII and GDP changes play out over time—and potentially influence one another. Here’s the rundown:

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    • Stationarity check: Both GII and GDP change series are stationary, meaning their means and variances are stable over time, setting us up for reliable forecasting.

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    • VAR Model (Vector Autoregression): This multivariate model lets us explore how past values in both series relate to current values. Notably, past GII changes significantly impact current GII changes.

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    • Model Performance: The VAR model has good fit, as shown by the forecast closely tracking actual values. Low MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) confirm that our model predicts GII and GDP change with minimal error. Check out the plots and table below!

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    • Granger Causality Test: Curious whether GDP changes can predict changes in GII? The Granger causality test says no. With p-values above 0.05, we fail to find evidence that GDP changes drive GII changes. This suggests that while both variables evolve over time, they don’t seem to directly influence each other.

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    Conclusion

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    In wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research.

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    In terms of the Imfp library, we gained valuable experience working with real-world economic indicators like GDP and the GII. The library’s function in fetching databases, parameters, and datasets made it straightforward to access IMF’s data, allowing us to focus on analysis instead. The integration of other Python tools—such as seaborn, matplotlib, and numpy—not only streamlined our computational processes but also ensured consistency across the project.

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    Future Directions

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    First, we must consider what other factors might influence the relationship between GDP and GII change. The GII is a composite index, shaped by a myriad of social factors, including cultural norms, legal frameworks, and environmental shifts. Future studies could benefit from incorporating additional predictors into the analysis and exploring the interaction between economic growth and gender equality within specific country contexts.

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    Second, there’s potential to enhance the predictive power of our Vector Autoregression (VAR) time series model. While we established that GDP change does not cause GII change, our model performed well in forecasting trends for both variables independently. In practice, policymakers may want to forecast GII trends independently of GDP if they are implementing gender-focused policies. Future research could investigate time series modeling to further unravel the dynamics of GII and GDP changes.

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    So, as we wrap up this chapter, let’s keep our curiosity alive and our questions flowing. After all, every end is just a new beginning in the quest for knowledge!

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    - - - - - \ No newline at end of file diff --git a/demo/demo/_site/Code/imfp_demo.html b/demo/demo/_site/Code/imfp_demo.html deleted file mode 100644 index f4f6888..0000000 --- a/demo/demo/_site/Code/imfp_demo.html +++ /dev/null @@ -1,2439 +0,0 @@ - - - - - - - - - -imfp Demo Project – imfp Demo - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    imfp Demo Project

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    This data analysis project aims to explore the relationship between economic growth and gender equality using imfp, which allows us to download data from IMF (International Monetary Fund). imfp can be integrated with other python tools to streamline the computational process. To demonstrate its functionality, the project experimented with a variety of visualization and analysis methods.

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    Ready for some insights about if a stronger economy mean more equal opportunities for all genders? Let’s dive in!

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    Executive Summary

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    In this project, we explored the following:

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    1. Data Fetching
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    • Make API call to fetch 4 datasets: GII (Gender Inequality Index), Nominal GDP, GDP Deflator Index, Population series
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    1. Feature Engineering
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    • Cleaning: Convert GDP Deflator Index to a yearly basis and variables to numeric
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    • Dependent Variable: Percent Change of Gender Inequality Index
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    • Independent Variable: Percent Change of Real GDP per Capita
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    • Transform variables to display magnitude of change
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    • Merge the datasets
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    1. Data Visualization
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    • Scatterplot
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    • Time Series Line Plots
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    • Barplot
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    1. Statistical Analysis
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    • Descriptive Statistics
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    • Regression Analysis
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    • Time Series Analysis
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    Suggested packages

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    pandas: view and manipulate data frame; matplotlib.pyplot: make plots; seaborn: make plots; numpy: computation; LinearRegression: implement linear regression; tabulate: format data into tables;

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    A custom module is written to simplify the process of making API calls and fetching information with imfp library. load_or_fetch_databases, load_or_fetch_parameters load_or_fetch_dataset load and retreive database, parameters, and dataset from a local or remote source. view_dataframe_in_browser displays dataframe in a web browser.

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    statsmodels.api, adfuller, ARIMA,VAR,plot_acf,plot_pacf,mean_absolute_error,mean_squared_error, andgrangercausalitytests are specifically used for time series analysis.

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    import os
    -import pickle
    -from tempfile import NamedTemporaryFile
    -import pandas as pd
    -import imfp
    -import webbrowser
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    -# Function to display a DataFrame in a web browser
    -def view_dataframe_in_browser(df):
    -    html = df.to_html()
    -    with NamedTemporaryFile(delete=False, mode="w", suffix=".html") as f:
    -        url = "file://" + f.name
    -        f.write(html)
    -    webbrowser.open(url)
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    -
    -# Function to load databases from CSV or fetch from API
    -def load_or_fetch_databases():
    -    csv_path = os.path.join("data", "databases.csv")
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    -    # Try to load from CSV
    -    if os.path.exists(csv_path):
    -        try:
    -            return pd.read_csv(csv_path)
    -        except Exception as e:
    -            print(f"Error loading CSV: {e}")
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    -    # If CSV doesn't exist or couldn't be loaded, fetch from API
    -    print("Fetching databases from IMF API...")
    -    databases = imfp.imf_databases()
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    -    # Save to CSV for future use
    -    databases.to_csv(csv_path, index=False)
    -    print(f"Databases saved to {csv_path}")
    -
    -    return databases
    -
    -
    -def load_or_fetch_parameters(database_name):
    -    pickle_path = os.path.join("data", f"{database_name}.pickle")
    -
    -    # Try to load from pickle file
    -    if os.path.exists(pickle_path):
    -        try:
    -            with open(pickle_path, "rb") as f:
    -                return pickle.load(f)
    -        except Exception as e:
    -            print(f"Error loading pickle file: {e}")
    -
    -    # If pickle doesn't exist or couldn't be loaded, fetch from API
    -    print(f"Fetching parameters for {database_name} from IMF API...")
    -    parameters = imfp.imf_parameters(database_name)
    -
    -    # Save to pickle file for future use
    -    os.makedirs("data", exist_ok=True)  # Ensure the data directory exists
    -    with open(pickle_path, "wb") as f:
    -        pickle.dump(parameters, f)
    -    print(f"Parameters saved to {pickle_path}")
    -
    -    return parameters
    -
    -
    -def load_or_fetch_dataset(database_id, indicator):
    -    file_name = f"{database_id}.{indicator}.csv"
    -    csv_path = os.path.join("data", file_name)
    -
    -    # Try to load from CSV file
    -    if os.path.exists(csv_path):
    -        try:
    -            return pd.read_csv(csv_path)
    -        except Exception as e:
    -            print(f"Error loading CSV file: {e}")
    -
    -    # If CSV doesn't exist or couldn't be loaded, fetch from API
    -    print(f"Fetching dataset for {database_id}.{indicator} from IMF API...")
    -    dataset = imfp.imf_dataset(database_id=database_id, indicator=[indicator])
    -
    -    # Save to CSV file for future use
    -    os.makedirs("data", exist_ok=True)  # Ensure the data directory exists
    -    dataset.to_csv(csv_path, index=False)
    -    print(f"Dataset saved to {csv_path}")
    -
    -    return dataset
    -
    -
    -
    import pandas as pd
    -import matplotlib.pyplot as plt
    -import seaborn as sns
    -import numpy as np
    -from sklearn.linear_model import LinearRegression
    -from tabulate import tabulate
    -import statsmodels.api as sm
    -from statsmodels.tsa.stattools import adfuller
    -from statsmodels.tsa.arima.model import ARIMA
    -from statsmodels.tsa.vector_ar.var_model import VAR
    -from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
    -from sklearn.metrics import mean_absolute_error
    -from sklearn.metrics import mean_squared_error
    -from statsmodels.tsa.stattools import grangercausalitytests
    -
    -
    -
    -

    Data Fetching

    -

    In this section, we extracted four datasets through API calls: Gender Inequality Index(GII), GDP Deflator, Nominal GDP, and Population. Two databases were used: Gender Equality and International Financial Statistics (IFS).

    -
    -
    from pathlib import Path
    -Path("data").mkdir(exist_ok=True)
    -
    -
    -
    # Load or fetch databases
    -databases = load_or_fetch_databases()
    -
    -# Filter out databases that contain a year in the description
    -databases[
    -  ~databases['description'].str.contains(r"[\d]{4}", regex=True)
    -]
    -
    -# view_dataframe_in_browser(databases)
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    database_iddescription
    25HPDDHistorical Public Debt (HPDD)
    34RAFIT2AGGRevenue Administration Fiscal Information Tool...
    47GENDER_EQUALITYGender Equality
    63PGCSPrivate and Public Capital Stock Dataset
    69GENDER_BUDGETINGGender Budgeting
    125CPIConsumer Price Index (CPI)
    153IRFCLInternational Reserves and Foreign Currency Li...
    191IFS_DISCONTINUEDInternational Financial Statistics (IFS), Disc...
    192EQExport Quality
    193EDExport Diversification
    226FISCALDECENTRALIZATIONFiscal Decentralization
    291FDIFinancial Development Index
    292PSBSFADPublic Sector Balance Sheet (PSBS)(FAD)
    293UNSDG_IMF_INPUTSSustainable Development Goals, IMF Inputs
    294CPISCoordinated Portfolio Investment Survey (CPIS)
    295PCTOTCommodity Terms of Trade
    296FMFiscal Monitor (FM)
    297AFRREOSub-Saharan Africa Regional Economic Outlook (...
    298WHDREOWestern Hemisphere Regional Economic Outlook (...
    299MCDREOMiddle East and Central Asia Regional Economic...
    300APDREOAsia and Pacific Regional Economic Outlook (AP...
    301BOPAGGBalance of Payments (BOP), World and Regional ...
    302PCPSPrimary Commodity Price System (PCPS)
    303CDISCoordinated Direct Investment Survey (CDIS)
    304BOPBalance of Payments (BOP)
    305COFERCurrency Composition of Official Foreign Excha...
    306DOTDirection of Trade Statistics (DOTS)
    307RAFIT3PRA-FIT Round3 Completion and Participation Rates
    308FASFinancial Access Survey (FAS)
    309FSIFinancial Soundness Indicators (FSIs)
    310FSIREFinancial Soundness Indicators: Reporting enti...
    311BOPSDMXUSDBalance of Payments (BOP), Global SDMX (US Dol...
    312NAMAIN_IDC_NSystem of National Accounts (SNA), NA_MAIN
    313GFSRGovernment Finance Statistics (GFS), Revenue
    314GFSSSUCGovernment Finance Statistics (GFS), Statement...
    315GFSCOFOGGovernment Finance Statistics (GFS), Expenditu...
    316GFSFALCSGovernment Finance Statistics (GFS), Financial...
    317GFSIBSGovernment Finance Statistics (GFS), Integrate...
    318GFSMABGovernment Finance Statistics (GFS), Main Aggr...
    319GFSEGovernment Finance Statistics (GFS), Expense
    320IFSInternational Financial Statistics (IFS)
    321MFSMonetary and Financial Statistics (MFS)
    - -
    -
    -
    -
    -
    datasets = ["GENDER_EQUALITY", "IFS"]
    -params = {}
    -
    -# Fetch valid parameters for two datasets
    -for dataset in datasets:
    -    params[dataset] = load_or_fetch_parameters(dataset)
    -
    -    valid_keys = list(params[dataset].keys())
    -    print(f"Parameters for {dataset}: ", valid_keys)
    -
    -# view_dataframe_in_browser(params.get("IFS").get("indicator"))
    -
    -
    Parameters for GENDER_EQUALITY:  ['freq', 'ref_area', 'indicator']
    -Parameters for IFS:  ['freq', 'ref_area', 'indicator']
    -
    -
    -
    -
    datasets = {}
    -dsets = [("GENDER_EQUALITY", "GE_GII"), ("IFS", "NGDP_D_SA_IX"), ("IFS", "NGDP_XDC"), ("IFS", "LP_PE_NUM")]
    -
    -for dset in dsets:
    -    datasets[dset[0] + "." + dset[1]] = load_or_fetch_dataset(dset[0], dset[1])
    -
    -
    -
    # "Gender Inequality Index"
    -GII = "GENDER_EQUALITY.GE_GII"
    -
    -# "Gross Domestic Product, Deflator, Seasonally Adjusted, Index"
    -GDP_deflator = "IFS.NGDP_D_SA_IX"
    -
    -# "Gross Domestic Product, Nominal, Domestic Currency"
    -GDP_nominal = "IFS.NGDP_XDC"
    -
    -# "Population, Persons, Number of"
    -GDP_population = "IFS.LP_PE_NUM"
    -
    -# Assign the datasets to new variables so we don't change the originals
    -GII_data = datasets[GII]
    -GDP_deflator_data = datasets[GDP_deflator]
    -GDP_nominal_data = datasets[GDP_nominal]
    -GDP_population_data = datasets[GDP_population]
    -
    -
    -
    -

    Feature Engineering

    -
    -

    Data Cleaning

    -

    Since the GDP deflator is reported on a quarterly basis, we need to convert it to a yearly basis.

    -
    -
    # Filter to keep only rows with a partial string match for "Q4" in the time_period column
    -GDP_deflator_data = GDP_deflator_data[GDP_deflator_data['time_period'].str.contains("Q4")]
    -
    -
    -
    # Split the time_period into year and quarter and keep the year only
    -GDP_deflator_data.loc[:, 'time_period'] = GDP_deflator_data['time_period'].str[0:4]
    -
    -
    -
    datasets = [GII_data, GDP_deflator_data, GDP_nominal_data, GDP_population_data]
    -
    -for i, dataset in enumerate(datasets):    
    -    # Use .loc to modify the columns
    -    datasets[i].loc[:, 'obs_value'] = pd.to_numeric(datasets[i]['obs_value'], errors='coerce')
    -    datasets[i].loc[:, 'time_period'] = pd.to_numeric(datasets[i]['time_period'], errors='coerce')
    -    datasets[i].loc[:, 'unit_mult'] = pd.to_numeric(datasets[i]['unit_mult'], errors='coerce')
    -
    -
    -
    # Calculate percent change for each ref_area
    -GII_data[f'pct_change'] = GII_data.sort_values(['ref_area', 'time_period']).groupby('ref_area')['obs_value'].pct_change()
    -
    -# Display the first few rows of the updated dataset
    -GII_data.head()
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    freqref_areaindicatorunit_multtime_formattime_periodobs_valuepct_change
    0AAFGE_GII0P1Y19900.828244NaN
    1AAFGE_GII0P1Y19910.817706-0.012723
    2AAFGE_GII0P1Y19920.809806-0.009662
    3AAFGE_GII0P1Y19930.803078-0.008308
    4AAFGE_GII0P1Y19940.797028-0.007533
    - -
    -
    -
    -
    -
    # Create a new dataframe with only the required columns
    -GII_data = GII_data[['ref_area', 'time_period', 'pct_change']].copy()
    -
    -# Display the first few rows of the new dataset
    -GII_data.head()
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    ref_areatime_periodpct_change
    0AF1990NaN
    1AF1991-0.012723
    2AF1992-0.009662
    3AF1993-0.008308
    4AF1994-0.007533
    - -
    -
    -
    -
    -
    # GDP Deflator Dataset
    -# Create a new dataframe with only the required columns
    -GDP_deflator_data = GDP_deflator_data[['ref_area', 'time_period', 'unit_mult', 'obs_value']].copy()
    -
    -# Divide obs_value by 100
    -GDP_deflator_data['obs_value'] = GDP_deflator_data['obs_value'] / 100
    -
    -# Display the first few rows of the new dataset
    -GDP_deflator_data.head()
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    ref_areatime_periodunit_multobs_value
    3FI199000.732006
    7FI199100.739841
    11FI199200.746543
    15FI199300.756193
    19FI199400.779373
    - -
    -
    -
    -
    -
    # GDP Nominal Data
    -# Create a new dataframe with only the required columns
    -GDP_nominal_data = GDP_nominal_data[['ref_area', 'time_period', 'unit_mult','obs_value']].copy()
    -
    -# Display the first few rows of the new dataset
    -GDP_nominal_data.head()
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    ref_areatime_periodunit_multobs_value
    0NE200562418864.0
    1NE200662596972.0
    2NE200762762961.0
    3NE200863247835.0
    4NE200963403683.0
    - -
    -
    -
    -
    -
    # GDP Population Data 
    -# Create a new dataframe with only the required columns
    -GDP_population_data = GDP_population_data[['ref_area', 'time_period', 'unit_mult','obs_value']].copy()
    -
    -# Display the first few rows of the new dataset
    -GDP_population_data.head()
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    ref_areatime_periodunit_multobs_value
    0GA19503473.296
    1GA19513476.381
    2GA19523478.655
    3GA19533480.536
    4GA19543482.332
    - -
    -
    -
    -
    -
    # Combine all the datasets above for further calculation
    -merged_df = pd.merge(pd.merge(GDP_deflator_data,GDP_nominal_data, on=['time_period', 'ref_area'], suffixes=('_index', '_nominal'), how='inner'), GDP_population_data, on=['time_period', 'ref_area'], how='inner')
    -
    -# Display the GDP data in a web browser
    -# view_dataframe_in_browser(merged_df)
    -
    -# Display the first few rows of the dataset
    -merged_df.head()
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    ref_areatime_periodunit_mult_indexobs_value_indexunit_mult_nominalobs_value_nominalunit_multobs_value
    0FI199000.732006690964.034996.220
    1FI199100.739841686913.035019.134
    2FI199200.746543684786.035044.928
    3FI199300.756193685610.035071.782
    4FI199400.779373690646.035097.090
    - -
    -
    -
    -
    -
    merged_df['adjusted_index'] = merged_df['obs_value_index'] * (10 ** (merged_df['unit_mult_index']))
    -merged_df['adjusted_nominal'] = merged_df['obs_value_nominal'] * (10 ** (merged_df['unit_mult_nominal']))
    -merged_df['adjusted_population'] = merged_df['obs_value'] * (10 ** (merged_df['unit_mult']))
    -
    -
    -
    # Merged dataset
    -# Create a new dataframe with only the required columns
    -merged_df = merged_df[['ref_area', 'time_period','adjusted_nominal', 'adjusted_index', 'adjusted_population']].copy()
    -
    -# Display the first few rows of the dataset
    -merged_df.head()
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    ref_areatime_periodadjusted_nominaladjusted_indexadjusted_population
    0FI19909.096400e+100.7320064996220.0
    1FI19918.691300e+100.7398415019134.0
    2FI19928.478600e+100.7465435044928.0
    3FI19938.561000e+100.7561935071782.0
    4FI19949.064600e+100.7793735097090.0
    - -
    -
    -
    -
    -
    # Step 1: Real GDP = (Nominal GDP / GDP Deflator Index)
    -merged_df['Real_GDP_domestic'] = (merged_df['adjusted_nominal'] / merged_df['adjusted_index'])
    -
    -# Step 2: Real GDP per Capita = Real GDP / Population
    -merged_df['Real_GDP_per_capita'] = merged_df['Real_GDP_domestic'] / merged_df['adjusted_population']
    -
    -# Check the results
    -merged_df.head()
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    ref_areatime_periodadjusted_nominaladjusted_indexadjusted_populationReal_GDP_domesticReal_GDP_per_capita
    0FI19909.096400e+100.7320064996220.01.242667e+1124872.143699
    1FI19918.691300e+100.7398415019134.01.174753e+1123405.490395
    2FI19928.478600e+100.7465435044928.01.135715e+1122512.011198
    3FI19938.561000e+100.7561935071782.01.132119e+1122321.919259
    4FI19949.064600e+100.7793735097090.01.163063e+1122818.181344
    - -
    -
    -
    -
    -
    # Calculate percent change for each ref_area
    -merged_df[f'GDP_change'] = merged_df.sort_values(['ref_area', 'time_period']).groupby('ref_area')['Real_GDP_per_capita'].pct_change()
    -
    -# Rename dataset
    -GDP_data = merged_df
    -
    -# Display the first few rows of the dataset
    -GDP_data.head()
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    ref_areatime_periodadjusted_nominaladjusted_indexadjusted_populationReal_GDP_domesticReal_GDP_per_capitaGDP_change
    0FI19909.096400e+100.7320064996220.01.242667e+1124872.143699NaN
    1FI19918.691300e+100.7398415019134.01.174753e+1123405.490395-0.058968
    2FI19928.478600e+100.7465435044928.01.135715e+1122512.011198-0.038174
    3FI19938.561000e+100.7561935071782.01.132119e+1122321.919259-0.008444
    4FI19949.064600e+100.7793735097090.01.163063e+1122818.1813440.022232
    - -
    -
    -
    -
    -
    # GII and GDP
    -# Merge the datasets
    -combined_data = pd.merge(GII_data, GDP_data, on=["ref_area", "time_period"], how = "inner")
    -
    -# Check the combined dataset
    -combined_data.head()
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    ref_areatime_periodpct_changeadjusted_nominaladjusted_indexadjusted_populationReal_GDP_domesticReal_GDP_per_capitaGDP_change
    0AR2004-0.0218964.851153e+110.40967238491970.01.184155e+1230763.687776NaN
    1AR2005-0.0085815.825382e+110.47010838892924.01.239159e+1231860.7727760.035662
    2AR20060.0073797.159043e+110.52653539289876.01.359651e+1234605.6280840.086152
    3AR2007-0.0201178.969801e+110.62913939684303.01.425727e+1235926.7345750.038176
    4AR20080.0007331.149646e+120.75775340080159.01.517178e+1237853.6002520.053633
    - -
    -
    -
    -
    -
    # GII and GDP
    -# Rename columns
    -combined_data = combined_data.rename(columns = {
    -    "ref_area": "Country",
    -    "time_period": "Time",
    -    "pct_change": "GII_change"
    -})
    -
    -# Display the GDP data in a web browser
    -# view_dataframe_in_browser(combined_data)
    -
    -# Check the combined dataset
    -combined_data.head()
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    CountryTimeGII_changeadjusted_nominaladjusted_indexadjusted_populationReal_GDP_domesticReal_GDP_per_capitaGDP_change
    0AR2004-0.0218964.851153e+110.40967238491970.01.184155e+1230763.687776NaN
    1AR2005-0.0085815.825382e+110.47010838892924.01.239159e+1231860.7727760.035662
    2AR20060.0073797.159043e+110.52653539289876.01.359651e+1234605.6280840.086152
    3AR2007-0.0201178.969801e+110.62913939684303.01.425727e+1235926.7345750.038176
    4AR20080.0007331.149646e+120.75775340080159.01.517178e+1237853.6002520.053633
    - -
    -
    -
    -
    -
    -
    -

    Data Visualization

    -
    -

    Scatterplot

    -

    Scatterplot use dots to represent values of two numeric variables. The horizontal axis is the percent change in Real GDP per capita. The vertical axis is the percent change in Gender Inequality Index(GII). Different colors represent different countries. We used a linear regression line to display the overall pattern.

    -

    Based on the scatterplot, it seems like there is a slight positive relationship between GDP change and GII change as shown by the flat regression line. Gender inequality is decreasing (gender equality is improving) a little faster in country-years with low GDP growth and a little slower in country-years with high GDP growth.

    -
    -
    # Drop NAs
    -combined_data = combined_data.dropna(subset=['GDP_change', 'GII_change'])
    -
    -# Plot the data points
    -plt.figure(figsize=(18, 10))
    -for country in combined_data['Country'].unique():
    -    country_data = combined_data[combined_data['Country'] == country]
    -    plt.scatter(country_data['GDP_change'], country_data['GII_change'],
    -             marker='o',linestyle='-', label=country)
    -plt.title('Country-Year Analysis of GDP Change vs. GII Change')
    -plt.xlabel('Percent Change in Real GDP per Capita (Country-Year)')
    -plt.ylabel('Percent Change in GII (Country-Year)')
    -plt.grid(True)
    -
    -# Prepare data for linear regression
    -X = combined_data['GDP_change'].values.reshape(-1, 1)
    -y = combined_data['GII_change'].values
    -
    -# Perform linear regression
    -reg = LinearRegression().fit(X, y)
    -y_pred = reg.predict(X)
    -
    -# Plot the regression line
    -plt.plot(combined_data['GDP_change'], y_pred, color='red', linewidth=2)
    -
    -plt.show()
    -
    -
    -
    -

    -
    -
    -
    -
    -
    -
    -

    Time Series Line Plot

    -

    Create separate line plots for GDP change and GII change over time for a few key countries might show the trends more clearly.

    -

    US: United States JP: Japan GB: United Kindom FR: France MX: Mexico

    -

    Based on the line plots, we see GDP change and GII change have different patterns. For example, in Mexico, when there is a big change in real GDP per captia in 1995, the change in GII is pretty stable.

    -
    -
    # Time Series Line plot for a few key countries
    -selected_countries  = ['US', 'JP', 'GB', 'FR', 'MX']
    -combined_data_selected = combined_data[combined_data['Country'].isin(selected_countries)]
    -
    -# Set up the Plot Structure
    -fig, ax = plt.subplots(2, 1, figsize=(18, 10), sharex=True)
    -
    -# Plot change in real GDP per capita over time
    -sns.lineplot(data = combined_data_selected, x = "Time", y = "GDP_change", hue = "Country", ax = ax[0])
    -ax[0].set_title("Percent Change in Real GDP per Capita Over Time")
    -ax[0].set_ylabel("Percent Change in Real GDP per Capita")
    -
    -# Plot change in GII over time
    -sns.lineplot(data = combined_data_selected, x = "Time", y = "GII_change", hue = "Country", ax = ax[1])
    -ax[1].set_title("Percent Change in GII over Time")
    -ax[1].set_xlabel("Time")
    -ax[1].set_ylabel("GII")
    -
    -plt.tight_layout
    -plt.show()
    -
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    Barplot

    -

    Use a barplot to show average changes in GII and GDP percent change for each country to visualize regions where inequality is improving or worsening.

    -

    This plot supports our previous observation how GII change seems to be not be correlated with GDP change. We also saw that, for country SI, Solvenia, there seems to be a large improvement in gender inequality.

    -
    -
    # Barplot using average GII and GDP change
    -# Calculate average change for each country
    -combined_data_avg = combined_data.groupby('Country')[['GII_change','GDP_change']].mean().reset_index()
    -
    -# Prepare to plot structure 
    -plt.figure(figsize = (18,10))
    -
    -# Create the barplot
    -combined_data_avg.plot(kind = 'bar', x = 'Country')
    -plt.ylabel('Average Change')
    -plt.xlabel('Country')
    -plt.legend(['GII change', 'GDP change'])
    -plt.grid(axis = 'y')
    -
    -# Show the plot
    -plt.show()
    -
    -
    <Figure size 1728x960 with 0 Axes>
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    Boxplot

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    Use boxplot to visualize the distribution of GDP and GII change by country, providing information about spread, median, and potential outliers. To provide a more informative view, we sequence countries in an ascending order by the median of percent change in GDP.

    -

    The boxplot displays a slight upward trend with no obvious pattern between GDP and GII change. In coutries with higher GDP change median, they also tend to have a larger spread of the GDP change. The median of GII change remains stable regardless of the magnitude of GDP change, implying weak or no association between GDP and GII change. We observe a potential outlier for country SI, Solvenia, which may explains its large improvement in Gender inequality.

    -
    -
    # Box plot for GII and GDP change
    -# Melt the dataframe to long format for combined boxplot
    -combined_data_melted = combined_data.melt(id_vars=['Country'], value_vars=['GII_change', 'GDP_change'], 
    -                                          var_name='Change_Type', value_name='Value')
    -
    -gdp_medians = combined_data.groupby('Country')['GDP_change'].median().sort_values()
    -
    -combined_data_melted['Country'] = pd.Categorical(combined_data_melted['Country'], categories=gdp_medians.index, ordered= True)
    -
    -# Prepare the plot structure
    -plt.figure(figsize=(18, 10))
    -sns.boxplot(data = combined_data_melted, x = "Country", y = 'Value', hue = 'Change_Type')
    -plt.title('Distribution of GII and GDP change by Country')
    -plt.xlabel('Country')
    -plt.ylabel('Change')
    -plt.legend(title = 'Change Type')
    -
    -# Show the plot
    -plt.show()
    -
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    Correlation Matrix

    -

    Create a heatmap to show the relationship between GII and GDP change.

    -

    A positive correlation coefficient indicates a positive relationship: the larger the GDP change, the larger the GII change. A negative correlation coefficient indicates a negative relationship: the larger the GDP change, the smaller the GII change. A correlation coefficient closer to 0 indicates there is weak or no relationship.

    -

    Based on the numeric values in the plot, there is a moderately strong positive correlation between GII and GDP change for country Estonia(EE) and Ireland(IE).

    -
    -
    # Calculate the correlation
    -country_correlation = combined_data.groupby('Country')[['GII_change', 'GDP_change']].corr().iloc[0::2, -1].reset_index(name='Correlation')
    -
    -# Put the correlation value in a matrix format
    -correlation_matrix = country_correlation.pivot(index='Country', columns='level_1', values='Correlation')
    -
    -# Check for NaN values in the correlation matrix
    -correlation_matrix.fillna(0, inplace=True)  # Replace NaNs with 0 or another value as appropriate
    -
    -# Set up the plot structure
    -plt.figure(figsize=(18, 12))  # Adjust height to give more space for y-axis labels
    -
    -# Plot the heatmap
    -sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0, cbar_kws={"shrink": .8}, linewidths=.5)
    -
    -# Enhance axis labels and title
    -plt.title('Heatmap for GII and GDP Change', fontsize=20)
    -plt.xlabel('Variables', fontsize=16)
    -plt.ylabel('Country', fontsize=16)
    -
    -# Improve readability of y-axis labels
    -plt.yticks(fontsize=12)  # Adjust the font size for y-axis labels
    -
    -# Show the plot
    -plt.show()
    -
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    Statistical Analysis

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    Descriptive Statistics

    -

    There is a total of 915 data points. The mean of the GII change in -0.0314868, which indicates the overall grand mean percent change in gender inequality index is -3.15%. The mean of the GDP change is 0.0234633, showing the overall grand mean percent change in real GDP per capita is 2.35%.

    -
    -
    # Generate summary statistics
    -descriptive_stats = combined_data.describe()
    -print(tabulate(descriptive_stats, headers='keys', tablefmt='github'))
    -
    -
    |       |   GII_change |   adjusted_nominal |   adjusted_index |   adjusted_population |   Real_GDP_domestic |   Real_GDP_per_capita |   GDP_change |
    -|-------|--------------|--------------------|------------------|-----------------------|---------------------|-----------------------|--------------|
    -| count | 914          |      914           |      914         |         914           |       914           |         914           | 914          |
    -| mean  |  -0.0314506  |        7.85541e+13 |        0.855504  |           4.50061e+07 |         7.60737e+13 |           1.00595e+06 |   0.023532   |
    -| std   |   0.054783   |        6.01311e+14 |        0.210057  |           1.24493e+08 |         5.52717e+14 |           3.88155e+06 |   0.0418127  |
    -| min   |  -0.755003   |        2.18714e+09 |        0.0360636 |      396324           |         5.21733e+09 |        1798.42        |  -0.285847   |
    -| 25%   |  -0.0424105  |        1.07655e+11 |        0.743355  |           5.0737e+06  |         1.48206e+11 |       17176           |   0.00384809 |
    -| 50%   |  -0.020248   |        7.50706e+11 |        0.888883  |           1.0337e+07  |         9.94933e+11 |       35401           |   0.0222486  |
    -| 75%   |  -0.00451301 |        2.5911e+12  |        1.00175   |           4.5159e+07  |         2.9412e+12  |      120391           |   0.0427318  |
    -| max   |   0.156992   |        9.54613e+15 |        2.0788    |           1.28084e+09 |         7.92007e+15 |           3.14532e+07 |   0.241984   |
    -
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    Regression Analysis

    -

    Simple linear regression as a foundational approach provide us with a basic understanding of the relationship between GDP change and GII change.

    -

    Based on the summary, we can conclude the following: * Becasue p-value = 0.057, if we set alpha, the significance level, to be 0.05, we fail to reject the null hypothesis and conclude there is no significant relationship between percent change in real GDP per capita and gender inequality index.

    -
      -
    • R-squared = 0.004. Only 0.4% of the variance in GII change can be explained by GDP change.

    • -
    • We are 95% confident that the interval from -0.003 to 0.169 captures the true slope of GDP change. Because 0 is included, we are uncertain about the effect of GDP change on GII chnage.

    • -
    -
    -
    # Define independent and depenent variables
    -X = combined_data['GDP_change']
    -y = combined_data['GII_change']
    -
    -# Add a constant to indepdent variable to include an intercept
    -X = sm.add_constant(X)
    -
    -# Fit a simple linear regresion model and print out the summary
    -model = sm.OLS(y, X).fit()
    -print(model.summary())
    -
    -
                                OLS Regression Results                            
    -==============================================================================
    -Dep. Variable:             GII_change   R-squared:                       0.004
    -Model:                            OLS   Adj. R-squared:                  0.003
    -Method:                 Least Squares   F-statistic:                     3.551
    -Date:                Wed, 25 Dec 2024   Prob (F-statistic):             0.0598
    -Time:                        16:32:05   Log-Likelihood:                 1360.0
    -No. Observations:                 914   AIC:                            -2716.
    -Df Residuals:                     912   BIC:                            -2706.
    -Df Model:                           1                                         
    -Covariance Type:            nonrobust                                         
    -==============================================================================
    -                 coef    std err          t      P>|t|      [0.025      0.975]
    -------------------------------------------------------------------------------
    -const         -0.0334      0.002    -16.069      0.000      -0.037      -0.029
    -GDP_change     0.0816      0.043      1.885      0.060      -0.003       0.167
    -==============================================================================
    -Omnibus:                      841.660   Durbin-Watson:                   1.384
    -Prob(Omnibus):                  0.000   Jarque-Bera (JB):            53999.190
    -Skew:                          -3.938   Prob(JB):                         0.00
    -Kurtosis:                      39.823   Cond. No.                         23.9
    -==============================================================================
    -
    -Notes:
    -[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
    -
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    -
    -
    -

    Time Series Analysis

    -

    Time series analysis allows us to explore how the relationship between GII and GDP change vary across different time periods, accounting for lagged effects.

    -

    Here is a quick summary of the result: * Both GII and GDP change time series are stationary.

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      -
    • Past GII change values significantly influence cuurent GII change values.

    • -
    • VAR model has good model performance on forecasting future values based on historical data.

    • -
    • Changes in GDP does not cause/precde the changes in GII.

    • -
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    -

    ADF Test: Stationality Assumption Check

    -

    We want to use Augmented Dickey-Fuller (ADF) test to check whether a time series is stationary, which is the model assumption for many time series models.

    -

    Stationarity implies constant mean and variance over time, making it more predictable and stable for forecasting.

    -

    Based on the ADF test output, both GII and GDP change time series are stationary. We can proceed to the time series modeling section.

    -
    -
    # Augmented Dickey-Fuller (ADF) test for stationarity check
    -# Create melted datasets
    -combined_data_time = combined_data.melt(id_vars=['Time', 'Country'], value_vars=['GII_change','GDP_change'], var_name = 'Change_Type', value_name = 'Value')
    -GII = combined_data_time[(combined_data_time['Change_Type'] == 'GII_change')]                         
    -
    -GDP = combined_data_time[(combined_data_time['Change_Type'] == 'GDP_change')]
    -
    -# Stationary Check
    -def adf_test(series):
    -    result = adfuller(series.dropna())
    -    print(f'ADF Statistic: {result[0]}')
    -    print(f'p-value: {result[1]}')
    -    if result[1] < 0.05:
    -        print("Series is stationary")
    -    else:
    -        print("Series is not stationary")
    -
    -# Output the result
    -adf_test(GII['Value'])
    -adf_test(GDP['Value'])
    -
    -
    ADF Statistic: -16.069430375623295
    -p-value: 5.527565277907626e-29
    -Series is stationary
    -ADF Statistic: -8.535683414310181
    -p-value: 1.0095278443825336e-13
    -Series is stationary
    -
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    -

    VAR model: Examine variables separately

    -

    We fit a VAR (Vector Autoreression) model to see the relationship between GII and GDP change. VAR is particularly useful when dealing with multivariate time series data and allows us to examine the interdependence between variables.

    -

    Based on summary, here are several interpretations we can make: - We used AIC as the criteria for model selection. Lower value suggests a better fit. - Given that we want to predict GII change, we will focus on the first set “Results for equation GII_change.” - Past GII_change values significantly influence current GII_change, as shown in the small p-values of lags 1 and 2. - Lag 2 of GDP_change has a relatively low p-value but is not statistically significant.

    -
    -
    # Split the dataset into training and testing sets
    -split_ratio = 0.7
    -split_index = int(len(combined_data) * split_ratio)
    -
    -# Training set is used to fit the model
    -train_data = combined_data.iloc[:split_index]
    -
    -# Testing set is used for validation
    -test_data = combined_data.iloc[split_index:]
    -
    -print(f"Training data: {train_data.shape}")
    -print(f"Test data: {test_data.shape}")
    -
    -
    Training data: (639, 9)
    -Test data: (275, 9)
    -
    -
    -
    -
    # Fit a VAR model 
    -time_model = VAR(train_data[['GII_change', 'GDP_change']])
    -time_model_fitted = time_model.fit(maxlags = 15, ic="aic")
    -
    -# Print out the model summary
    -print(time_model_fitted.summary())
    -
    -
      Summary of Regression Results   
    -==================================
    -Model:                         VAR
    -Method:                        OLS
    -Date:           Wed, 25, Dec, 2024
    -Time:                     16:32:06
    ---------------------------------------------------------------------
    -No. of Equations:         2.00000    BIC:                   -12.2270
    -Nobs:                     636.000    HQIC:                  -12.2869
    -Log likelihood:           2128.47    FPE:                4.43927e-06
    -AIC:                     -12.3250    Det(Omega_mle):     4.34314e-06
    ---------------------------------------------------------------------
    -Results for equation GII_change
    -================================================================================
    -                   coefficient       std. error           t-stat            prob
    ---------------------------------------------------------------------------------
    -const                -0.023760         0.003176           -7.481           0.000
    -L1.GII_change         0.211342         0.039900            5.297           0.000
    -L1.GDP_change        -0.015713         0.046080           -0.341           0.733
    -L2.GII_change         0.111672         0.040402            2.764           0.006
    -L2.GDP_change         0.088293         0.046452            1.901           0.057
    -L3.GII_change        -0.021861         0.039774           -0.550           0.583
    -L3.GDP_change         0.000651         0.046141            0.014           0.989
    -================================================================================
    -
    -Results for equation GDP_change
    -================================================================================
    -                   coefficient       std. error           t-stat            prob
    ---------------------------------------------------------------------------------
    -const                 0.014585         0.002720            5.362           0.000
    -L1.GII_change         0.004128         0.034171            0.121           0.904
    -L1.GDP_change         0.141566         0.039464            3.587           0.000
    -L2.GII_change        -0.009734         0.034600           -0.281           0.778
    -L2.GDP_change         0.044282         0.039782            1.113           0.266
    -L3.GII_change        -0.037787         0.034063           -1.109           0.267
    -L3.GDP_change         0.143870         0.039516            3.641           0.000
    -================================================================================
    -
    -Correlation matrix of residuals
    -              GII_change  GDP_change
    -GII_change      1.000000    0.040992
    -GDP_change      0.040992    1.000000
    -
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    /Users/sammi/Library/Caches/pypoetry/virtualenvs/imfp-0AlIckOY-py3.13/lib/python3.13/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: An unsupported index was provided. As a result, forecasts cannot be generated. To use the model for forecasting, use one of the supported classes of index.
    -  self._init_dates(dates, freq)
    -
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    VAR Model: Forecasting

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    We apply the model learned above to the test data. Based on the plot, the forecast values seem to follow the actual data well, indicating a good model fit caputuring the underlying trends.

    -
    -
    # Number of steps to forecast (length of the test set)
    -n_steps = len(test_data)
    -
    -# Get the last values from the training set for forecasting
    -forecast_input = train_data[['GII_change', 'GDP_change']].values[-time_model_fitted.k_ar:]
    -
    -# Forecasting
    -forecast = time_model_fitted.forecast(y=forecast_input, steps=n_steps)
    -
    -# Create a DataFrame for the forecasted values
    -forecast_df = pd.DataFrame(forecast, index=test_data.index, columns=['GII_forecast', 'GDP_forecast'])
    -
    -# Ensure the index of the forecast_df matches the test_data index
    -forecast_df.index = test_data.index
    -
    -
    -
    plt.figure(figsize=(12, 6))
    -plt.plot(train_data['GII_change'], label='Training GII', color='blue')
    -plt.plot(test_data['GII_change'], label='Actual GII', color='orange')
    -plt.plot(forecast_df['GII_forecast'], label='Forecasted GII', color='green')
    -plt.title('GII Change Forecast vs Actual')
    -plt.legend()
    -plt.show()
    -
    -plt.figure(figsize=(12, 6))
    -plt.plot(train_data['GDP_change'], label='Training GDP', color='blue')
    -plt.plot(test_data['GDP_change'], label='Actual GDP', color='orange')
    -plt.plot(forecast_df['GDP_forecast'], label='Forecasted GDP', color='green')
    -plt.title('GDP Change Forecast vs Actual')
    -plt.legend()
    -plt.show()
    -
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    VAR Model: Model Performance

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    Low values of both MAE and RMSE indicate good model performance with small average errors in predictions.

    -
    -
    mae_gii = mean_absolute_error(test_data['GII_change'], forecast_df['GII_forecast'])
    -mae_gdp = mean_absolute_error(test_data['GDP_change'], forecast_df['GDP_forecast'])
    -
    -print(f'Mean Absolute Error for GII: {mae_gii}')
    -print(f'Mean Absolute Error for GDP: {mae_gdp}')
    -
    -
    Mean Absolute Error for GII: 0.03296113056900485
    -Mean Absolute Error for GDP: 0.027636232780964363
    -
    -
    -
    -
    rmse_gii = np.sqrt(mean_squared_error(test_data['GII_change'], forecast_df['GII_forecast']))
    -rmse_gdp = np.sqrt(mean_squared_error(test_data['GDP_change'], forecast_df['GDP_forecast']))
    -
    -print(f'RMSE for GII: {rmse_gii}')
    -print(f'RMSE for GDP: {rmse_gdp}')
    -
    -
    RMSE for GII: 0.06284754560659174
    -RMSE for GDP: 0.038429216092797085
    -
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    -
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    VAR Model: Granger causality test

    -

    Granger causality test evaluates whether one time series can predict another.

    -

    Based on the output, the lowest p-value is when lag = 2. However, because p-value > 0.05, we fail to reject the null hypothesis and conclude the GDP_change does not Granger-cause the GII_change.

    -
    -
    # Perform the Granger causality test
    -max_lag = 3
    -test_result = grangercausalitytests(train_data[['GII_change', 'GDP_change']], max_lag, verbose=True)
    -
    -
    
    -Granger Causality
    -number of lags (no zero) 1
    -ssr based F test:         F=0.0000  , p=0.9972  , df_denom=635, df_num=1
    -ssr based chi2 test:   chi2=0.0000  , p=0.9972  , df=1
    -likelihood ratio test: chi2=0.0000  , p=0.9972  , df=1
    -parameter F test:         F=0.0000  , p=0.9972  , df_denom=635, df_num=1
    -
    -Granger Causality
    -number of lags (no zero) 2
    -ssr based F test:         F=1.8529  , p=0.1576  , df_denom=632, df_num=2
    -ssr based chi2 test:   chi2=3.7351  , p=0.1545  , df=2
    -likelihood ratio test: chi2=3.7242  , p=0.1553  , df=2
    -parameter F test:         F=1.8529  , p=0.1576  , df_denom=632, df_num=2
    -
    -Granger Causality
    -number of lags (no zero) 3
    -ssr based F test:         F=1.2359  , p=0.2958  , df_denom=629, df_num=3
    -ssr based chi2 test:   chi2=3.7489  , p=0.2899  , df=3
    -likelihood ratio test: chi2=3.7379  , p=0.2912  , df=3
    -parameter F test:         F=1.2359  , p=0.2958  , df_denom=629, df_num=3
    -
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    /Users/sammi/Library/Caches/pypoetry/virtualenvs/imfp-0AlIckOY-py3.13/lib/python3.13/site-packages/statsmodels/tsa/stattools.py:1556: FutureWarning: verbose is deprecated since functions should not print results
    -  warnings.warn(
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    Conclusion

    -

    In wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research.

    -
    -
    -

    Future Directions

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    • Additional Predictors: Gender Inequality Index (GII) is a composite measure shaped by a mariad of social factors. What about culture, policy, and environment?
    • -
    • Specific Country: Are there any specific historical contexts that sparked economic growth and improvement in gender equality?
    • -
    • Opposite causation: Women entering workforce boost GDP?
    • -
    • Predictive Power of VAR model: Our model performed well in forecasting trends for both variables independently.
    • -
    -

    Let’s keep our curiosity alive and our questions flowing!

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--- a/demo/demo/_site/imfp_demo.html +++ /dev/null @@ -1,2462 +0,0 @@ - - - - - - - - - -imfp Demo Project – imfp Demo - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

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    imfp Demo Project

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    This data analysis project aims to explore the relationship between economic growth and gender equality using imfp, which allows us to download data from IMF (International Monetary Fund). imfp can be integrated with other python tools to streamline the computational process. To demonstrate its functionality, the project experimented with a variety of visualization and analysis methods.

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    Executive Summary

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    In this project, we explored the following:

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    1. Data Fetching
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    • Make API call to fetch 4 datasets: GII (Gender Inequality Index), Nominal GDP, GDP Deflator Index, Population series
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    1. Feature Engineering
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    • Cleaning: Convert GDP Deflator Index to a yearly basis and variables to numeric
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    • Dependent Variable: Percent Change of Gender Inequality Index
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    • Independent Variable: Percent Change of Real GDP per Capita
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    • Transform variables to display magnitude of change
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    • Merge the datasets
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    1. Data Visualization
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    • Scatterplot
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    • Time Series Line Plots
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    • Barplot
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    • Boxplot
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    • Heatmap
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    1. Statistical Analysis
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    • Descriptive Statistics
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    • Regression Analysis
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    • Time Series Analysis
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    Suggested packages

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    The integration of other Python tools not only streamlined our computational processes but also ensured consistency across the project.

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    A custom module is written to simplify the process of making API calls and fetching information with imfp library. load_or_fetch_databases, load_or_fetch_parameters load_or_fetch_dataset load and retreive database, parameters, and dataset from a local or remote source. view_dataframe_in_browser displays dataframe in a web browser.

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    import os
    -import pickle
    -from tempfile import NamedTemporaryFile
    -import pandas as pd
    -import imfp
    -import webbrowser
    -
    -
    -# Function to display a DataFrame in a web browser
    -def view_dataframe_in_browser(df):
    -    html = df.to_html()
    -    with NamedTemporaryFile(delete=False, mode="w", suffix=".html") as f:
    -        url = "file://" + f.name
    -        f.write(html)
    -    webbrowser.open(url)
    -
    -
    -# Function to load databases from CSV or fetch from API
    -def load_or_fetch_databases():
    -    csv_path = os.path.join("data", "databases.csv")
    -
    -    # Try to load from CSV
    -    if os.path.exists(csv_path):
    -        try:
    -            return pd.read_csv(csv_path)
    -        except Exception as e:
    -            print(f"Error loading CSV: {e}")
    -
    -    # If CSV doesn't exist or couldn't be loaded, fetch from API
    -    print("Fetching databases from IMF API...")
    -    databases = imfp.imf_databases()
    -
    -    # Save to CSV for future use
    -    databases.to_csv(csv_path, index=False)
    -    print(f"Databases saved to {csv_path}")
    -
    -    return databases
    -
    -
    -def load_or_fetch_parameters(database_name):
    -    pickle_path = os.path.join("data", f"{database_name}.pickle")
    -
    -    # Try to load from pickle file
    -    if os.path.exists(pickle_path):
    -        try:
    -            with open(pickle_path, "rb") as f:
    -                return pickle.load(f)
    -        except Exception as e:
    -            print(f"Error loading pickle file: {e}")
    -
    -    # If pickle doesn't exist or couldn't be loaded, fetch from API
    -    print(f"Fetching parameters for {database_name} from IMF API...")
    -    parameters = imfp.imf_parameters(database_name)
    -
    -    # Save to pickle file for future use
    -    os.makedirs("data", exist_ok=True)  # Ensure the data directory exists
    -    with open(pickle_path, "wb") as f:
    -        pickle.dump(parameters, f)
    -    print(f"Parameters saved to {pickle_path}")
    -
    -    return parameters
    -
    -
    -def load_or_fetch_dataset(database_id, indicator):
    -    file_name = f"{database_id}.{indicator}.csv"
    -    csv_path = os.path.join("data", file_name)
    -
    -    # Try to load from CSV file
    -    if os.path.exists(csv_path):
    -        try:
    -            return pd.read_csv(csv_path)
    -        except Exception as e:
    -            print(f"Error loading CSV file: {e}")
    -
    -    # If CSV doesn't exist or couldn't be loaded, fetch from API
    -    print(f"Fetching dataset for {database_id}.{indicator} from IMF API...")
    -    dataset = imfp.imf_dataset(database_id=database_id, indicator=[indicator])
    -
    -    # Save to CSV file for future use
    -    os.makedirs("data", exist_ok=True)  # Ensure the data directory exists
    -    dataset.to_csv(csv_path, index=False)
    -    print(f"Dataset saved to {csv_path}")
    -
    -    return dataset
    -
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    Here is a brief introduction about the packages used:

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    pandas: view and manipulate data frame

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    matplotlib.pyplot: make plots

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    seaborn: make plots

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    numpy: computation

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    LinearRegression: implement linear regression

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    tabulate: format data into tables

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    statsmodels.api, adfuller, ARIMA,VAR,plot_acf,plot_pacf,mean_absolute_error,mean_squared_error, andgrangercausalitytests are specifically used for time series analysis.

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    import pandas as pd
    -import matplotlib.pyplot as plt
    -import seaborn as sns
    -import numpy as np
    -from sklearn.linear_model import LinearRegression
    -from tabulate import tabulate
    -import statsmodels.api as sm
    -from statsmodels.tsa.stattools import adfuller
    -from statsmodels.tsa.arima.model import ARIMA
    -from statsmodels.tsa.vector_ar.var_model import VAR
    -from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
    -from sklearn.metrics import mean_absolute_error
    -from sklearn.metrics import mean_squared_error
    -from statsmodels.tsa.stattools import grangercausalitytests
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    Data Fetching

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    In this section, we extracted four datasets through API calls: Gender Inequality Index(GII), GDP Deflator, Nominal GDP, and Population.

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    from pathlib import Path
    -Path("data").mkdir(exist_ok=True)
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    # Load or fetch databases
    -databases = load_or_fetch_databases()
    -
    -# Filter out databases that contain a year in the description
    -databases[
    -  ~databases['description'].str.contains(r"[\d]{4}", regex=True)
    -]
    -
    -# view_dataframe_in_browser(databases)
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    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    database_iddescription
    25HPDDHistorical Public Debt (HPDD)
    34RAFIT2AGGRevenue Administration Fiscal Information Tool...
    47GENDER_EQUALITYGender Equality
    63PGCSPrivate and Public Capital Stock Dataset
    69GENDER_BUDGETINGGender Budgeting
    125CPIConsumer Price Index (CPI)
    153IRFCLInternational Reserves and Foreign Currency Li...
    191IFS_DISCONTINUEDInternational Financial Statistics (IFS), Disc...
    192EQExport Quality
    193EDExport Diversification
    226FISCALDECENTRALIZATIONFiscal Decentralization
    291FDIFinancial Development Index
    292PSBSFADPublic Sector Balance Sheet (PSBS)(FAD)
    293UNSDG_IMF_INPUTSSustainable Development Goals, IMF Inputs
    294CPISCoordinated Portfolio Investment Survey (CPIS)
    295PCTOTCommodity Terms of Trade
    296FMFiscal Monitor (FM)
    297AFRREOSub-Saharan Africa Regional Economic Outlook (...
    298WHDREOWestern Hemisphere Regional Economic Outlook (...
    299MCDREOMiddle East and Central Asia Regional Economic...
    300APDREOAsia and Pacific Regional Economic Outlook (AP...
    301BOPAGGBalance of Payments (BOP), World and Regional ...
    302PCPSPrimary Commodity Price System (PCPS)
    303CDISCoordinated Direct Investment Survey (CDIS)
    304BOPBalance of Payments (BOP)
    305COFERCurrency Composition of Official Foreign Excha...
    306DOTDirection of Trade Statistics (DOTS)
    307RAFIT3PRA-FIT Round3 Completion and Participation Rates
    308FASFinancial Access Survey (FAS)
    309FSIFinancial Soundness Indicators (FSIs)
    310FSIREFinancial Soundness Indicators: Reporting enti...
    311BOPSDMXUSDBalance of Payments (BOP), Global SDMX (US Dol...
    312NAMAIN_IDC_NSystem of National Accounts (SNA), NA_MAIN
    313GFSRGovernment Finance Statistics (GFS), Revenue
    314GFSSSUCGovernment Finance Statistics (GFS), Statement...
    315GFSCOFOGGovernment Finance Statistics (GFS), Expenditu...
    316GFSFALCSGovernment Finance Statistics (GFS), Financial...
    317GFSIBSGovernment Finance Statistics (GFS), Integrate...
    318GFSMABGovernment Finance Statistics (GFS), Main Aggr...
    319GFSEGovernment Finance Statistics (GFS), Expense
    320IFSInternational Financial Statistics (IFS)
    321MFSMonetary and Financial Statistics (MFS)
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    Two databases were used: Gender Equality and International Financial Statistics (IFS).

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    datasets = ["GENDER_EQUALITY", "IFS"]
    -params = {}
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    -# Fetch valid parameters for two datasets
    -for dataset in datasets:
    -    params[dataset] = load_or_fetch_parameters(dataset)
    -
    -    valid_keys = list(params[dataset].keys())
    -    print(f"Parameters for {dataset}: ", valid_keys)
    -
    -# view_dataframe_in_browser(params.get("IFS").get("indicator"))
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    Parameters for GENDER_EQUALITY:  ['freq', 'ref_area', 'indicator']
    -Parameters for IFS:  ['freq', 'ref_area', 'indicator']
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    “freq” stands for Frequency, such as Annual, Monthly, or Quarterly. “ref_area” stands for Geogrpahical Area, such as US (United States), JP (Japan), and GB (United Kindom). “indicator” refers to the specific dataset in the database, including input code and description. For example, if we display all the indicators for IFS database, the GDP deflator dataset has an input code of “NGDP_D_SA_IX” with a full name description of Gross Domestic Product, Deflator, Seasonally Adjusted, Index.

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    We paired the database with the specific dataset indicator to read and store the csv file.

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    datasets = {}
    -dsets = [("GENDER_EQUALITY", "GE_GII"), ("IFS", "NGDP_D_SA_IX"), ("IFS", "NGDP_XDC"), ("IFS", "LP_PE_NUM")]
    -
    -for dset in dsets:
    -    datasets[dset[0] + "." + dset[1]] = load_or_fetch_dataset(dset[0], dset[1])
    -
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    # "Gender Inequality Index"
    -GII = "GENDER_EQUALITY.GE_GII"
    -
    -# "Gross Domestic Product, Deflator, Seasonally Adjusted, Index"
    -GDP_deflator = "IFS.NGDP_D_SA_IX"
    -
    -# "Gross Domestic Product, Nominal, Domestic Currency"
    -GDP_nominal = "IFS.NGDP_XDC"
    -
    -# "Population, Persons, Number of"
    -GDP_population = "IFS.LP_PE_NUM"
    -
    -# Assign the datasets to new variables so we don't change the originals
    -GII_data = datasets[GII]
    -GDP_deflator_data = datasets[GDP_deflator]
    -GDP_nominal_data = datasets[GDP_nominal]
    -GDP_population_data = datasets[GDP_population]
    -
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    Feature Engineering

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    Data Cleaning

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    Since the GDP deflator was reported on a quarterly basis, we converted it to a yearly basis.

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    # Filter to keep only rows with a partial string match for "Q4" in the time_period column
    -GDP_deflator_data = GDP_deflator_data[GDP_deflator_data['time_period'].str.contains("Q4")]
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    # Split the time_period into year and quarter and keep the year only
    -GDP_deflator_data.loc[:, 'time_period'] = GDP_deflator_data['time_period'].str[0:4]
    -
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    We made all the variables numeric.

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    datasets = [GII_data, GDP_deflator_data, GDP_nominal_data, GDP_population_data]
    -
    -for i, dataset in enumerate(datasets):    
    -    # Use .loc to modify the columns
    -    datasets[i].loc[:, 'obs_value'] = pd.to_numeric(datasets[i]['obs_value'], errors='coerce')
    -    datasets[i].loc[:, 'time_period'] = pd.to_numeric(datasets[i]['time_period'], errors='coerce')
    -    datasets[i].loc[:, 'unit_mult'] = pd.to_numeric(datasets[i]['unit_mult'], errors='coerce')
    -
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    GII Percent Change: Dependent Variable

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    We kept percents as decimals to make them easy to work with for calculation. Different countries have different baseline level of economic growth and gender equality. We calculated the percent change to make them comparable.

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    # Calculate percent change for each ref_area
    -GII_data[f'pct_change'] = GII_data.sort_values(['ref_area', 'time_period']).groupby('ref_area')['obs_value'].pct_change()
    -
    -# Display the first few rows of the updated dataset
    -GII_data.head()
    -
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    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    freqref_areaindicatorunit_multtime_formattime_periodobs_valuepct_change
    0AAFGE_GII0P1Y19900.828244NaN
    1AAFGE_GII0P1Y19910.817706-0.012723
    2AAFGE_GII0P1Y19920.809806-0.009662
    3AAFGE_GII0P1Y19930.803078-0.008308
    4AAFGE_GII0P1Y19940.797028-0.007533
    - -
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    We subset the data frame to keep only the columns we want:

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    # Create a new dataframe with only the required columns
    -GII_data = GII_data[['ref_area', 'time_period', 'pct_change']].copy()
    -
    -# Display the first few rows of the new dataset
    -GII_data.head()
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    ref_areatime_periodpct_change
    0AF1990NaN
    1AF1991-0.012723
    2AF1992-0.009662
    3AF1993-0.008308
    4AF1994-0.007533
    - -
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    GDP: Independent Variable

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    We kept the columns we want only for GDP-related datasets for easier table merging.

    -
    -
    # GDP Deflator Dataset
    -# Create a new dataframe with only the required columns
    -GDP_deflator_data = GDP_deflator_data[['ref_area', 'time_period', 'unit_mult', 'obs_value']].copy()
    -
    -# Divide obs_value by 100
    -GDP_deflator_data['obs_value'] = GDP_deflator_data['obs_value'] / 100
    -
    -# Display the first few rows of the new dataset
    -GDP_deflator_data.head()
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    ref_areatime_periodunit_multobs_value
    3FI199000.732006
    7FI199100.739841
    11FI199200.746543
    15FI199300.756193
    19FI199400.779373
    - -
    -
    -
    -
    -
    # GDP Nominal Data
    -# Create a new dataframe with only the required columns
    -GDP_nominal_data = GDP_nominal_data[['ref_area', 'time_period', 'unit_mult','obs_value']].copy()
    -
    -# Display the first few rows of the new dataset
    -GDP_nominal_data.head()
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    ref_areatime_periodunit_multobs_value
    0NE200562418864.0
    1NE200662596972.0
    2NE200762762961.0
    3NE200863247835.0
    4NE200963403683.0
    - -
    -
    -
    -
    -
    # GDP Population Data 
    -# Create a new dataframe with only the required columns
    -GDP_population_data = GDP_population_data[['ref_area', 'time_period', 'unit_mult','obs_value']].copy()
    -
    -# Display the first few rows of the new dataset
    -GDP_population_data.head()
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    ref_areatime_periodunit_multobs_value
    0GA19503473.296
    1GA19513476.381
    2GA19523478.655
    3GA19533480.536
    4GA19543482.332
    - -
    -
    -
    -
    -
    # Combine all the datasets above for further calculation
    -merged_df = pd.merge(pd.merge(GDP_deflator_data,GDP_nominal_data, on=['time_period', 'ref_area'], suffixes=('_index', '_nominal'), how='inner'), GDP_population_data, on=['time_period', 'ref_area'], how='inner')
    -
    -# Display the GDP data in a web browser
    -# view_dataframe_in_browser(merged_df)
    -
    -# Display the first few rows of the dataset
    -merged_df.head()
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    ref_areatime_periodunit_mult_indexobs_value_indexunit_mult_nominalobs_value_nominalunit_multobs_value
    0FI199000.732006690964.034996.220
    1FI199100.739841686913.035019.134
    2FI199200.746543684786.035044.928
    3FI199300.756193685610.035071.782
    4FI199400.779373690646.035097.090
    - -
    -
    -
    -

    We want to adjust GDP data based on unit multiplier. Unit multiplier stands for the number of zeroes we need to add to the value column. For example, in 1950, the observed population data for country GA (Georgia) was 473.296. With a unit muliplier of 3, the adjusted population would be 473296.

    -
    -
    merged_df['adjusted_index'] = merged_df['obs_value_index'] * (10 ** (merged_df['unit_mult_index']))
    -merged_df['adjusted_nominal'] = merged_df['obs_value_nominal'] * (10 ** (merged_df['unit_mult_nominal']))
    -merged_df['adjusted_population'] = merged_df['obs_value'] * (10 ** (merged_df['unit_mult']))
    -
    -
    -
    # Merged dataset
    -# Create a new dataframe with only the required columns
    -merged_df = merged_df[['ref_area', 'time_period','adjusted_nominal', 'adjusted_index', 'adjusted_population']].copy()
    -
    -# Display the first few rows of the dataset
    -merged_df.head()
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    ref_areatime_periodadjusted_nominaladjusted_indexadjusted_population
    0FI19909.096400e+100.7320064996220.0
    1FI19918.691300e+100.7398415019134.0
    2FI19928.478600e+100.7465435044928.0
    3FI19938.561000e+100.7561935071782.0
    4FI19949.064600e+100.7793735097090.0
    - -
    -
    -
    -

    We wanted to compute the Real GDP per capita.

    -
    -
    # Step 1: Real GDP = (Nominal GDP / GDP Deflator Index)
    -merged_df['Real_GDP_domestic'] = (merged_df['adjusted_nominal'] / merged_df['adjusted_index'])
    -
    -# Step 2: Real GDP per Capita = Real GDP / Population
    -merged_df['Real_GDP_per_capita'] = merged_df['Real_GDP_domestic'] / merged_df['adjusted_population']
    -
    -# Check the results
    -merged_df.head()
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    ref_areatime_periodadjusted_nominaladjusted_indexadjusted_populationReal_GDP_domesticReal_GDP_per_capita
    0FI19909.096400e+100.7320064996220.01.242667e+1124872.143699
    1FI19918.691300e+100.7398415019134.01.174753e+1123405.490395
    2FI19928.478600e+100.7465435044928.01.135715e+1122512.011198
    3FI19938.561000e+100.7561935071782.01.132119e+1122321.919259
    4FI19949.064600e+100.7793735097090.01.163063e+1122818.181344
    - -
    -
    -
    -

    We calculated the percentage change in Real GDP per capita and put it in a new column.

    -
    -
    # Calculate percent change for each ref_area
    -merged_df[f'GDP_change'] = merged_df.sort_values(['ref_area', 'time_period']).groupby('ref_area')['Real_GDP_per_capita'].pct_change()
    -
    -# Rename dataset
    -GDP_data = merged_df
    -
    -# Display the first few rows of the dataset
    -GDP_data.head()
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    ref_areatime_periodadjusted_nominaladjusted_indexadjusted_populationReal_GDP_domesticReal_GDP_per_capitaGDP_change
    0FI19909.096400e+100.7320064996220.01.242667e+1124872.143699NaN
    1FI19918.691300e+100.7398415019134.01.174753e+1123405.490395-0.058968
    2FI19928.478600e+100.7465435044928.01.135715e+1122512.011198-0.038174
    3FI19938.561000e+100.7561935071782.01.132119e+1122321.919259-0.008444
    4FI19949.064600e+100.7793735097090.01.163063e+1122818.1813440.022232
    - -
    -
    -
    -
    -
    # GII and GDP
    -# Merge the datasets
    -combined_data = pd.merge(GII_data, GDP_data, on=["ref_area", "time_period"], how = "inner")
    -
    -# Check the combined dataset
    -combined_data.head()
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    ref_areatime_periodpct_changeadjusted_nominaladjusted_indexadjusted_populationReal_GDP_domesticReal_GDP_per_capitaGDP_change
    0AR2004-0.0218964.851153e+110.40967238491970.01.184155e+1230763.687776NaN
    1AR2005-0.0085815.825382e+110.47010838892924.01.239159e+1231860.7727760.035662
    2AR20060.0073797.159043e+110.52653539289876.01.359651e+1234605.6280840.086152
    3AR2007-0.0201178.969801e+110.62913939684303.01.425727e+1235926.7345750.038176
    4AR20080.0007331.149646e+120.75775340080159.01.517178e+1237853.6002520.053633
    - -
    -
    -
    -
    -
    # GII and GDP
    -# Rename columns
    -combined_data = combined_data.rename(columns = {
    -    "ref_area": "Country",
    -    "time_period": "Time",
    -    "pct_change": "GII_change"
    -})
    -
    -# Display the GDP data in a web browser
    -# view_dataframe_in_browser(combined_data)
    -
    -# Check the combined dataset
    -combined_data.head()
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    CountryTimeGII_changeadjusted_nominaladjusted_indexadjusted_populationReal_GDP_domesticReal_GDP_per_capitaGDP_change
    0AR2004-0.0218964.851153e+110.40967238491970.01.184155e+1230763.687776NaN
    1AR2005-0.0085815.825382e+110.47010838892924.01.239159e+1231860.7727760.035662
    2AR20060.0073797.159043e+110.52653539289876.01.359651e+1234605.6280840.086152
    3AR2007-0.0201178.969801e+110.62913939684303.01.425727e+1235926.7345750.038176
    4AR20080.0007331.149646e+120.75775340080159.01.517178e+1237853.6002520.053633
    - -
    -
    -
    -
    -
    -
    -

    Data Visualization

    -
    -

    Scatterplot

    -

    Scatterplot use dots to represent values of two numeric variables. The horizontal axis was the percent change in Real GDP per capita. The vertical axis was the percent change in Gender Inequality Index(GII). Different colors represented different countries. We used a linear regression line to display the overall pattern.

    -

    Based on the scatterplot, it seemed like there was a slight positive relationship between GDP change and GII change as shown by the flat regression line. Gender inequality was decreasing (gender equality was improving) a little faster in country-years with low GDP growth and a little slower in country-years with high GDP growth.

    -
    -
    # Drop NAs
    -combined_data = combined_data.dropna(subset=['GDP_change', 'GII_change'])
    -
    -# Plot the data points
    -plt.figure(figsize=(18, 10))
    -for country in combined_data['Country'].unique():
    -    country_data = combined_data[combined_data['Country'] == country]
    -    plt.scatter(country_data['GDP_change'], country_data['GII_change'],
    -             marker='o',linestyle='-', label=country)
    -plt.title('Country-Year Analysis of GDP Change vs. GII Change')
    -plt.xlabel('Percent Change in Real GDP per Capita (Country-Year)')
    -plt.ylabel('Percent Change in GII (Country-Year)')
    -plt.grid(True)
    -
    -# Prepare data for linear regression
    -X = combined_data['GDP_change'].values.reshape(-1, 1)
    -y = combined_data['GII_change'].values
    -
    -# Perform linear regression
    -reg = LinearRegression().fit(X, y)
    -y_pred = reg.predict(X)
    -
    -# Plot the regression line
    -plt.plot(combined_data['GDP_change'], y_pred, color='red', linewidth=2)
    -
    -plt.show()
    -
    -
    -
    -

    -
    -
    -
    -
    -
    -
    -

    Time Series Line Plot

    -

    We created separate line plots for GDP change and GII change over time for a few key countries might show the trends more clearly.

    -

    US: United States

    -

    JP: Japan

    -

    GB: United Kindom

    -

    FR: France

    -

    MX: Mexico

    -

    Based on the line plots, we saw GDP change and GII change have different patterns. For example, in Mexico, when there was a big change in real GDP per captia in 1995, the change in GII was pretty stable.

    -
    -
    # Time Series Line plot for a few key countries
    -selected_countries  = ['US', 'JP', 'GB', 'FR', 'MX']
    -combined_data_selected = combined_data[combined_data['Country'].isin(selected_countries)]
    -
    -# Set up the Plot Structure
    -fig, ax = plt.subplots(2, 1, figsize=(18, 10), sharex=True)
    -
    -# Plot change in real GDP per capita over time
    -sns.lineplot(data = combined_data_selected, x = "Time", y = "GDP_change", hue = "Country", ax = ax[0])
    -ax[0].set_title("Percent Change in Real GDP per Capita Over Time")
    -ax[0].set_ylabel("Percent Change in Real GDP per Capita")
    -
    -# Plot change in GII over time
    -sns.lineplot(data = combined_data_selected, x = "Time", y = "GII_change", hue = "Country", ax = ax[1])
    -ax[1].set_title("Percent Change in GII over Time")
    -ax[1].set_xlabel("Time")
    -ax[1].set_ylabel("GII")
    -
    -plt.tight_layout
    -plt.show()
    -
    -
    -
    -

    -
    -
    -
    -
    -
    -
    -

    Barplot

    -

    We used a barplot to show average changes in GII and GDP percent change for each country to visualize regions where inequality was improving or worsening.

    -

    This plot supported our previous observation how GII change seemed to be not be correlated with GDP change. We also saw that, for country SI, Solvenia, there seems to be a large improvement in gender inequality.

    -
    -
    # Barplot using average GII and GDP change
    -# Calculate average change for each country
    -combined_data_avg = combined_data.groupby('Country')[['GII_change','GDP_change']].mean().reset_index()
    -
    -# Prepare to plot structure 
    -plt.figure(figsize = (18,10))
    -
    -# Create the barplot
    -combined_data_avg.plot(kind = 'bar', x = 'Country')
    -plt.ylabel('Average Change')
    -plt.xlabel('Country')
    -plt.legend(['GII change', 'GDP change'])
    -plt.grid(axis = 'y')
    -
    -# Show the plot
    -plt.show()
    -
    -
    <Figure size 1728x960 with 0 Axes>
    -
    -
    -
    -
    -

    -
    -
    -
    -
    -
    -
    -

    Boxplot

    -

    We used boxplot to visualize the distribution of GDP and GII change by country, providing information about spread, median, and potential outliers. To provide a more informative view, we sequenced countries in an ascending order by the median of percent change in GDP.

    -

    The boxplot displayed a slight upward trend with no obvious pattern between GDP and GII change. In coutries with higher GDP change median, they also tend to have a larger spread of the GDP change. The median of GII change remained stable regardless of the magnitude of GDP change, implying weak or no association between GDP and GII change. We observed a potential outlier for country SI, Solvenia, which may explained its large improvement in Gender inequality.

    -
    -
    # Box plot for GII and GDP change
    -# Melt the dataframe to long format for combined boxplot
    -combined_data_melted = combined_data.melt(id_vars=['Country'], value_vars=['GII_change', 'GDP_change'], 
    -                                          var_name='Change_Type', value_name='Value')
    -
    -gdp_medians = combined_data.groupby('Country')['GDP_change'].median().sort_values()
    -
    -combined_data_melted['Country'] = pd.Categorical(combined_data_melted['Country'], categories=gdp_medians.index, ordered= True)
    -
    -# Prepare the plot structure
    -plt.figure(figsize=(18, 10))
    -sns.boxplot(data = combined_data_melted, x = "Country", y = 'Value', hue = 'Change_Type')
    -plt.title('Distribution of GII and GDP change by Country')
    -plt.xlabel('Country')
    -plt.ylabel('Change')
    -plt.legend(title = 'Change Type')
    -
    -# Show the plot
    -plt.show()
    -
    -
    -
    -

    -
    -
    -
    -
    -
    -
    -

    Correlation Matrix

    -

    We created a heatmap to show the relationship between GII and GDP change.

    -

    A positive correlation coefficient indicates a positive relationship: the larger the GDP change, the larger the GII change. A negative correlation coefficient indicates a negative relationship: the larger the GDP change, the smaller the GII change. A correlation coefficient closer to 0 indicates there is weak or no relationship.

    -

    Based on the numeric values in the plot, there was a moderately strong positive correlation between GII and GDP change for country Estonia(EE) and Ireland(IE).

    -
    -
    # Calculate the correlation
    -country_correlation = combined_data.groupby('Country')[['GII_change', 'GDP_change']].corr().iloc[0::2, -1].reset_index(name='Correlation')
    -
    -# Put the correlation value in a matrix format
    -correlation_matrix = country_correlation.pivot(index='Country', columns='level_1', values='Correlation')
    -
    -# Check for NaN values in the correlation matrix
    -correlation_matrix.fillna(0, inplace=True)  # Replace NaNs with 0 or another value as appropriate
    -
    -# Set up the plot structure
    -plt.figure(figsize=(18, 12))  # Adjust height to give more space for y-axis labels
    -
    -# Plot the heatmap
    -sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0, cbar_kws={"shrink": .8}, linewidths=.5)
    -
    -# Enhance axis labels and title
    -plt.title('Heatmap for GII and GDP Change', fontsize=20)
    -plt.xlabel('Variables', fontsize=16)
    -plt.ylabel('Country', fontsize=16)
    -
    -# Improve readability of y-axis labels
    -plt.yticks(fontsize=12)  # Adjust the font size for y-axis labels
    -
    -# Show the plot
    -plt.show()
    -
    -
    -
    -

    -
    -
    -
    -
    -
    -
    -
    -

    Statistical Analysis

    -
    -

    Descriptive Statistics

    -

    There was a total of 915 data points. The mean of the GII change in -0.0314868, which indicated the overall grand mean percent change in gender inequality index is -3.15%. The mean of the GDP change was 0.0234633, showing the overall grand mean percent change in real GDP per capita was 2.35%.

    -
    -
    # Generate summary statistics
    -descriptive_stats = combined_data.describe()
    -print(tabulate(descriptive_stats, headers='keys', tablefmt='github'))
    -
    -
    |       |   GII_change |   adjusted_nominal |   adjusted_index |   adjusted_population |   Real_GDP_domestic |   Real_GDP_per_capita |   GDP_change |
    -|-------|--------------|--------------------|------------------|-----------------------|---------------------|-----------------------|--------------|
    -| count | 914          |      914           |      914         |         914           |       914           |         914           | 914          |
    -| mean  |  -0.0314506  |        7.85541e+13 |        0.855504  |           4.50061e+07 |         7.60737e+13 |           1.00595e+06 |   0.023532   |
    -| std   |   0.054783   |        6.01311e+14 |        0.210057  |           1.24493e+08 |         5.52717e+14 |           3.88155e+06 |   0.0418127  |
    -| min   |  -0.755003   |        2.18714e+09 |        0.0360636 |      396324           |         5.21733e+09 |        1798.42        |  -0.285847   |
    -| 25%   |  -0.0424105  |        1.07655e+11 |        0.743355  |           5.0737e+06  |         1.48206e+11 |       17176           |   0.00384809 |
    -| 50%   |  -0.020248   |        7.50706e+11 |        0.888883  |           1.0337e+07  |         9.94933e+11 |       35401           |   0.0222486  |
    -| 75%   |  -0.00451301 |        2.5911e+12  |        1.00175   |           4.5159e+07  |         2.9412e+12  |      120391           |   0.0427318  |
    -| max   |   0.156992   |        9.54613e+15 |        2.0788    |           1.28084e+09 |         7.92007e+15 |           3.14532e+07 |   0.241984   |
    -
    -
    -
    -
    -

    Regression Analysis

    -

    Simple linear regression as a foundational approach provide us with a basic understanding of the relationship between GDP change and GII change.

    -

    Based on the summary, we concluded the following:

    -
      -
    • Becasue p-value = 0.057, if we set alpha, the significance level, to be 0.05, we failed to reject the null hypothesis and conclude there was no significant relationship between percent change in real GDP per capita and gender inequality index.

    • -
    • R-squared = 0.004. Only 0.4% of the variance in GII change could be explained by GDP change.

    • -
    • We were 95% confident that the interval from -0.003 to 0.169 captured the true slope of GDP change. Because 0 was included, we are uncertain about the effect of GDP change on GII chnage.

    • -
    -
    -
    # Define independent and depenent variables
    -X = combined_data['GDP_change']
    -y = combined_data['GII_change']
    -
    -# Add a constant to indepdent variable to include an intercept
    -X = sm.add_constant(X)
    -
    -# Fit a simple linear regresion model and print out the summary
    -model = sm.OLS(y, X).fit()
    -print(model.summary())
    -
    -
                                OLS Regression Results                            
    -==============================================================================
    -Dep. Variable:             GII_change   R-squared:                       0.004
    -Model:                            OLS   Adj. R-squared:                  0.003
    -Method:                 Least Squares   F-statistic:                     3.551
    -Date:                Wed, 25 Dec 2024   Prob (F-statistic):             0.0598
    -Time:                        18:00:11   Log-Likelihood:                 1360.0
    -No. Observations:                 914   AIC:                            -2716.
    -Df Residuals:                     912   BIC:                            -2706.
    -Df Model:                           1                                         
    -Covariance Type:            nonrobust                                         
    -==============================================================================
    -                 coef    std err          t      P>|t|      [0.025      0.975]
    -------------------------------------------------------------------------------
    -const         -0.0334      0.002    -16.069      0.000      -0.037      -0.029
    -GDP_change     0.0816      0.043      1.885      0.060      -0.003       0.167
    -==============================================================================
    -Omnibus:                      841.660   Durbin-Watson:                   1.384
    -Prob(Omnibus):                  0.000   Jarque-Bera (JB):            53999.190
    -Skew:                          -3.938   Prob(JB):                         0.00
    -Kurtosis:                      39.823   Cond. No.                         23.9
    -==============================================================================
    -
    -Notes:
    -[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
    -
    -
    -
    -
    -

    Time Series Analysis

    -

    Time series analysis allows us to explore how the relationship between GII and GDP change vary across different time periods, accounting for lagged effects.

    -

    Here was a quick summary of the result: * Both GII and GDP change time series were stationary.

    -
      -
    • Past GII change values significantly influenced cuurent GII change values.

    • -
    • VAR model had good model performance on forecasting future values based on historical data.

    • -
    • Changes in GDP did not cause/precde the changes in GII.

    • -
    -
    -

    ADF Test: Stationality Assumption Check

    -

    We wanted to use Augmented Dickey-Fuller (ADF) test to check whether a time series was stationary, which was the model assumption for many time series models.

    -

    Stationarity implied constant mean and variance over time, making it more predictable and stable for forecasting.

    -

    Based on the ADF test output, both GII and GDP change time series were stationary. We proceeded to the time series modeling section.

    -
    -
    # Augmented Dickey-Fuller (ADF) test for stationarity check
    -# Create melted datasets
    -combined_data_time = combined_data.melt(id_vars=['Time', 'Country'], value_vars=['GII_change','GDP_change'], var_name = 'Change_Type', value_name = 'Value')
    -GII = combined_data_time[(combined_data_time['Change_Type'] == 'GII_change')]                         
    -
    -GDP = combined_data_time[(combined_data_time['Change_Type'] == 'GDP_change')]
    -
    -# Stationary Check
    -def adf_test(series):
    -    result = adfuller(series.dropna())
    -    print(f'ADF Statistic: {result[0]}')
    -    print(f'p-value: {result[1]}')
    -    if result[1] < 0.05:
    -        print("Series is stationary")
    -    else:
    -        print("Series is not stationary")
    -
    -# Output the result
    -adf_test(GII['Value'])
    -adf_test(GDP['Value'])
    -
    -
    ADF Statistic: -16.069430375623295
    -p-value: 5.527565277907626e-29
    -Series is stationary
    -ADF Statistic: -8.535683414310181
    -p-value: 1.0095278443825336e-13
    -Series is stationary
    -
    -
    -
    -
    -

    VAR model: Examine variables separately

    -

    We fitted a VAR (Vector Autoreression) model to see the relationship between GII and GDP change. VAR is particularly useful when dealing with multivariate time series data and allows us to examine the interdependence between variables.

    -

    Based on summary, here were several interpretations we could make:

    -
      -
    • We used AIC as the criteria for model selection. Lower value suggests a better fit.

    • -
    • Given that we wanted to predict GII change, we focused on the first set “Results for equation GII_change.”

    • -
    • Past GII_change values significantly influenced current GII_change, as shown in the small p-values of lags 1 and 2.

    • -
    • Lag 2 of GDP_change had a relatively low p-value but is not statistically significant.

    • -
    -
    -
    # Split the dataset into training and testing sets
    -split_ratio = 0.7
    -split_index = int(len(combined_data) * split_ratio)
    -
    -# Training set is used to fit the model
    -train_data = combined_data.iloc[:split_index]
    -
    -# Testing set is used for validation
    -test_data = combined_data.iloc[split_index:]
    -
    -print(f"Training data: {train_data.shape}")
    -print(f"Test data: {test_data.shape}")
    -
    -
    Training data: (639, 9)
    -Test data: (275, 9)
    -
    -
    -
    -
    # Fit a VAR model 
    -time_model = VAR(train_data[['GII_change', 'GDP_change']])
    -time_model_fitted = time_model.fit(maxlags = 15, ic="aic")
    -
    -# Print out the model summary
    -print(time_model_fitted.summary())
    -
    -
      Summary of Regression Results   
    -==================================
    -Model:                         VAR
    -Method:                        OLS
    -Date:           Wed, 25, Dec, 2024
    -Time:                     18:00:11
    ---------------------------------------------------------------------
    -No. of Equations:         2.00000    BIC:                   -12.2270
    -Nobs:                     636.000    HQIC:                  -12.2869
    -Log likelihood:           2128.47    FPE:                4.43927e-06
    -AIC:                     -12.3250    Det(Omega_mle):     4.34314e-06
    ---------------------------------------------------------------------
    -Results for equation GII_change
    -================================================================================
    -                   coefficient       std. error           t-stat            prob
    ---------------------------------------------------------------------------------
    -const                -0.023760         0.003176           -7.481           0.000
    -L1.GII_change         0.211342         0.039900            5.297           0.000
    -L1.GDP_change        -0.015713         0.046080           -0.341           0.733
    -L2.GII_change         0.111672         0.040402            2.764           0.006
    -L2.GDP_change         0.088293         0.046452            1.901           0.057
    -L3.GII_change        -0.021861         0.039774           -0.550           0.583
    -L3.GDP_change         0.000651         0.046141            0.014           0.989
    -================================================================================
    -
    -Results for equation GDP_change
    -================================================================================
    -                   coefficient       std. error           t-stat            prob
    ---------------------------------------------------------------------------------
    -const                 0.014585         0.002720            5.362           0.000
    -L1.GII_change         0.004128         0.034171            0.121           0.904
    -L1.GDP_change         0.141566         0.039464            3.587           0.000
    -L2.GII_change        -0.009734         0.034600           -0.281           0.778
    -L2.GDP_change         0.044282         0.039782            1.113           0.266
    -L3.GII_change        -0.037787         0.034063           -1.109           0.267
    -L3.GDP_change         0.143870         0.039516            3.641           0.000
    -================================================================================
    -
    -Correlation matrix of residuals
    -              GII_change  GDP_change
    -GII_change      1.000000    0.040992
    -GDP_change      0.040992    1.000000
    -
    -
    -
    -
    -
    -
    /Users/sammi/Library/Caches/pypoetry/virtualenvs/imfp-0AlIckOY-py3.13/lib/python3.13/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: An unsupported index was provided. As a result, forecasts cannot be generated. To use the model for forecasting, use one of the supported classes of index.
    -  self._init_dates(dates, freq)
    -
    -
    -
    -
    -

    VAR Model: Forecasting

    -

    We applied the model learned above to the test data. Based on the plot, the forecast values seem to follow the actual data well, indicating a good model fit caputuring the underlying trends.

    -
    -
    # Number of steps to forecast (length of the test set)
    -n_steps = len(test_data)
    -
    -# Get the last values from the training set for forecasting
    -forecast_input = train_data[['GII_change', 'GDP_change']].values[-time_model_fitted.k_ar:]
    -
    -# Forecasting
    -forecast = time_model_fitted.forecast(y=forecast_input, steps=n_steps)
    -
    -# Create a DataFrame for the forecasted values
    -forecast_df = pd.DataFrame(forecast, index=test_data.index, columns=['GII_forecast', 'GDP_forecast'])
    -
    -# Ensure the index of the forecast_df matches the test_data index
    -forecast_df.index = test_data.index
    -
    -
    -
    plt.figure(figsize=(12, 6))
    -plt.plot(train_data['GII_change'], label='Training GII', color='blue')
    -plt.plot(test_data['GII_change'], label='Actual GII', color='orange')
    -plt.plot(forecast_df['GII_forecast'], label='Forecasted GII', color='green')
    -plt.title('GII Change Forecast vs Actual')
    -plt.legend()
    -plt.show()
    -
    -plt.figure(figsize=(12, 6))
    -plt.plot(train_data['GDP_change'], label='Training GDP', color='blue')
    -plt.plot(test_data['GDP_change'], label='Actual GDP', color='orange')
    -plt.plot(forecast_df['GDP_forecast'], label='Forecasted GDP', color='green')
    -plt.title('GDP Change Forecast vs Actual')
    -plt.legend()
    -plt.show()
    -
    -
    -
    -

    -
    -
    -
    -
    -
    -
    -

    -
    -
    -
    -
    -
    -
    -

    VAR Model: Model Performance

    -

    Low values of both MAE and RMSE indicate good model performance with small average errors in predictions.

    -
    -
    mae_gii = mean_absolute_error(test_data['GII_change'], forecast_df['GII_forecast'])
    -mae_gdp = mean_absolute_error(test_data['GDP_change'], forecast_df['GDP_forecast'])
    -
    -print(f'Mean Absolute Error for GII: {mae_gii}')
    -print(f'Mean Absolute Error for GDP: {mae_gdp}')
    -
    -
    Mean Absolute Error for GII: 0.03296113056900485
    -Mean Absolute Error for GDP: 0.027636232780964363
    -
    -
    -
    -
    rmse_gii = np.sqrt(mean_squared_error(test_data['GII_change'], forecast_df['GII_forecast']))
    -rmse_gdp = np.sqrt(mean_squared_error(test_data['GDP_change'], forecast_df['GDP_forecast']))
    -
    -print(f'RMSE for GII: {rmse_gii}')
    -print(f'RMSE for GDP: {rmse_gdp}')
    -
    -
    RMSE for GII: 0.06284754560659174
    -RMSE for GDP: 0.038429216092797085
    -
    -
    -
    -
    -

    VAR Model: Granger causality test

    -

    Granger causality test evaluates whether one time series can predict another.

    -

    Based on the output, the lowest p-value is when lag = 2. However, because p-value > 0.05, we fail to reject the null hypothesis and conclude the GDP_change does not Granger-cause the GII_change.

    -
    -
    # Perform the Granger causality test
    -max_lag = 3
    -test_result = grangercausalitytests(train_data[['GII_change', 'GDP_change']], max_lag, verbose=True)
    -
    -
    
    -Granger Causality
    -number of lags (no zero) 1
    -ssr based F test:         F=0.0000  , p=0.9972  , df_denom=635, df_num=1
    -ssr based chi2 test:   chi2=0.0000  , p=0.9972  , df=1
    -likelihood ratio test: chi2=0.0000  , p=0.9972  , df=1
    -parameter F test:         F=0.0000  , p=0.9972  , df_denom=635, df_num=1
    -
    -Granger Causality
    -number of lags (no zero) 2
    -ssr based F test:         F=1.8529  , p=0.1576  , df_denom=632, df_num=2
    -ssr based chi2 test:   chi2=3.7351  , p=0.1545  , df=2
    -likelihood ratio test: chi2=3.7242  , p=0.1553  , df=2
    -parameter F test:         F=1.8529  , p=0.1576  , df_denom=632, df_num=2
    -
    -Granger Causality
    -number of lags (no zero) 3
    -ssr based F test:         F=1.2359  , p=0.2958  , df_denom=629, df_num=3
    -ssr based chi2 test:   chi2=3.7489  , p=0.2899  , df=3
    -likelihood ratio test: chi2=3.7379  , p=0.2912  , df=3
    -parameter F test:         F=1.2359  , p=0.2958  , df_denom=629, df_num=3
    -
    -
    -
    /Users/sammi/Library/Caches/pypoetry/virtualenvs/imfp-0AlIckOY-py3.13/lib/python3.13/site-packages/statsmodels/tsa/stattools.py:1556: FutureWarning: verbose is deprecated since functions should not print results
    -  warnings.warn(
    -
    -
    -
    -
    -
    -
    -

    Conclusion

    -

    In wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research. Check out the blog for full analysis!

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    Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data

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    Jenny Xu

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    December 25, 2024

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    Introduction Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders? Our main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables. Ready for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!

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    Data Preparation ​​To explore how economic growth connects with gender equality, we turned to the Imfp library to access a wealth of global economic data, ready to analyze through some straightforward API calls. Let’s break down our two main variables of interest. Here’s the lowdown on the 4 key data series we pulled together:

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    Dependent Variable: Percent Change in Gender Inequality Index (GII) Our gender equality measure, the Gender Inequality Index (GII), is a composite score that reflects inequality in three key areas: reproductive health, empowerment, and labor market, ranging from 0 (equality) to 1 (inequality). We calculated the percent change in GII over time to make it easier to compare GII change across countries with different baseline levels of inequality.

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    Independent Variable: Percent Change in Real GDP per capita Our economic growth measure is real GDP per capita, an indicator of a country’s economic output per person, adjusted for inflation. The higher the real GDP per capita, the wealthier and more prosperous a country is. Here is a peek at the formula:

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    Data Visualization Scatterplot

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    Nothing like a good scatterplot to help us understand the relationship between two numbers, right? For our project, the scatterplot lets us visually explore how Percent Change in Real GDP per capita relates to Percent Change in GII. Each dot on the plot represents a country-year combo. The gentle upward slope suggests that in years of low GDP growth, gender inequality tends to decrease a bit faster, while in years of high GDP growth, the inequality drops at a slightly slower rate. It’s a subtle clue, but slower-growing economies might be making slightly quicker strides in gender equality. Time Series Line plot

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    The time series line plot gives us the big picture of GDP and GII changes over time across a few select countries, including the United States, Japan, the United Kingdom, France, and Mexico. Mexico, for example, saw great GDP swings in 1995, yet its GII stayed relatively stable. Meanwhile, Japan and France saw some dramatic GII shifts. These fluctuations leave us wondering: what historical events sparked these changes? We may need to take a deeper look into each country’s unique timeline to find the answers.

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    Boxplot

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    Next, we have the boxplot to help us visualize the distribution. By sequencing countries in ascending order of median GDP change, we see an interesting trend: countries with higher median GDP growth also seem to show more variability in their GDP changes. The GII change, on the other hand, keeps a relatively stable median, hinting at a weaker association with GDP growth.

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    Heatmap

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    Finally, our heatmap wraps it all up with a quick, intuitive look at correlations between GDP and GII across countries. In countries like Estonia (EE) and Ireland (IE), Australia (AT), South Africa (ZA), Thailand (TH), Slovenia (SI), we see positive correlations in warm colors, indicating that as GDP growth rates increase, GII growth rates tend to rise as well. For countries like Cyprus (CY), Romania (RO), and the United States (US), we see cooler colors, representing negative correlations: as GDP growth rate rises, GII growth rates decrease, suggesting an improvement in gender equality alongside economic growth. This color-coded comparison leaves us curious—could the distinction between developed and developing countries play a role in these relationships? It’s a question that might inspire future research directions!

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    Statistical Analysis Descriptive Statistics Let’s start with a quick overview of our descriptive statistics: our dataset includes 915 data points. On average, GII change is -3.15%, indicating an overall reduction in gender inequality over time. Meanwhile, GDP change averages 2.35%, hinting at modest economic growth. A full breakdown of these values can be found in the table below for more detail.

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    Regression Analysis To get a sense of any relationship between GDP change and GII change, we ran a simple linear regression. Here’s what we found: P-value: With a p-value of 0.057, we fail to reject the null hypothesis. There is no statistically significant relationship. In other words, we don’t find strong evidence of a link between GDP and GII changes. R-squared: GDP change explains only 0.4% of the variation in GII change—a pretty minimal effect. 95% Confidence Interval: We’re 95% confident the interval from -0.003 to 0.169 captures the true slope. The effect of GDP change on GII is uncertain as 0 is within this range.

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    Time Series Analysis Time series analysis allows us to dig deeper into how GII and GDP changes play out over time—and potentially influence one another. Here’s the rundown: Stationarity check: Both GII and GDP change series are stationary, meaning their means and variances are stable over time, setting us up for reliable forecasting. VAR Model (Vector Autoregression): This multivariate model lets us explore how past values in both series relate to current values. Notably, past GII changes significantly impact current GII changes. Model Performance: The VAR model has good fit, as shown by the forecast closely tracking actual values. Low MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) confirm that our model predicts GII and GDP change with minimal error. Check out the plots and table below! Granger Causality Test: Curious whether GDP changes can predict changes in GII? The Granger causality test says no. With p-values above 0.05, we fail to find evidence that GDP changes drive GII changes. This suggests that while both variables evolve over time, they don’t seem to directly influence each other.

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    Conclusion In wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research. In terms of the Imfp library, we gained valuable experience working with real-world economic indicators like GDP and the GII. The library’s function in fetching databases, parameters, and datasets made it straightforward to access IMF’s data, allowing us to focus on analysis instead. The integration of other Python tools—such as seaborn, matplotlib, and numpy—not only streamlined our computational processes but also ensured consistency across the project. Future Directions First, we must consider what other factors might influence the relationship between GDP and GII change. The GII is a composite index, shaped by a myriad of social factors, including cultural norms, legal frameworks, and environmental shifts. Future studies could benefit from incorporating additional predictors into the analysis and exploring the interaction between economic growth and gender equality within specific country contexts. Second, there’s potential to enhance the predictive power of our Vector Autoregression (VAR) time series model. While we established that GDP change does not cause GII change, our model performed well in forecasting trends for both variables independently. In practice, policymakers may want to forecast GII trends independently of GDP if they are implementing gender-focused policies. Future research could investigate time series modeling to further unravel the dynamics of GII and GDP changes. So, as we wrap up this chapter, let’s keep our curiosity alive and our questions flowing. After all, every end is just a new beginning in the quest for knowledge!

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    Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data

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    Data Analysis
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    Author
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    Jenny Xu

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    December 25, 2024

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    Introduction

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    Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders?

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    Our main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables.

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    Ready for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!

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    Data Preparation

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    ​​To explore how economic growth connects with gender equality, we turned to the Imfp library to access a wealth of global economic data, ready to analyze through some straightforward API calls. Let’s break down our two main variables of interest. Here’s the lowdown on the 4 key data series we pulled together:

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    Dependent Variable: Percent Change in Gender Inequality Index (GII)

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    Our gender equality measure, the Gender Inequality Index (GII), is a composite score that reflects inequality in three key areas: reproductive health, empowerment, and labor market, ranging from 0 (equality) to 1 (inequality). We calculated the percent change in GII over time to make it easier to compare GII change across countries with different baseline levels of inequality.

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    Independent Variable: Percent Change in Real GDP per capita

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    Our economic growth measure is real GDP per capita, an indicator of a country’s economic output per person, adjusted for inflation. The higher the real GDP per capita, the wealthier and more prosperous a country is. Here is a peek at the formula:

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    Data Visualization

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    Scatterplot

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    Nothing like a good scatterplot to help us understand the relationship between two numbers, right? For our project, the scatterplot lets us visually explore how Percent Change in Real GDP per capita relates to Percent Change in GII. Each dot on the plot represents a country-year combo. The gentle upward slope suggests that in years of low GDP growth, gender inequality tends to decrease a bit faster, while in years of high GDP growth, the inequality drops at a slightly slower rate. It’s a subtle clue, but slower-growing economies might be making slightly quicker strides in gender equality.

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    Time Series Line plot

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    The time series line plot gives us the big picture of GDP and GII changes over time across a few select countries, including the United States, Japan, the United Kingdom, France, and Mexico. Mexico, for example, saw great GDP swings in 1995, yet its GII stayed relatively stable. Meanwhile, Japan and France saw some dramatic GII shifts. These fluctuations leave us wondering: what historical events sparked these changes? We may need to take a deeper look into each country’s unique timeline to find the answers.

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    Boxplot

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    Next, we have the boxplot to help us visualize the distribution. By sequencing countries in ascending order of median GDP change, we see an interesting trend: countries with higher median GDP growth also seem to show more variability in their GDP changes. The GII change, on the other hand, keeps a relatively stable median, hinting at a weaker association with GDP growth.

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    Heatmap

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    Finally, our heatmap wraps it all up with a quick, intuitive look at correlations between GDP and GII across countries. In countries like Estonia (EE) and Ireland (IE), Australia (AT), South Africa (ZA), Thailand (TH), Slovenia (SI), we see positive correlations in warm colors, indicating that as GDP growth rates increase, GII growth rates tend to rise as well. For countries like Cyprus (CY), Romania (RO), and the United States (US), we see cooler colors, representing negative correlations: as GDP growth rate rises, GII growth rates decrease, suggesting an improvement in gender equality alongside economic growth. This color-coded comparison leaves us curious—could the distinction between developed and developing countries play a role in these relationships? It’s a question that might inspire future research directions!

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    Statistical Analysis

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    Descriptive Statistics

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    Let’s start with a quick overview of our descriptive statistics: our dataset includes 915 data points. On average, GII change is -3.15%, indicating an overall reduction in gender inequality over time. Meanwhile, GDP change averages 2.35%, hinting at modest economic growth. A full breakdown of these values can be found in the table below for more detail.

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    Regression Analysis

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    To get a sense of any relationship between GDP change and GII change, we ran a simple linear regression. Here’s what we found:

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    • P-value: With a p-value of 0.057, we fail to reject the null hypothesis. There is no statistically significant relationship. In other words, we don’t find strong evidence of a link between GDP and GII changes.

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    • R-squared: GDP change explains only 0.4% of the variation in GII change—a pretty minimal effect.

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    • 95% Confidence Interval: We’re 95% confident the interval from -0.003 to 0.169 captures the true slope. The effect of GDP change on GII is uncertain as 0 is within this range.

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    Time Series Analysis

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    Time series analysis allows us to dig deeper into how GII and GDP changes play out over time—and potentially influence one another. Here’s the rundown:

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    • Stationarity check: Both GII and GDP change series are stationary, meaning their means and variances are stable over time, setting us up for reliable forecasting.

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    • VAR Model (Vector Autoregression): This multivariate model lets us explore how past values in both series relate to current values. Notably, past GII changes significantly impact current GII changes.

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    • Model Performance: The VAR model has good fit, as shown by the forecast closely tracking actual values. Low MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) confirm that our model predicts GII and GDP change with minimal error. Check out the plots and table below!

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    • Granger Causality Test: Curious whether GDP changes can predict changes in GII? The Granger causality test says no. With p-values above 0.05, we fail to find evidence that GDP changes drive GII changes. This suggests that while both variables evolve over time, they don’t seem to directly influence each other.

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    Conclusion

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    In wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research.

    -

    In terms of the Imfp library, we gained valuable experience working with real-world economic indicators like GDP and the GII. The library’s function in fetching databases, parameters, and datasets made it straightforward to access IMF’s data, allowing us to focus on analysis instead. The integration of other Python tools—such as seaborn, matplotlib, and numpy—not only streamlined our computational processes but also ensured consistency across the project.

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    Future Directions

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    First, we must consider what other factors might influence the relationship between GDP and GII change. The GII is a composite index, shaped by a myriad of social factors, including cultural norms, legal frameworks, and environmental shifts. Future studies could benefit from incorporating additional predictors into the analysis and exploring the interaction between economic growth and gender equality within specific country contexts.

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    Second, there’s potential to enhance the predictive power of our Vector Autoregression (VAR) time series model. While we established that GDP change does not cause GII change, our model performed well in forecasting trends for both variables independently. In practice, policymakers may want to forecast GII trends independently of GDP if they are implementing gender-focused policies. Future research could investigate time series modeling to further unravel the dynamics of GII and GDP changes.

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    So, as we wrap up this chapter, let’s keep our curiosity alive and our questions flowing. After all, every end is just a new beginning in the quest for knowledge!

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    Welcome To My Blog

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    Author
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    Tristan O’Malley

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    December 22, 2024

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    This is the first post in a Quarto blog. Welcome!

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    Since this post doesn’t specify an explicit image, the first image in the post will be used in the listing page of posts.

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It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders? Our main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables. Ready for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!\nData Preparation ​​To explore how economic growth connects with gender equality, we turned to the Imfp library to access a wealth of global economic data, ready to analyze through some straightforward API calls. Let’s break down our two main variables of interest. Here’s the lowdown on the 4 key data series we pulled together:\nDependent Variable: Percent Change in Gender Inequality Index (GII) Our gender equality measure, the Gender Inequality Index (GII), is a composite score that reflects inequality in three key areas: reproductive health, empowerment, and labor market, ranging from 0 (equality) to 1 (inequality). We calculated the percent change in GII over time to make it easier to compare GII change across countries with different baseline levels of inequality.\nIndependent Variable: Percent Change in Real GDP per capita Our economic growth measure is real GDP per capita, an indicator of a country’s economic output per person, adjusted for inflation. The higher the real GDP per capita, the wealthier and more prosperous a country is. Here is a peek at the formula:\nData Visualization Scatterplot\nNothing like a good scatterplot to help us understand the relationship between two numbers, right? For our project, the scatterplot lets us visually explore how Percent Change in Real GDP per capita relates to Percent Change in GII. Each dot on the plot represents a country-year combo. The gentle upward slope suggests that in years of low GDP growth, gender inequality tends to decrease a bit faster, while in years of high GDP growth, the inequality drops at a slightly slower rate. It’s a subtle clue, but slower-growing economies might be making slightly quicker strides in gender equality. Time Series Line plot\nThe time series line plot gives us the big picture of GDP and GII changes over time across a few select countries, including the United States, Japan, the United Kingdom, France, and Mexico. Mexico, for example, saw great GDP swings in 1995, yet its GII stayed relatively stable. Meanwhile, Japan and France saw some dramatic GII shifts. These fluctuations leave us wondering: what historical events sparked these changes? We may need to take a deeper look into each country’s unique timeline to find the answers.\nBoxplot\nNext, we have the boxplot to help us visualize the distribution. By sequencing countries in ascending order of median GDP change, we see an interesting trend: countries with higher median GDP growth also seem to show more variability in their GDP changes. The GII change, on the other hand, keeps a relatively stable median, hinting at a weaker association with GDP growth.\nHeatmap\nFinally, our heatmap wraps it all up with a quick, intuitive look at correlations between GDP and GII across countries. In countries like Estonia (EE) and Ireland (IE), Australia (AT), South Africa (ZA), Thailand (TH), Slovenia (SI), we see positive correlations in warm colors, indicating that as GDP growth rates increase, GII growth rates tend to rise as well. For countries like Cyprus (CY), Romania (RO), and the United States (US), we see cooler colors, representing negative correlations: as GDP growth rate rises, GII growth rates decrease, suggesting an improvement in gender equality alongside economic growth. This color-coded comparison leaves us curious—could the distinction between developed and developing countries play a role in these relationships? It’s a question that might inspire future research directions!\nStatistical Analysis Descriptive Statistics Let’s start with a quick overview of our descriptive statistics: our dataset includes 915 data points. On average, GII change is -3.15%, indicating an overall reduction in gender inequality over time. Meanwhile, GDP change averages 2.35%, hinting at modest economic growth. A full breakdown of these values can be found in the table below for more detail.\nRegression Analysis To get a sense of any relationship between GDP change and GII change, we ran a simple linear regression. Here’s what we found: P-value: With a p-value of 0.057, we fail to reject the null hypothesis. There is no statistically significant relationship. In other words, we don’t find strong evidence of a link between GDP and GII changes. R-squared: GDP change explains only 0.4% of the variation in GII change—a pretty minimal effect. 95% Confidence Interval: We’re 95% confident the interval from -0.003 to 0.169 captures the true slope. The effect of GDP change on GII is uncertain as 0 is within this range.\nTime Series Analysis Time series analysis allows us to dig deeper into how GII and GDP changes play out over time—and potentially influence one another. Here’s the rundown: Stationarity check: Both GII and GDP change series are stationary, meaning their means and variances are stable over time, setting us up for reliable forecasting. VAR Model (Vector Autoregression): This multivariate model lets us explore how past values in both series relate to current values. Notably, past GII changes significantly impact current GII changes. Model Performance: The VAR model has good fit, as shown by the forecast closely tracking actual values. Low MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) confirm that our model predicts GII and GDP change with minimal error. Check out the plots and table below! Granger Causality Test: Curious whether GDP changes can predict changes in GII? The Granger causality test says no. With p-values above 0.05, we fail to find evidence that GDP changes drive GII changes. This suggests that while both variables evolve over time, they don’t seem to directly influence each other.\nConclusion In wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research. In terms of the Imfp library, we gained valuable experience working with real-world economic indicators like GDP and the GII. The library’s function in fetching databases, parameters, and datasets made it straightforward to access IMF’s data, allowing us to focus on analysis instead. The integration of other Python tools—such as seaborn, matplotlib, and numpy—not only streamlined our computational processes but also ensured consistency across the project. Future Directions First, we must consider what other factors might influence the relationship between GDP and GII change. The GII is a composite index, shaped by a myriad of social factors, including cultural norms, legal frameworks, and environmental shifts. Future studies could benefit from incorporating additional predictors into the analysis and exploring the interaction between economic growth and gender equality within specific country contexts. Second, there’s potential to enhance the predictive power of our Vector Autoregression (VAR) time series model. While we established that GDP change does not cause GII change, our model performed well in forecasting trends for both variables independently. In practice, policymakers may want to forecast GII trends independently of GDP if they are implementing gender-focused policies. Future research could investigate time series modeling to further unravel the dynamics of GII and GDP changes. So, as we wrap up this chapter, let’s keep our curiosity alive and our questions flowing. After all, every end is just a new beginning in the quest for knowledge!" - }, - { - "objectID": "posts/welcome/index.html", - "href": "posts/welcome/index.html", - "title": "Welcome To My Blog", - "section": "", - "text": "This is the first post in a Quarto blog. Welcome!\n\nSince this post doesn’t specify an explicit image, the first image in the post will be used in the listing page of posts." - }, - { - "objectID": "posts/blog/Blog.html", - "href": "posts/blog/Blog.html", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "", - "text": "Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders?\nOur main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables.\nReady for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!" - }, - { - "objectID": "posts/blog/Blog.html#introduction", - "href": "posts/blog/Blog.html#introduction", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "", - "text": "Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders?\nOur main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables.\nReady for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!" - }, - { - "objectID": "posts/blog/Blog.html#data-preparation", - "href": "posts/blog/Blog.html#data-preparation", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "Data Preparation", - "text": "Data Preparation\n​​To explore how economic growth connects with gender equality, we turned to the Imfp library to access a wealth of global economic data, ready to analyze through some straightforward API calls. Let’s break down our two main variables of interest. Here’s the lowdown on the 4 key data series we pulled together:\n\n\nDependent Variable: Percent Change in Gender Inequality Index (GII)\nOur gender equality measure, the Gender Inequality Index (GII), is a composite score that reflects inequality in three key areas: reproductive health, empowerment, and labor market, ranging from 0 (equality) to 1 (inequality). We calculated the percent change in GII over time to make it easier to compare GII change across countries with different baseline levels of inequality.\n\n\nIndependent Variable: Percent Change in Real GDP per capita\nOur economic growth measure is real GDP per capita, an indicator of a country’s economic output per person, adjusted for inflation. The higher the real GDP per capita, the wealthier and more prosperous a country is. Here is a peek at the formula:" - }, - { - "objectID": "posts/blog/Blog.html#data-visualization", - "href": "posts/blog/Blog.html#data-visualization", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "Data Visualization", - "text": "Data Visualization\n\nScatterplot\nNothing like a good scatterplot to help us understand the relationship between two numbers, right? For our project, the scatterplot lets us visually explore how Percent Change in Real GDP per capita relates to Percent Change in GII. Each dot on the plot represents a country-year combo. The gentle upward slope suggests that in years of low GDP growth, gender inequality tends to decrease a bit faster, while in years of high GDP growth, the inequality drops at a slightly slower rate. It’s a subtle clue, but slower-growing economies might be making slightly quicker strides in gender equality.\n \n\n\nTime Series Line plot\nThe time series line plot gives us the big picture of GDP and GII changes over time across a few select countries, including the United States, Japan, the United Kingdom, France, and Mexico. Mexico, for example, saw great GDP swings in 1995, yet its GII stayed relatively stable. Meanwhile, Japan and France saw some dramatic GII shifts. These fluctuations leave us wondering: what historical events sparked these changes? We may need to take a deeper look into each country’s unique timeline to find the answers.\n \n\n\nBoxplot\nNext, we have the boxplot to help us visualize the distribution. By sequencing countries in ascending order of median GDP change, we see an interesting trend: countries with higher median GDP growth also seem to show more variability in their GDP changes. The GII change, on the other hand, keeps a relatively stable median, hinting at a weaker association with GDP growth.\n \n\n\nHeatmap\nFinally, our heatmap wraps it all up with a quick, intuitive look at correlations between GDP and GII across countries. In countries like Estonia (EE) and Ireland (IE), Australia (AT), South Africa (ZA), Thailand (TH), Slovenia (SI), we see positive correlations in warm colors, indicating that as GDP growth rates increase, GII growth rates tend to rise as well. For countries like Cyprus (CY), Romania (RO), and the United States (US), we see cooler colors, representing negative correlations: as GDP growth rate rises, GII growth rates decrease, suggesting an improvement in gender equality alongside economic growth. This color-coded comparison leaves us curious—could the distinction between developed and developing countries play a role in these relationships? It’s a question that might inspire future research directions!" - }, - { - "objectID": "posts/blog/Blog.html#statistical-analysis", - "href": "posts/blog/Blog.html#statistical-analysis", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "Statistical Analysis", - "text": "Statistical Analysis\n\nDescriptive Statistics\nLet’s start with a quick overview of our descriptive statistics: our dataset includes 915 data points. On average, GII change is -3.15%, indicating an overall reduction in gender inequality over time. Meanwhile, GDP change averages 2.35%, hinting at modest economic growth. A full breakdown of these values can be found in the table below for more detail.\n\n\n\nRegression Analysis\nTo get a sense of any relationship between GDP change and GII change, we ran a simple linear regression. Here’s what we found:\n\nP-value: With a p-value of 0.057, we fail to reject the null hypothesis. There is no statistically significant relationship. In other words, we don’t find strong evidence of a link between GDP and GII changes.\nR-squared: GDP change explains only 0.4% of the variation in GII change—a pretty minimal effect.\n95% Confidence Interval: We’re 95% confident the interval from -0.003 to 0.169 captures the true slope. The effect of GDP change on GII is uncertain as 0 is within this range.\n\n\n\nTime Series Analysis\nTime series analysis allows us to dig deeper into how GII and GDP changes play out over time—and potentially influence one another. Here’s the rundown:\n\nStationarity check: Both GII and GDP change series are stationary, meaning their means and variances are stable over time, setting us up for reliable forecasting.\nVAR Model (Vector Autoregression): This multivariate model lets us explore how past values in both series relate to current values. Notably, past GII changes significantly impact current GII changes.\nModel Performance: The VAR model has good fit, as shown by the forecast closely tracking actual values. Low MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) confirm that our model predicts GII and GDP change with minimal error. Check out the plots and table below!\nGranger Causality Test: Curious whether GDP changes can predict changes in GII? The Granger causality test says no. With p-values above 0.05, we fail to find evidence that GDP changes drive GII changes. This suggests that while both variables evolve over time, they don’t seem to directly influence each other." - }, - { - "objectID": "posts/blog/Blog.html#conclusion", - "href": "posts/blog/Blog.html#conclusion", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "Conclusion", - "text": "Conclusion\nIn wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research.\nIn terms of the Imfp library, we gained valuable experience working with real-world economic indicators like GDP and the GII. The library’s function in fetching databases, parameters, and datasets made it straightforward to access IMF’s data, allowing us to focus on analysis instead. The integration of other Python tools—such as seaborn, matplotlib, and numpy—not only streamlined our computational processes but also ensured consistency across the project." - }, - { - "objectID": "posts/blog/Blog.html#future-directions", - "href": "posts/blog/Blog.html#future-directions", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "Future Directions", - "text": "Future Directions\nFirst, we must consider what other factors might influence the relationship between GDP and GII change. The GII is a composite index, shaped by a myriad of social factors, including cultural norms, legal frameworks, and environmental shifts. Future studies could benefit from incorporating additional predictors into the analysis and exploring the interaction between economic growth and gender equality within specific country contexts.\nSecond, there’s potential to enhance the predictive power of our Vector Autoregression (VAR) time series model. While we established that GDP change does not cause GII change, our model performed well in forecasting trends for both variables independently. In practice, policymakers may want to forecast GII trends independently of GDP if they are implementing gender-focused policies. Future research could investigate time series modeling to further unravel the dynamics of GII and GDP changes.\nSo, as we wrap up this chapter, let’s keep our curiosity alive and our questions flowing. After all, every end is just a new beginning in the quest for knowledge!" - }, - { - "objectID": "imfp_demo.html", - "href": "imfp_demo.html", - "title": "imfp Demo Project", - "section": "", - "text": "This data analysis project aims to explore the relationship between economic growth and gender equality using imfp, which allows us to download data from IMF (International Monetary Fund). imfp can be integrated with other python tools to streamline the computational process. To demonstrate its functionality, the project experimented with a variety of visualization and analysis methods." - }, - { - "objectID": "imfp_demo.html#executive-summary", - "href": "imfp_demo.html#executive-summary", - "title": "imfp Demo Project", - "section": "Executive Summary", - "text": "Executive Summary\nIn this project, we explored the following:\n\nData Fetching\n\n\nMake API call to fetch 4 datasets: GII (Gender Inequality Index), Nominal GDP, GDP Deflator Index, Population series\n\n\nFeature Engineering\n\n\nCleaning: Convert GDP Deflator Index to a yearly basis and variables to numeric\nDependent Variable: Percent Change of Gender Inequality Index\nIndependent Variable: Percent Change of Real GDP per Capita\nTransform variables to display magnitude of change\nMerge the datasets\n\n\nData Visualization\n\n\nScatterplot\nTime Series Line Plots\nBarplot\nBoxplot\nHeatmap\n\n\nStatistical Analysis\n\n\nDescriptive Statistics\nRegression Analysis\nTime Series Analysis" - }, - { - "objectID": "imfp_demo.html#suggested-packages", - "href": "imfp_demo.html#suggested-packages", - "title": "imfp Demo Project", - "section": "Suggested packages", - "text": "Suggested packages\nThe integration of other Python tools not only streamlined our computational processes but also ensured consistency across the project.\nA custom module is written to simplify the process of making API calls and fetching information with imfp library. load_or_fetch_databases, load_or_fetch_parameters load_or_fetch_dataset load and retreive database, parameters, and dataset from a local or remote source. view_dataframe_in_browser displays dataframe in a web browser.\n\nimport os\nimport pickle\nfrom tempfile import NamedTemporaryFile\nimport pandas as pd\nimport imfp\nimport webbrowser\n\n\n# Function to display a DataFrame in a web browser\ndef view_dataframe_in_browser(df):\n html = df.to_html()\n with NamedTemporaryFile(delete=False, mode=\"w\", suffix=\".html\") as f:\n url = \"file://\" + f.name\n f.write(html)\n webbrowser.open(url)\n\n\n# Function to load databases from CSV or fetch from API\ndef load_or_fetch_databases():\n csv_path = os.path.join(\"data\", \"databases.csv\")\n\n # Try to load from CSV\n if os.path.exists(csv_path):\n try:\n return pd.read_csv(csv_path)\n except Exception as e:\n print(f\"Error loading CSV: {e}\")\n\n # If CSV doesn't exist or couldn't be loaded, fetch from API\n print(\"Fetching databases from IMF API...\")\n databases = imfp.imf_databases()\n\n # Save to CSV for future use\n databases.to_csv(csv_path, index=False)\n print(f\"Databases saved to {csv_path}\")\n\n return databases\n\n\ndef load_or_fetch_parameters(database_name):\n pickle_path = os.path.join(\"data\", f\"{database_name}.pickle\")\n\n # Try to load from pickle file\n if os.path.exists(pickle_path):\n try:\n with open(pickle_path, \"rb\") as f:\n return pickle.load(f)\n except Exception as e:\n print(f\"Error loading pickle file: {e}\")\n\n # If pickle doesn't exist or couldn't be loaded, fetch from API\n print(f\"Fetching parameters for {database_name} from IMF API...\")\n parameters = imfp.imf_parameters(database_name)\n\n # Save to pickle file for future use\n os.makedirs(\"data\", exist_ok=True) # Ensure the data directory exists\n with open(pickle_path, \"wb\") as f:\n pickle.dump(parameters, f)\n print(f\"Parameters saved to {pickle_path}\")\n\n return parameters\n\n\ndef load_or_fetch_dataset(database_id, indicator):\n file_name = f\"{database_id}.{indicator}.csv\"\n csv_path = os.path.join(\"data\", file_name)\n\n # Try to load from CSV file\n if os.path.exists(csv_path):\n try:\n return pd.read_csv(csv_path)\n except Exception as e:\n print(f\"Error loading CSV file: {e}\")\n\n # If CSV doesn't exist or couldn't be loaded, fetch from API\n print(f\"Fetching dataset for {database_id}.{indicator} from IMF API...\")\n dataset = imfp.imf_dataset(database_id=database_id, indicator=[indicator])\n\n # Save to CSV file for future use\n os.makedirs(\"data\", exist_ok=True) # Ensure the data directory exists\n dataset.to_csv(csv_path, index=False)\n print(f\"Dataset saved to {csv_path}\")\n\n return dataset\n\nHere is a brief introduction about the packages used:\npandas: view and manipulate data frame\nmatplotlib.pyplot: make plots\nseaborn: make plots\nnumpy: computation\nLinearRegression: implement linear regression\ntabulate: format data into tables\nstatsmodels.api, adfuller, ARIMA,VAR,plot_acf,plot_pacf,mean_absolute_error,mean_squared_error, andgrangercausalitytests are specifically used for time series analysis.\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\nfrom tabulate import tabulate\nimport statsmodels.api as sm\nfrom statsmodels.tsa.stattools import adfuller\nfrom statsmodels.tsa.arima.model import ARIMA\nfrom statsmodels.tsa.vector_ar.var_model import VAR\nfrom statsmodels.graphics.tsaplots import plot_acf, plot_pacf\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.metrics import mean_squared_error\nfrom statsmodels.tsa.stattools import grangercausalitytests" - }, - { - "objectID": "imfp_demo.html#data-fetching", - "href": "imfp_demo.html#data-fetching", - "title": "imfp Demo Project", - "section": "Data Fetching", - "text": "Data Fetching\nIn this section, we extracted four datasets through API calls: Gender Inequality Index(GII), GDP Deflator, Nominal GDP, and Population.\n\nfrom pathlib import Path\nPath(\"data\").mkdir(exist_ok=True)\n\n\n# Load or fetch databases\ndatabases = load_or_fetch_databases()\n\n# Filter out databases that contain a year in the description\ndatabases[\n ~databases['description'].str.contains(r\"[\\d]{4}\", regex=True)\n]\n\n# view_dataframe_in_browser(databases)\n\n\n\n\n\n\n\n\ndatabase_id\ndescription\n\n\n\n\n25\nHPDD\nHistorical Public Debt (HPDD)\n\n\n34\nRAFIT2AGG\nRevenue Administration Fiscal Information Tool...\n\n\n47\nGENDER_EQUALITY\nGender Equality\n\n\n63\nPGCS\nPrivate and Public Capital Stock Dataset\n\n\n69\nGENDER_BUDGETING\nGender Budgeting\n\n\n125\nCPI\nConsumer Price Index (CPI)\n\n\n153\nIRFCL\nInternational Reserves and Foreign Currency Li...\n\n\n191\nIFS_DISCONTINUED\nInternational Financial Statistics (IFS), Disc...\n\n\n192\nEQ\nExport Quality\n\n\n193\nED\nExport Diversification\n\n\n226\nFISCALDECENTRALIZATION\nFiscal Decentralization\n\n\n291\nFDI\nFinancial Development Index\n\n\n292\nPSBSFAD\nPublic Sector Balance Sheet (PSBS)(FAD)\n\n\n293\nUNSDG_IMF_INPUTS\nSustainable Development Goals, IMF Inputs\n\n\n294\nCPIS\nCoordinated Portfolio Investment Survey (CPIS)\n\n\n295\nPCTOT\nCommodity Terms of Trade\n\n\n296\nFM\nFiscal Monitor (FM)\n\n\n297\nAFRREO\nSub-Saharan Africa Regional Economic Outlook (...\n\n\n298\nWHDREO\nWestern Hemisphere Regional Economic Outlook (...\n\n\n299\nMCDREO\nMiddle East and Central Asia Regional Economic...\n\n\n300\nAPDREO\nAsia and Pacific Regional Economic Outlook (AP...\n\n\n301\nBOPAGG\nBalance of Payments (BOP), World and Regional ...\n\n\n302\nPCPS\nPrimary Commodity Price System (PCPS)\n\n\n303\nCDIS\nCoordinated Direct Investment Survey (CDIS)\n\n\n304\nBOP\nBalance of Payments (BOP)\n\n\n305\nCOFER\nCurrency Composition of Official Foreign Excha...\n\n\n306\nDOT\nDirection of Trade Statistics (DOTS)\n\n\n307\nRAFIT3P\nRA-FIT Round3 Completion and Participation Rates\n\n\n308\nFAS\nFinancial Access Survey (FAS)\n\n\n309\nFSI\nFinancial Soundness Indicators (FSIs)\n\n\n310\nFSIRE\nFinancial Soundness Indicators: Reporting enti...\n\n\n311\nBOPSDMXUSD\nBalance of Payments (BOP), Global SDMX (US Dol...\n\n\n312\nNAMAIN_IDC_N\nSystem of National Accounts (SNA), NA_MAIN\n\n\n313\nGFSR\nGovernment Finance Statistics (GFS), Revenue\n\n\n314\nGFSSSUC\nGovernment Finance Statistics (GFS), Statement...\n\n\n315\nGFSCOFOG\nGovernment Finance Statistics (GFS), Expenditu...\n\n\n316\nGFSFALCS\nGovernment Finance Statistics (GFS), Financial...\n\n\n317\nGFSIBS\nGovernment Finance Statistics (GFS), Integrate...\n\n\n318\nGFSMAB\nGovernment Finance Statistics (GFS), Main Aggr...\n\n\n319\nGFSE\nGovernment Finance Statistics (GFS), Expense\n\n\n320\nIFS\nInternational Financial Statistics (IFS)\n\n\n321\nMFS\nMonetary and Financial Statistics (MFS)\n\n\n\n\n\n\n\nTwo databases were used: Gender Equality and International Financial Statistics (IFS).\n\ndatasets = [\"GENDER_EQUALITY\", \"IFS\"]\nparams = {}\n\n# Fetch valid parameters for two datasets\nfor dataset in datasets:\n params[dataset] = load_or_fetch_parameters(dataset)\n\n valid_keys = list(params[dataset].keys())\n print(f\"Parameters for {dataset}: \", valid_keys)\n\n# view_dataframe_in_browser(params.get(\"IFS\").get(\"indicator\"))\n\nParameters for GENDER_EQUALITY: ['freq', 'ref_area', 'indicator']\nParameters for IFS: ['freq', 'ref_area', 'indicator']\n\n\n“freq” stands for Frequency, such as Annual, Monthly, or Quarterly. “ref_area” stands for Geogrpahical Area, such as US (United States), JP (Japan), and GB (United Kindom). “indicator” refers to the specific dataset in the database, including input code and description. For example, if we display all the indicators for IFS database, the GDP deflator dataset has an input code of “NGDP_D_SA_IX” with a full name description of Gross Domestic Product, Deflator, Seasonally Adjusted, Index.\nWe paired the database with the specific dataset indicator to read and store the csv file.\n\ndatasets = {}\ndsets = [(\"GENDER_EQUALITY\", \"GE_GII\"), (\"IFS\", \"NGDP_D_SA_IX\"), (\"IFS\", \"NGDP_XDC\"), (\"IFS\", \"LP_PE_NUM\")]\n\nfor dset in dsets:\n datasets[dset[0] + \".\" + dset[1]] = load_or_fetch_dataset(dset[0], dset[1])\n\n\n# \"Gender Inequality Index\"\nGII = \"GENDER_EQUALITY.GE_GII\"\n\n# \"Gross Domestic Product, Deflator, Seasonally Adjusted, Index\"\nGDP_deflator = \"IFS.NGDP_D_SA_IX\"\n\n# \"Gross Domestic Product, Nominal, Domestic Currency\"\nGDP_nominal = \"IFS.NGDP_XDC\"\n\n# \"Population, Persons, Number of\"\nGDP_population = \"IFS.LP_PE_NUM\"\n\n# Assign the datasets to new variables so we don't change the originals\nGII_data = datasets[GII]\nGDP_deflator_data = datasets[GDP_deflator]\nGDP_nominal_data = datasets[GDP_nominal]\nGDP_population_data = datasets[GDP_population]" - }, - { - "objectID": "imfp_demo.html#feature-engineering", - "href": "imfp_demo.html#feature-engineering", - "title": "imfp Demo Project", - "section": "Feature Engineering", - "text": "Feature Engineering\n\nData Cleaning\nSince the GDP deflator was reported on a quarterly basis, we converted it to a yearly basis.\n\n# Filter to keep only rows with a partial string match for \"Q4\" in the time_period column\nGDP_deflator_data = GDP_deflator_data[GDP_deflator_data['time_period'].str.contains(\"Q4\")]\n\n\n# Split the time_period into year and quarter and keep the year only\nGDP_deflator_data.loc[:, 'time_period'] = GDP_deflator_data['time_period'].str[0:4]\n\nWe made all the variables numeric.\n\ndatasets = [GII_data, GDP_deflator_data, GDP_nominal_data, GDP_population_data]\n\nfor i, dataset in enumerate(datasets): \n # Use .loc to modify the columns\n datasets[i].loc[:, 'obs_value'] = pd.to_numeric(datasets[i]['obs_value'], errors='coerce')\n datasets[i].loc[:, 'time_period'] = pd.to_numeric(datasets[i]['time_period'], errors='coerce')\n datasets[i].loc[:, 'unit_mult'] = pd.to_numeric(datasets[i]['unit_mult'], errors='coerce')\n\n\n\nGII Percent Change: Dependent Variable\nWe kept percents as decimals to make them easy to work with for calculation. Different countries have different baseline level of economic growth and gender equality. We calculated the percent change to make them comparable.\n\n# Calculate percent change for each ref_area\nGII_data[f'pct_change'] = GII_data.sort_values(['ref_area', 'time_period']).groupby('ref_area')['obs_value'].pct_change()\n\n# Display the first few rows of the updated dataset\nGII_data.head()\n\n\n\n\n\n\n\n\nfreq\nref_area\nindicator\nunit_mult\ntime_format\ntime_period\nobs_value\npct_change\n\n\n\n\n0\nA\nAF\nGE_GII\n0\nP1Y\n1990\n0.828244\nNaN\n\n\n1\nA\nAF\nGE_GII\n0\nP1Y\n1991\n0.817706\n-0.012723\n\n\n2\nA\nAF\nGE_GII\n0\nP1Y\n1992\n0.809806\n-0.009662\n\n\n3\nA\nAF\nGE_GII\n0\nP1Y\n1993\n0.803078\n-0.008308\n\n\n4\nA\nAF\nGE_GII\n0\nP1Y\n1994\n0.797028\n-0.007533\n\n\n\n\n\n\n\nWe subset the data frame to keep only the columns we want:\n\n# Create a new dataframe with only the required columns\nGII_data = GII_data[['ref_area', 'time_period', 'pct_change']].copy()\n\n# Display the first few rows of the new dataset\nGII_data.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\npct_change\n\n\n\n\n0\nAF\n1990\nNaN\n\n\n1\nAF\n1991\n-0.012723\n\n\n2\nAF\n1992\n-0.009662\n\n\n3\nAF\n1993\n-0.008308\n\n\n4\nAF\n1994\n-0.007533\n\n\n\n\n\n\n\n\n\nGDP: Independent Variable\nWe kept the columns we want only for GDP-related datasets for easier table merging.\n\n# GDP Deflator Dataset\n# Create a new dataframe with only the required columns\nGDP_deflator_data = GDP_deflator_data[['ref_area', 'time_period', 'unit_mult', 'obs_value']].copy()\n\n# Divide obs_value by 100\nGDP_deflator_data['obs_value'] = GDP_deflator_data['obs_value'] / 100\n\n# Display the first few rows of the new dataset\nGDP_deflator_data.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nunit_mult\nobs_value\n\n\n\n\n3\nFI\n1990\n0\n0.732006\n\n\n7\nFI\n1991\n0\n0.739841\n\n\n11\nFI\n1992\n0\n0.746543\n\n\n15\nFI\n1993\n0\n0.756193\n\n\n19\nFI\n1994\n0\n0.779373\n\n\n\n\n\n\n\n\n# GDP Nominal Data\n# Create a new dataframe with only the required columns\nGDP_nominal_data = GDP_nominal_data[['ref_area', 'time_period', 'unit_mult','obs_value']].copy()\n\n# Display the first few rows of the new dataset\nGDP_nominal_data.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nunit_mult\nobs_value\n\n\n\n\n0\nNE\n2005\n6\n2418864.0\n\n\n1\nNE\n2006\n6\n2596972.0\n\n\n2\nNE\n2007\n6\n2762961.0\n\n\n3\nNE\n2008\n6\n3247835.0\n\n\n4\nNE\n2009\n6\n3403683.0\n\n\n\n\n\n\n\n\n# GDP Population Data \n# Create a new dataframe with only the required columns\nGDP_population_data = GDP_population_data[['ref_area', 'time_period', 'unit_mult','obs_value']].copy()\n\n# Display the first few rows of the new dataset\nGDP_population_data.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nunit_mult\nobs_value\n\n\n\n\n0\nGA\n1950\n3\n473.296\n\n\n1\nGA\n1951\n3\n476.381\n\n\n2\nGA\n1952\n3\n478.655\n\n\n3\nGA\n1953\n3\n480.536\n\n\n4\nGA\n1954\n3\n482.332\n\n\n\n\n\n\n\n\n# Combine all the datasets above for further calculation\nmerged_df = pd.merge(pd.merge(GDP_deflator_data,GDP_nominal_data, on=['time_period', 'ref_area'], suffixes=('_index', '_nominal'), how='inner'), GDP_population_data, on=['time_period', 'ref_area'], how='inner')\n\n# Display the GDP data in a web browser\n# view_dataframe_in_browser(merged_df)\n\n# Display the first few rows of the dataset\nmerged_df.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nunit_mult_index\nobs_value_index\nunit_mult_nominal\nobs_value_nominal\nunit_mult\nobs_value\n\n\n\n\n0\nFI\n1990\n0\n0.732006\n6\n90964.0\n3\n4996.220\n\n\n1\nFI\n1991\n0\n0.739841\n6\n86913.0\n3\n5019.134\n\n\n2\nFI\n1992\n0\n0.746543\n6\n84786.0\n3\n5044.928\n\n\n3\nFI\n1993\n0\n0.756193\n6\n85610.0\n3\n5071.782\n\n\n4\nFI\n1994\n0\n0.779373\n6\n90646.0\n3\n5097.090\n\n\n\n\n\n\n\nWe want to adjust GDP data based on unit multiplier. Unit multiplier stands for the number of zeroes we need to add to the value column. For example, in 1950, the observed population data for country GA (Georgia) was 473.296. With a unit muliplier of 3, the adjusted population would be 473296.\n\nmerged_df['adjusted_index'] = merged_df['obs_value_index'] * (10 ** (merged_df['unit_mult_index']))\nmerged_df['adjusted_nominal'] = merged_df['obs_value_nominal'] * (10 ** (merged_df['unit_mult_nominal']))\nmerged_df['adjusted_population'] = merged_df['obs_value'] * (10 ** (merged_df['unit_mult']))\n\n\n# Merged dataset\n# Create a new dataframe with only the required columns\nmerged_df = merged_df[['ref_area', 'time_period','adjusted_nominal', 'adjusted_index', 'adjusted_population']].copy()\n\n# Display the first few rows of the dataset\nmerged_df.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nadjusted_nominal\nadjusted_index\nadjusted_population\n\n\n\n\n0\nFI\n1990\n9.096400e+10\n0.732006\n4996220.0\n\n\n1\nFI\n1991\n8.691300e+10\n0.739841\n5019134.0\n\n\n2\nFI\n1992\n8.478600e+10\n0.746543\n5044928.0\n\n\n3\nFI\n1993\n8.561000e+10\n0.756193\n5071782.0\n\n\n4\nFI\n1994\n9.064600e+10\n0.779373\n5097090.0\n\n\n\n\n\n\n\nWe wanted to compute the Real GDP per capita.\n\n# Step 1: Real GDP = (Nominal GDP / GDP Deflator Index)\nmerged_df['Real_GDP_domestic'] = (merged_df['adjusted_nominal'] / merged_df['adjusted_index'])\n\n# Step 2: Real GDP per Capita = Real GDP / Population\nmerged_df['Real_GDP_per_capita'] = merged_df['Real_GDP_domestic'] / merged_df['adjusted_population']\n\n# Check the results\nmerged_df.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nadjusted_nominal\nadjusted_index\nadjusted_population\nReal_GDP_domestic\nReal_GDP_per_capita\n\n\n\n\n0\nFI\n1990\n9.096400e+10\n0.732006\n4996220.0\n1.242667e+11\n24872.143699\n\n\n1\nFI\n1991\n8.691300e+10\n0.739841\n5019134.0\n1.174753e+11\n23405.490395\n\n\n2\nFI\n1992\n8.478600e+10\n0.746543\n5044928.0\n1.135715e+11\n22512.011198\n\n\n3\nFI\n1993\n8.561000e+10\n0.756193\n5071782.0\n1.132119e+11\n22321.919259\n\n\n4\nFI\n1994\n9.064600e+10\n0.779373\n5097090.0\n1.163063e+11\n22818.181344\n\n\n\n\n\n\n\nWe calculated the percentage change in Real GDP per capita and put it in a new column.\n\n# Calculate percent change for each ref_area\nmerged_df[f'GDP_change'] = merged_df.sort_values(['ref_area', 'time_period']).groupby('ref_area')['Real_GDP_per_capita'].pct_change()\n\n# Rename dataset\nGDP_data = merged_df\n\n# Display the first few rows of the dataset\nGDP_data.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nadjusted_nominal\nadjusted_index\nadjusted_population\nReal_GDP_domestic\nReal_GDP_per_capita\nGDP_change\n\n\n\n\n0\nFI\n1990\n9.096400e+10\n0.732006\n4996220.0\n1.242667e+11\n24872.143699\nNaN\n\n\n1\nFI\n1991\n8.691300e+10\n0.739841\n5019134.0\n1.174753e+11\n23405.490395\n-0.058968\n\n\n2\nFI\n1992\n8.478600e+10\n0.746543\n5044928.0\n1.135715e+11\n22512.011198\n-0.038174\n\n\n3\nFI\n1993\n8.561000e+10\n0.756193\n5071782.0\n1.132119e+11\n22321.919259\n-0.008444\n\n\n4\nFI\n1994\n9.064600e+10\n0.779373\n5097090.0\n1.163063e+11\n22818.181344\n0.022232\n\n\n\n\n\n\n\n\n# GII and GDP\n# Merge the datasets\ncombined_data = pd.merge(GII_data, GDP_data, on=[\"ref_area\", \"time_period\"], how = \"inner\")\n\n# Check the combined dataset\ncombined_data.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\npct_change\nadjusted_nominal\nadjusted_index\nadjusted_population\nReal_GDP_domestic\nReal_GDP_per_capita\nGDP_change\n\n\n\n\n0\nAR\n2004\n-0.021896\n4.851153e+11\n0.409672\n38491970.0\n1.184155e+12\n30763.687776\nNaN\n\n\n1\nAR\n2005\n-0.008581\n5.825382e+11\n0.470108\n38892924.0\n1.239159e+12\n31860.772776\n0.035662\n\n\n2\nAR\n2006\n0.007379\n7.159043e+11\n0.526535\n39289876.0\n1.359651e+12\n34605.628084\n0.086152\n\n\n3\nAR\n2007\n-0.020117\n8.969801e+11\n0.629139\n39684303.0\n1.425727e+12\n35926.734575\n0.038176\n\n\n4\nAR\n2008\n0.000733\n1.149646e+12\n0.757753\n40080159.0\n1.517178e+12\n37853.600252\n0.053633\n\n\n\n\n\n\n\n\n# GII and GDP\n# Rename columns\ncombined_data = combined_data.rename(columns = {\n \"ref_area\": \"Country\",\n \"time_period\": \"Time\",\n \"pct_change\": \"GII_change\"\n})\n\n# Display the GDP data in a web browser\n# view_dataframe_in_browser(combined_data)\n\n# Check the combined dataset\ncombined_data.head()\n\n\n\n\n\n\n\n\nCountry\nTime\nGII_change\nadjusted_nominal\nadjusted_index\nadjusted_population\nReal_GDP_domestic\nReal_GDP_per_capita\nGDP_change\n\n\n\n\n0\nAR\n2004\n-0.021896\n4.851153e+11\n0.409672\n38491970.0\n1.184155e+12\n30763.687776\nNaN\n\n\n1\nAR\n2005\n-0.008581\n5.825382e+11\n0.470108\n38892924.0\n1.239159e+12\n31860.772776\n0.035662\n\n\n2\nAR\n2006\n0.007379\n7.159043e+11\n0.526535\n39289876.0\n1.359651e+12\n34605.628084\n0.086152\n\n\n3\nAR\n2007\n-0.020117\n8.969801e+11\n0.629139\n39684303.0\n1.425727e+12\n35926.734575\n0.038176\n\n\n4\nAR\n2008\n0.000733\n1.149646e+12\n0.757753\n40080159.0\n1.517178e+12\n37853.600252\n0.053633" - }, - { - "objectID": "imfp_demo.html#data-visualization", - "href": "imfp_demo.html#data-visualization", - "title": "imfp Demo Project", - "section": "Data Visualization", - "text": "Data Visualization\n\nScatterplot\nScatterplot use dots to represent values of two numeric variables. The horizontal axis was the percent change in Real GDP per capita. The vertical axis was the percent change in Gender Inequality Index(GII). Different colors represented different countries. We used a linear regression line to display the overall pattern.\nBased on the scatterplot, it seemed like there was a slight positive relationship between GDP change and GII change as shown by the flat regression line. Gender inequality was decreasing (gender equality was improving) a little faster in country-years with low GDP growth and a little slower in country-years with high GDP growth.\n\n# Drop NAs\ncombined_data = combined_data.dropna(subset=['GDP_change', 'GII_change'])\n\n# Plot the data points\nplt.figure(figsize=(18, 10))\nfor country in combined_data['Country'].unique():\n country_data = combined_data[combined_data['Country'] == country]\n plt.scatter(country_data['GDP_change'], country_data['GII_change'],\n marker='o',linestyle='-', label=country)\nplt.title('Country-Year Analysis of GDP Change vs. GII Change')\nplt.xlabel('Percent Change in Real GDP per Capita (Country-Year)')\nplt.ylabel('Percent Change in GII (Country-Year)')\nplt.grid(True)\n\n# Prepare data for linear regression\nX = combined_data['GDP_change'].values.reshape(-1, 1)\ny = combined_data['GII_change'].values\n\n# Perform linear regression\nreg = LinearRegression().fit(X, y)\ny_pred = reg.predict(X)\n\n# Plot the regression line\nplt.plot(combined_data['GDP_change'], y_pred, color='red', linewidth=2)\n\nplt.show()\n\n\n\n\n\n\n\n\n\n\nTime Series Line Plot\nWe created separate line plots for GDP change and GII change over time for a few key countries might show the trends more clearly.\nUS: United States\nJP: Japan\nGB: United Kindom\nFR: France\nMX: Mexico\nBased on the line plots, we saw GDP change and GII change have different patterns. For example, in Mexico, when there was a big change in real GDP per captia in 1995, the change in GII was pretty stable.\n\n# Time Series Line plot for a few key countries\nselected_countries = ['US', 'JP', 'GB', 'FR', 'MX']\ncombined_data_selected = combined_data[combined_data['Country'].isin(selected_countries)]\n\n# Set up the Plot Structure\nfig, ax = plt.subplots(2, 1, figsize=(18, 10), sharex=True)\n\n# Plot change in real GDP per capita over time\nsns.lineplot(data = combined_data_selected, x = \"Time\", y = \"GDP_change\", hue = \"Country\", ax = ax[0])\nax[0].set_title(\"Percent Change in Real GDP per Capita Over Time\")\nax[0].set_ylabel(\"Percent Change in Real GDP per Capita\")\n\n# Plot change in GII over time\nsns.lineplot(data = combined_data_selected, x = \"Time\", y = \"GII_change\", hue = \"Country\", ax = ax[1])\nax[1].set_title(\"Percent Change in GII over Time\")\nax[1].set_xlabel(\"Time\")\nax[1].set_ylabel(\"GII\")\n\nplt.tight_layout\nplt.show()\n\n\n\n\n\n\n\n\n\n\nBarplot\nWe used a barplot to show average changes in GII and GDP percent change for each country to visualize regions where inequality was improving or worsening.\nThis plot supported our previous observation how GII change seemed to be not be correlated with GDP change. We also saw that, for country SI, Solvenia, there seems to be a large improvement in gender inequality.\n\n# Barplot using average GII and GDP change\n# Calculate average change for each country\ncombined_data_avg = combined_data.groupby('Country')[['GII_change','GDP_change']].mean().reset_index()\n\n# Prepare to plot structure \nplt.figure(figsize = (18,10))\n\n# Create the barplot\ncombined_data_avg.plot(kind = 'bar', x = 'Country')\nplt.ylabel('Average Change')\nplt.xlabel('Country')\nplt.legend(['GII change', 'GDP change'])\nplt.grid(axis = 'y')\n\n# Show the plot\nplt.show()\n\n<Figure size 1728x960 with 0 Axes>\n\n\n\n\n\n\n\n\n\n\n\nBoxplot\nWe used boxplot to visualize the distribution of GDP and GII change by country, providing information about spread, median, and potential outliers. To provide a more informative view, we sequenced countries in an ascending order by the median of percent change in GDP.\nThe boxplot displayed a slight upward trend with no obvious pattern between GDP and GII change. In coutries with higher GDP change median, they also tend to have a larger spread of the GDP change. The median of GII change remained stable regardless of the magnitude of GDP change, implying weak or no association between GDP and GII change. We observed a potential outlier for country SI, Solvenia, which may explained its large improvement in Gender inequality.\n\n# Box plot for GII and GDP change\n# Melt the dataframe to long format for combined boxplot\ncombined_data_melted = combined_data.melt(id_vars=['Country'], value_vars=['GII_change', 'GDP_change'], \n var_name='Change_Type', value_name='Value')\n\ngdp_medians = combined_data.groupby('Country')['GDP_change'].median().sort_values()\n\ncombined_data_melted['Country'] = pd.Categorical(combined_data_melted['Country'], categories=gdp_medians.index, ordered= True)\n\n# Prepare the plot structure\nplt.figure(figsize=(18, 10))\nsns.boxplot(data = combined_data_melted, x = \"Country\", y = 'Value', hue = 'Change_Type')\nplt.title('Distribution of GII and GDP change by Country')\nplt.xlabel('Country')\nplt.ylabel('Change')\nplt.legend(title = 'Change Type')\n\n# Show the plot\nplt.show()\n\n\n\n\n\n\n\n\n\n\nCorrelation Matrix\nWe created a heatmap to show the relationship between GII and GDP change.\nA positive correlation coefficient indicates a positive relationship: the larger the GDP change, the larger the GII change. A negative correlation coefficient indicates a negative relationship: the larger the GDP change, the smaller the GII change. A correlation coefficient closer to 0 indicates there is weak or no relationship.\nBased on the numeric values in the plot, there was a moderately strong positive correlation between GII and GDP change for country Estonia(EE) and Ireland(IE).\n\n# Calculate the correlation\ncountry_correlation = combined_data.groupby('Country')[['GII_change', 'GDP_change']].corr().iloc[0::2, -1].reset_index(name='Correlation')\n\n# Put the correlation value in a matrix format\ncorrelation_matrix = country_correlation.pivot(index='Country', columns='level_1', values='Correlation')\n\n# Check for NaN values in the correlation matrix\ncorrelation_matrix.fillna(0, inplace=True) # Replace NaNs with 0 or another value as appropriate\n\n# Set up the plot structure\nplt.figure(figsize=(18, 12)) # Adjust height to give more space for y-axis labels\n\n# Plot the heatmap\nsns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0, cbar_kws={\"shrink\": .8}, linewidths=.5)\n\n# Enhance axis labels and title\nplt.title('Heatmap for GII and GDP Change', fontsize=20)\nplt.xlabel('Variables', fontsize=16)\nplt.ylabel('Country', fontsize=16)\n\n# Improve readability of y-axis labels\nplt.yticks(fontsize=12) # Adjust the font size for y-axis labels\n\n# Show the plot\nplt.show()" - }, - { - "objectID": "imfp_demo.html#statistical-analysis", - "href": "imfp_demo.html#statistical-analysis", - "title": "imfp Demo Project", - "section": "Statistical Analysis", - "text": "Statistical Analysis\n\nDescriptive Statistics\nThere was a total of 915 data points. The mean of the GII change in -0.0314868, which indicated the overall grand mean percent change in gender inequality index is -3.15%. The mean of the GDP change was 0.0234633, showing the overall grand mean percent change in real GDP per capita was 2.35%.\n\n# Generate summary statistics\ndescriptive_stats = combined_data.describe()\nprint(tabulate(descriptive_stats, headers='keys', tablefmt='github'))\n\n| | GII_change | adjusted_nominal | adjusted_index | adjusted_population | Real_GDP_domestic | Real_GDP_per_capita | GDP_change |\n|-------|--------------|--------------------|------------------|-----------------------|---------------------|-----------------------|--------------|\n| count | 914 | 914 | 914 | 914 | 914 | 914 | 914 |\n| mean | -0.0314506 | 7.85541e+13 | 0.855504 | 4.50061e+07 | 7.60737e+13 | 1.00595e+06 | 0.023532 |\n| std | 0.054783 | 6.01311e+14 | 0.210057 | 1.24493e+08 | 5.52717e+14 | 3.88155e+06 | 0.0418127 |\n| min | -0.755003 | 2.18714e+09 | 0.0360636 | 396324 | 5.21733e+09 | 1798.42 | -0.285847 |\n| 25% | -0.0424105 | 1.07655e+11 | 0.743355 | 5.0737e+06 | 1.48206e+11 | 17176 | 0.00384809 |\n| 50% | -0.020248 | 7.50706e+11 | 0.888883 | 1.0337e+07 | 9.94933e+11 | 35401 | 0.0222486 |\n| 75% | -0.00451301 | 2.5911e+12 | 1.00175 | 4.5159e+07 | 2.9412e+12 | 120391 | 0.0427318 |\n| max | 0.156992 | 9.54613e+15 | 2.0788 | 1.28084e+09 | 7.92007e+15 | 3.14532e+07 | 0.241984 |\n\n\n\n\nRegression Analysis\nSimple linear regression as a foundational approach provide us with a basic understanding of the relationship between GDP change and GII change.\nBased on the summary, we concluded the following:\n\nBecasue p-value = 0.057, if we set alpha, the significance level, to be 0.05, we failed to reject the null hypothesis and conclude there was no significant relationship between percent change in real GDP per capita and gender inequality index.\nR-squared = 0.004. Only 0.4% of the variance in GII change could be explained by GDP change.\nWe were 95% confident that the interval from -0.003 to 0.169 captured the true slope of GDP change. Because 0 was included, we are uncertain about the effect of GDP change on GII chnage.\n\n\n# Define independent and depenent variables\nX = combined_data['GDP_change']\ny = combined_data['GII_change']\n\n# Add a constant to indepdent variable to include an intercept\nX = sm.add_constant(X)\n\n# Fit a simple linear regresion model and print out the summary\nmodel = sm.OLS(y, X).fit()\nprint(model.summary())\n\n OLS Regression Results \n==============================================================================\nDep. Variable: GII_change R-squared: 0.004\nModel: OLS Adj. R-squared: 0.003\nMethod: Least Squares F-statistic: 3.551\nDate: Wed, 25 Dec 2024 Prob (F-statistic): 0.0598\nTime: 18:00:11 Log-Likelihood: 1360.0\nNo. Observations: 914 AIC: -2716.\nDf Residuals: 912 BIC: -2706.\nDf Model: 1 \nCovariance Type: nonrobust \n==============================================================================\n coef std err t P>|t| [0.025 0.975]\n------------------------------------------------------------------------------\nconst -0.0334 0.002 -16.069 0.000 -0.037 -0.029\nGDP_change 0.0816 0.043 1.885 0.060 -0.003 0.167\n==============================================================================\nOmnibus: 841.660 Durbin-Watson: 1.384\nProb(Omnibus): 0.000 Jarque-Bera (JB): 53999.190\nSkew: -3.938 Prob(JB): 0.00\nKurtosis: 39.823 Cond. No. 23.9\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\n\n\n\nTime Series Analysis\nTime series analysis allows us to explore how the relationship between GII and GDP change vary across different time periods, accounting for lagged effects.\nHere was a quick summary of the result: * Both GII and GDP change time series were stationary.\n\nPast GII change values significantly influenced cuurent GII change values.\nVAR model had good model performance on forecasting future values based on historical data.\nChanges in GDP did not cause/precde the changes in GII.\n\n\nADF Test: Stationality Assumption Check\nWe wanted to use Augmented Dickey-Fuller (ADF) test to check whether a time series was stationary, which was the model assumption for many time series models.\nStationarity implied constant mean and variance over time, making it more predictable and stable for forecasting.\nBased on the ADF test output, both GII and GDP change time series were stationary. We proceeded to the time series modeling section.\n\n# Augmented Dickey-Fuller (ADF) test for stationarity check\n# Create melted datasets\ncombined_data_time = combined_data.melt(id_vars=['Time', 'Country'], value_vars=['GII_change','GDP_change'], var_name = 'Change_Type', value_name = 'Value')\nGII = combined_data_time[(combined_data_time['Change_Type'] == 'GII_change')] \n\nGDP = combined_data_time[(combined_data_time['Change_Type'] == 'GDP_change')]\n\n# Stationary Check\ndef adf_test(series):\n result = adfuller(series.dropna())\n print(f'ADF Statistic: {result[0]}')\n print(f'p-value: {result[1]}')\n if result[1] < 0.05:\n print(\"Series is stationary\")\n else:\n print(\"Series is not stationary\")\n\n# Output the result\nadf_test(GII['Value'])\nadf_test(GDP['Value'])\n\nADF Statistic: -16.069430375623295\np-value: 5.527565277907626e-29\nSeries is stationary\nADF Statistic: -8.535683414310181\np-value: 1.0095278443825336e-13\nSeries is stationary\n\n\n\n\nVAR model: Examine variables separately\nWe fitted a VAR (Vector Autoreression) model to see the relationship between GII and GDP change. VAR is particularly useful when dealing with multivariate time series data and allows us to examine the interdependence between variables.\nBased on summary, here were several interpretations we could make:\n\nWe used AIC as the criteria for model selection. Lower value suggests a better fit.\nGiven that we wanted to predict GII change, we focused on the first set “Results for equation GII_change.”\nPast GII_change values significantly influenced current GII_change, as shown in the small p-values of lags 1 and 2.\nLag 2 of GDP_change had a relatively low p-value but is not statistically significant.\n\n\n# Split the dataset into training and testing sets\nsplit_ratio = 0.7\nsplit_index = int(len(combined_data) * split_ratio)\n\n# Training set is used to fit the model\ntrain_data = combined_data.iloc[:split_index]\n\n# Testing set is used for validation\ntest_data = combined_data.iloc[split_index:]\n\nprint(f\"Training data: {train_data.shape}\")\nprint(f\"Test data: {test_data.shape}\")\n\nTraining data: (639, 9)\nTest data: (275, 9)\n\n\n\n# Fit a VAR model \ntime_model = VAR(train_data[['GII_change', 'GDP_change']])\ntime_model_fitted = time_model.fit(maxlags = 15, ic=\"aic\")\n\n# Print out the model summary\nprint(time_model_fitted.summary())\n\n Summary of Regression Results \n==================================\nModel: VAR\nMethod: OLS\nDate: Wed, 25, Dec, 2024\nTime: 18:00:11\n--------------------------------------------------------------------\nNo. of Equations: 2.00000 BIC: -12.2270\nNobs: 636.000 HQIC: -12.2869\nLog likelihood: 2128.47 FPE: 4.43927e-06\nAIC: -12.3250 Det(Omega_mle): 4.34314e-06\n--------------------------------------------------------------------\nResults for equation GII_change\n================================================================================\n coefficient std. error t-stat prob\n--------------------------------------------------------------------------------\nconst -0.023760 0.003176 -7.481 0.000\nL1.GII_change 0.211342 0.039900 5.297 0.000\nL1.GDP_change -0.015713 0.046080 -0.341 0.733\nL2.GII_change 0.111672 0.040402 2.764 0.006\nL2.GDP_change 0.088293 0.046452 1.901 0.057\nL3.GII_change -0.021861 0.039774 -0.550 0.583\nL3.GDP_change 0.000651 0.046141 0.014 0.989\n================================================================================\n\nResults for equation GDP_change\n================================================================================\n coefficient std. error t-stat prob\n--------------------------------------------------------------------------------\nconst 0.014585 0.002720 5.362 0.000\nL1.GII_change 0.004128 0.034171 0.121 0.904\nL1.GDP_change 0.141566 0.039464 3.587 0.000\nL2.GII_change -0.009734 0.034600 -0.281 0.778\nL2.GDP_change 0.044282 0.039782 1.113 0.266\nL3.GII_change -0.037787 0.034063 -1.109 0.267\nL3.GDP_change 0.143870 0.039516 3.641 0.000\n================================================================================\n\nCorrelation matrix of residuals\n GII_change GDP_change\nGII_change 1.000000 0.040992\nGDP_change 0.040992 1.000000\n\n\n\n\n\n/Users/sammi/Library/Caches/pypoetry/virtualenvs/imfp-0AlIckOY-py3.13/lib/python3.13/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: An unsupported index was provided. As a result, forecasts cannot be generated. To use the model for forecasting, use one of the supported classes of index.\n self._init_dates(dates, freq)\n\n\n\n\nVAR Model: Forecasting\nWe applied the model learned above to the test data. Based on the plot, the forecast values seem to follow the actual data well, indicating a good model fit caputuring the underlying trends.\n\n# Number of steps to forecast (length of the test set)\nn_steps = len(test_data)\n\n# Get the last values from the training set for forecasting\nforecast_input = train_data[['GII_change', 'GDP_change']].values[-time_model_fitted.k_ar:]\n\n# Forecasting\nforecast = time_model_fitted.forecast(y=forecast_input, steps=n_steps)\n\n# Create a DataFrame for the forecasted values\nforecast_df = pd.DataFrame(forecast, index=test_data.index, columns=['GII_forecast', 'GDP_forecast'])\n\n# Ensure the index of the forecast_df matches the test_data index\nforecast_df.index = test_data.index\n\n\nplt.figure(figsize=(12, 6))\nplt.plot(train_data['GII_change'], label='Training GII', color='blue')\nplt.plot(test_data['GII_change'], label='Actual GII', color='orange')\nplt.plot(forecast_df['GII_forecast'], label='Forecasted GII', color='green')\nplt.title('GII Change Forecast vs Actual')\nplt.legend()\nplt.show()\n\nplt.figure(figsize=(12, 6))\nplt.plot(train_data['GDP_change'], label='Training GDP', color='blue')\nplt.plot(test_data['GDP_change'], label='Actual GDP', color='orange')\nplt.plot(forecast_df['GDP_forecast'], label='Forecasted GDP', color='green')\nplt.title('GDP Change Forecast vs Actual')\nplt.legend()\nplt.show()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nVAR Model: Model Performance\nLow values of both MAE and RMSE indicate good model performance with small average errors in predictions.\n\nmae_gii = mean_absolute_error(test_data['GII_change'], forecast_df['GII_forecast'])\nmae_gdp = mean_absolute_error(test_data['GDP_change'], forecast_df['GDP_forecast'])\n\nprint(f'Mean Absolute Error for GII: {mae_gii}')\nprint(f'Mean Absolute Error for GDP: {mae_gdp}')\n\nMean Absolute Error for GII: 0.03296113056900485\nMean Absolute Error for GDP: 0.027636232780964363\n\n\n\nrmse_gii = np.sqrt(mean_squared_error(test_data['GII_change'], forecast_df['GII_forecast']))\nrmse_gdp = np.sqrt(mean_squared_error(test_data['GDP_change'], forecast_df['GDP_forecast']))\n\nprint(f'RMSE for GII: {rmse_gii}')\nprint(f'RMSE for GDP: {rmse_gdp}')\n\nRMSE for GII: 0.06284754560659174\nRMSE for GDP: 0.038429216092797085\n\n\n\n\nVAR Model: Granger causality test\nGranger causality test evaluates whether one time series can predict another.\nBased on the output, the lowest p-value is when lag = 2. However, because p-value > 0.05, we fail to reject the null hypothesis and conclude the GDP_change does not Granger-cause the GII_change.\n\n# Perform the Granger causality test\nmax_lag = 3\ntest_result = grangercausalitytests(train_data[['GII_change', 'GDP_change']], max_lag, verbose=True)\n\n\nGranger Causality\nnumber of lags (no zero) 1\nssr based F test: F=0.0000 , p=0.9972 , df_denom=635, df_num=1\nssr based chi2 test: chi2=0.0000 , p=0.9972 , df=1\nlikelihood ratio test: chi2=0.0000 , p=0.9972 , df=1\nparameter F test: F=0.0000 , p=0.9972 , df_denom=635, df_num=1\n\nGranger Causality\nnumber of lags (no zero) 2\nssr based F test: F=1.8529 , p=0.1576 , df_denom=632, df_num=2\nssr based chi2 test: chi2=3.7351 , p=0.1545 , df=2\nlikelihood ratio test: chi2=3.7242 , p=0.1553 , df=2\nparameter F test: F=1.8529 , p=0.1576 , df_denom=632, df_num=2\n\nGranger Causality\nnumber of lags (no zero) 3\nssr based F test: F=1.2359 , p=0.2958 , df_denom=629, df_num=3\nssr based chi2 test: chi2=3.7489 , p=0.2899 , df=3\nlikelihood ratio test: chi2=3.7379 , p=0.2912 , df=3\nparameter F test: F=1.2359 , p=0.2958 , df_denom=629, df_num=3\n\n\n/Users/sammi/Library/Caches/pypoetry/virtualenvs/imfp-0AlIckOY-py3.13/lib/python3.13/site-packages/statsmodels/tsa/stattools.py:1556: FutureWarning: verbose is deprecated since functions should not print results\n warnings.warn(" - }, - { - "objectID": "imfp_demo.html#conclusion", - "href": "imfp_demo.html#conclusion", - "title": "imfp Demo Project", - "section": "Conclusion", - "text": "Conclusion\nIn wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research. Check out the blog for full analysis!" - }, - { - "objectID": "imfp_demo.html#future-directions", - "href": "imfp_demo.html#future-directions", - "title": "imfp Demo Project", - "section": "Future Directions", - "text": "Future Directions\n\nAdditional Predictors: Gender Inequality Index (GII) is a composite measure shaped by a mariad of social factors. What about culture, policy, and environment?\nSpecific Country: Are there any specific historical contexts that sparked economic growth and improvement in gender equality?\nOpposite causation: Women entering workforce boost GDP?\nPredictive Power of VAR model: Our model performed well in forecasting trends for both variables independently.\n\nLet’s keep our curiosity alive and our questions flowing!" - }, - { - "objectID": "posts/blog/listing.html", - "href": "posts/blog/listing.html", - "title": "blog", - "section": "", - "text": "No matching items" - }, - { - "objectID": "About.html", - "href": "About.html", - "title": "Jenny Xu", - "section": "", - "text": "Hi there! My name is Jenny, and I’m a third-year student at University of California, Davis, double majoring in Statistics and Psychology. I’ve always been interested in becoming a data analyst working in tech, internet, or research industries. Interning at Promptly Technologies helped me learn a ton. A quick fun fact for me is that my MBTI is ISFJ (Defender)!" - }, - { - "objectID": "listing.html", - "href": "listing.html", - "title": "Blog", - "section": "", - "text": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data\n\n\n\n\n\n\nData Analysis\n\n\n\nApply imfp in a real world data-analysis project.\n\n\n\n\n\nDec 25, 2024\n\n\nJenny Xu\n\n\n\n\n\n\nNo matching items" - }, - { - "objectID": "Code/imfp_demo.html", - "href": "Code/imfp_demo.html", - "title": "imfp Demo Project", - "section": "", - "text": "This data analysis project aims to explore the relationship between economic growth and gender equality using imfp, which allows us to download data from IMF (International Monetary Fund). imfp can be integrated with other python tools to streamline the computational process. To demonstrate its functionality, the project experimented with a variety of visualization and analysis methods.\nReady for some insights about if a stronger economy mean more equal opportunities for all genders? Let’s dive in!" - }, - { - "objectID": "Code/imfp_demo.html#executive-summary", - "href": "Code/imfp_demo.html#executive-summary", - "title": "imfp Demo Project", - "section": "Executive Summary", - "text": "Executive Summary\nIn this project, we explored the following:\n\nData Fetching\n\n\nMake API call to fetch 4 datasets: GII (Gender Inequality Index), Nominal GDP, GDP Deflator Index, Population series\n\n\nFeature Engineering\n\n\nCleaning: Convert GDP Deflator Index to a yearly basis and variables to numeric\nDependent Variable: Percent Change of Gender Inequality Index\nIndependent Variable: Percent Change of Real GDP per Capita\nTransform variables to display magnitude of change\nMerge the datasets\n\n\nData Visualization\n\n\nScatterplot\nTime Series Line Plots\nBarplot\nBoxplot\nHeatmap\n\n\nStatistical Analysis\n\n\nDescriptive Statistics\nRegression Analysis\nTime Series Analysis" - }, - { - "objectID": "Code/imfp_demo.html#suggested-packages", - "href": "Code/imfp_demo.html#suggested-packages", - "title": "imfp Demo Project", - "section": "Suggested packages", - "text": "Suggested packages\npandas: view and manipulate data frame; matplotlib.pyplot: make plots; seaborn: make plots; numpy: computation; LinearRegression: implement linear regression; tabulate: format data into tables;\nA custom module is written to simplify the process of making API calls and fetching information with imfp library. load_or_fetch_databases, load_or_fetch_parameters load_or_fetch_dataset load and retreive database, parameters, and dataset from a local or remote source. view_dataframe_in_browser displays dataframe in a web browser.\nstatsmodels.api, adfuller, ARIMA,VAR,plot_acf,plot_pacf,mean_absolute_error,mean_squared_error, andgrangercausalitytests are specifically used for time series analysis.\n\nimport os\nimport pickle\nfrom tempfile import NamedTemporaryFile\nimport pandas as pd\nimport imfp\nimport webbrowser\n\n\n# Function to display a DataFrame in a web browser\ndef view_dataframe_in_browser(df):\n html = df.to_html()\n with NamedTemporaryFile(delete=False, mode=\"w\", suffix=\".html\") as f:\n url = \"file://\" + f.name\n f.write(html)\n webbrowser.open(url)\n\n\n# Function to load databases from CSV or fetch from API\ndef load_or_fetch_databases():\n csv_path = os.path.join(\"data\", \"databases.csv\")\n\n # Try to load from CSV\n if os.path.exists(csv_path):\n try:\n return pd.read_csv(csv_path)\n except Exception as e:\n print(f\"Error loading CSV: {e}\")\n\n # If CSV doesn't exist or couldn't be loaded, fetch from API\n print(\"Fetching databases from IMF API...\")\n databases = imfp.imf_databases()\n\n # Save to CSV for future use\n databases.to_csv(csv_path, index=False)\n print(f\"Databases saved to {csv_path}\")\n\n return databases\n\n\ndef load_or_fetch_parameters(database_name):\n pickle_path = os.path.join(\"data\", f\"{database_name}.pickle\")\n\n # Try to load from pickle file\n if os.path.exists(pickle_path):\n try:\n with open(pickle_path, \"rb\") as f:\n return pickle.load(f)\n except Exception as e:\n print(f\"Error loading pickle file: {e}\")\n\n # If pickle doesn't exist or couldn't be loaded, fetch from API\n print(f\"Fetching parameters for {database_name} from IMF API...\")\n parameters = imfp.imf_parameters(database_name)\n\n # Save to pickle file for future use\n os.makedirs(\"data\", exist_ok=True) # Ensure the data directory exists\n with open(pickle_path, \"wb\") as f:\n pickle.dump(parameters, f)\n print(f\"Parameters saved to {pickle_path}\")\n\n return parameters\n\n\ndef load_or_fetch_dataset(database_id, indicator):\n file_name = f\"{database_id}.{indicator}.csv\"\n csv_path = os.path.join(\"data\", file_name)\n\n # Try to load from CSV file\n if os.path.exists(csv_path):\n try:\n return pd.read_csv(csv_path)\n except Exception as e:\n print(f\"Error loading CSV file: {e}\")\n\n # If CSV doesn't exist or couldn't be loaded, fetch from API\n print(f\"Fetching dataset for {database_id}.{indicator} from IMF API...\")\n dataset = imfp.imf_dataset(database_id=database_id, indicator=[indicator])\n\n # Save to CSV file for future use\n os.makedirs(\"data\", exist_ok=True) # Ensure the data directory exists\n dataset.to_csv(csv_path, index=False)\n print(f\"Dataset saved to {csv_path}\")\n\n return dataset\n\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\nfrom tabulate import tabulate\nimport statsmodels.api as sm\nfrom statsmodels.tsa.stattools import adfuller\nfrom statsmodels.tsa.arima.model import ARIMA\nfrom statsmodels.tsa.vector_ar.var_model import VAR\nfrom statsmodels.graphics.tsaplots import plot_acf, plot_pacf\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.metrics import mean_squared_error\nfrom statsmodels.tsa.stattools import grangercausalitytests" - }, - { - "objectID": "Code/imfp_demo.html#data-fetching", - "href": "Code/imfp_demo.html#data-fetching", - "title": "imfp Demo Project", - "section": "Data Fetching", - "text": "Data Fetching\nIn this section, we extracted four datasets through API calls: Gender Inequality Index(GII), GDP Deflator, Nominal GDP, and Population. Two databases were used: Gender Equality and International Financial Statistics (IFS).\n\nfrom pathlib import Path\nPath(\"data\").mkdir(exist_ok=True)\n\n\n# Load or fetch databases\ndatabases = load_or_fetch_databases()\n\n# Filter out databases that contain a year in the description\ndatabases[\n ~databases['description'].str.contains(r\"[\\d]{4}\", regex=True)\n]\n\n# view_dataframe_in_browser(databases)\n\n\n\n\n\n\n\n\ndatabase_id\ndescription\n\n\n\n\n25\nHPDD\nHistorical Public Debt (HPDD)\n\n\n34\nRAFIT2AGG\nRevenue Administration Fiscal Information Tool...\n\n\n47\nGENDER_EQUALITY\nGender Equality\n\n\n63\nPGCS\nPrivate and Public Capital Stock Dataset\n\n\n69\nGENDER_BUDGETING\nGender Budgeting\n\n\n125\nCPI\nConsumer Price Index (CPI)\n\n\n153\nIRFCL\nInternational Reserves and Foreign Currency Li...\n\n\n191\nIFS_DISCONTINUED\nInternational Financial Statistics (IFS), Disc...\n\n\n192\nEQ\nExport Quality\n\n\n193\nED\nExport Diversification\n\n\n226\nFISCALDECENTRALIZATION\nFiscal Decentralization\n\n\n291\nFDI\nFinancial Development Index\n\n\n292\nPSBSFAD\nPublic Sector Balance Sheet (PSBS)(FAD)\n\n\n293\nUNSDG_IMF_INPUTS\nSustainable Development Goals, IMF Inputs\n\n\n294\nCPIS\nCoordinated Portfolio Investment Survey (CPIS)\n\n\n295\nPCTOT\nCommodity Terms of Trade\n\n\n296\nFM\nFiscal Monitor (FM)\n\n\n297\nAFRREO\nSub-Saharan Africa Regional Economic Outlook (...\n\n\n298\nWHDREO\nWestern Hemisphere Regional Economic Outlook (...\n\n\n299\nMCDREO\nMiddle East and Central Asia Regional Economic...\n\n\n300\nAPDREO\nAsia and Pacific Regional Economic Outlook (AP...\n\n\n301\nBOPAGG\nBalance of Payments (BOP), World and Regional ...\n\n\n302\nPCPS\nPrimary Commodity Price System (PCPS)\n\n\n303\nCDIS\nCoordinated Direct Investment Survey (CDIS)\n\n\n304\nBOP\nBalance of Payments (BOP)\n\n\n305\nCOFER\nCurrency Composition of Official Foreign Excha...\n\n\n306\nDOT\nDirection of Trade Statistics (DOTS)\n\n\n307\nRAFIT3P\nRA-FIT Round3 Completion and Participation Rates\n\n\n308\nFAS\nFinancial Access Survey (FAS)\n\n\n309\nFSI\nFinancial Soundness Indicators (FSIs)\n\n\n310\nFSIRE\nFinancial Soundness Indicators: Reporting enti...\n\n\n311\nBOPSDMXUSD\nBalance of Payments (BOP), Global SDMX (US Dol...\n\n\n312\nNAMAIN_IDC_N\nSystem of National Accounts (SNA), NA_MAIN\n\n\n313\nGFSR\nGovernment Finance Statistics (GFS), Revenue\n\n\n314\nGFSSSUC\nGovernment Finance Statistics (GFS), Statement...\n\n\n315\nGFSCOFOG\nGovernment Finance Statistics (GFS), Expenditu...\n\n\n316\nGFSFALCS\nGovernment Finance Statistics (GFS), Financial...\n\n\n317\nGFSIBS\nGovernment Finance Statistics (GFS), Integrate...\n\n\n318\nGFSMAB\nGovernment Finance Statistics (GFS), Main Aggr...\n\n\n319\nGFSE\nGovernment Finance Statistics (GFS), Expense\n\n\n320\nIFS\nInternational Financial Statistics (IFS)\n\n\n321\nMFS\nMonetary and Financial Statistics (MFS)\n\n\n\n\n\n\n\n\ndatasets = [\"GENDER_EQUALITY\", \"IFS\"]\nparams = {}\n\n# Fetch valid parameters for two datasets\nfor dataset in datasets:\n params[dataset] = load_or_fetch_parameters(dataset)\n\n valid_keys = list(params[dataset].keys())\n print(f\"Parameters for {dataset}: \", valid_keys)\n\n# view_dataframe_in_browser(params.get(\"IFS\").get(\"indicator\"))\n\nParameters for GENDER_EQUALITY: ['freq', 'ref_area', 'indicator']\nParameters for IFS: ['freq', 'ref_area', 'indicator']\n\n\n\ndatasets = {}\ndsets = [(\"GENDER_EQUALITY\", \"GE_GII\"), (\"IFS\", \"NGDP_D_SA_IX\"), (\"IFS\", \"NGDP_XDC\"), (\"IFS\", \"LP_PE_NUM\")]\n\nfor dset in dsets:\n datasets[dset[0] + \".\" + dset[1]] = load_or_fetch_dataset(dset[0], dset[1])\n\n\n# \"Gender Inequality Index\"\nGII = \"GENDER_EQUALITY.GE_GII\"\n\n# \"Gross Domestic Product, Deflator, Seasonally Adjusted, Index\"\nGDP_deflator = \"IFS.NGDP_D_SA_IX\"\n\n# \"Gross Domestic Product, Nominal, Domestic Currency\"\nGDP_nominal = \"IFS.NGDP_XDC\"\n\n# \"Population, Persons, Number of\"\nGDP_population = \"IFS.LP_PE_NUM\"\n\n# Assign the datasets to new variables so we don't change the originals\nGII_data = datasets[GII]\nGDP_deflator_data = datasets[GDP_deflator]\nGDP_nominal_data = datasets[GDP_nominal]\nGDP_population_data = datasets[GDP_population]" - }, - { - "objectID": "Code/imfp_demo.html#feature-engineering", - "href": "Code/imfp_demo.html#feature-engineering", - "title": "imfp Demo Project", - "section": "Feature Engineering", - "text": "Feature Engineering\n\nData Cleaning\nSince the GDP deflator is reported on a quarterly basis, we need to convert it to a yearly basis.\n\n# Filter to keep only rows with a partial string match for \"Q4\" in the time_period column\nGDP_deflator_data = GDP_deflator_data[GDP_deflator_data['time_period'].str.contains(\"Q4\")]\n\n\n# Split the time_period into year and quarter and keep the year only\nGDP_deflator_data.loc[:, 'time_period'] = GDP_deflator_data['time_period'].str[0:4]\n\n\ndatasets = [GII_data, GDP_deflator_data, GDP_nominal_data, GDP_population_data]\n\nfor i, dataset in enumerate(datasets): \n # Use .loc to modify the columns\n datasets[i].loc[:, 'obs_value'] = pd.to_numeric(datasets[i]['obs_value'], errors='coerce')\n datasets[i].loc[:, 'time_period'] = pd.to_numeric(datasets[i]['time_period'], errors='coerce')\n datasets[i].loc[:, 'unit_mult'] = pd.to_numeric(datasets[i]['unit_mult'], errors='coerce')\n\n\n# Calculate percent change for each ref_area\nGII_data[f'pct_change'] = GII_data.sort_values(['ref_area', 'time_period']).groupby('ref_area')['obs_value'].pct_change()\n\n# Display the first few rows of the updated dataset\nGII_data.head()\n\n\n\n\n\n\n\n\nfreq\nref_area\nindicator\nunit_mult\ntime_format\ntime_period\nobs_value\npct_change\n\n\n\n\n0\nA\nAF\nGE_GII\n0\nP1Y\n1990\n0.828244\nNaN\n\n\n1\nA\nAF\nGE_GII\n0\nP1Y\n1991\n0.817706\n-0.012723\n\n\n2\nA\nAF\nGE_GII\n0\nP1Y\n1992\n0.809806\n-0.009662\n\n\n3\nA\nAF\nGE_GII\n0\nP1Y\n1993\n0.803078\n-0.008308\n\n\n4\nA\nAF\nGE_GII\n0\nP1Y\n1994\n0.797028\n-0.007533\n\n\n\n\n\n\n\n\n# Create a new dataframe with only the required columns\nGII_data = GII_data[['ref_area', 'time_period', 'pct_change']].copy()\n\n# Display the first few rows of the new dataset\nGII_data.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\npct_change\n\n\n\n\n0\nAF\n1990\nNaN\n\n\n1\nAF\n1991\n-0.012723\n\n\n2\nAF\n1992\n-0.009662\n\n\n3\nAF\n1993\n-0.008308\n\n\n4\nAF\n1994\n-0.007533\n\n\n\n\n\n\n\n\n# GDP Deflator Dataset\n# Create a new dataframe with only the required columns\nGDP_deflator_data = GDP_deflator_data[['ref_area', 'time_period', 'unit_mult', 'obs_value']].copy()\n\n# Divide obs_value by 100\nGDP_deflator_data['obs_value'] = GDP_deflator_data['obs_value'] / 100\n\n# Display the first few rows of the new dataset\nGDP_deflator_data.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nunit_mult\nobs_value\n\n\n\n\n3\nFI\n1990\n0\n0.732006\n\n\n7\nFI\n1991\n0\n0.739841\n\n\n11\nFI\n1992\n0\n0.746543\n\n\n15\nFI\n1993\n0\n0.756193\n\n\n19\nFI\n1994\n0\n0.779373\n\n\n\n\n\n\n\n\n# GDP Nominal Data\n# Create a new dataframe with only the required columns\nGDP_nominal_data = GDP_nominal_data[['ref_area', 'time_period', 'unit_mult','obs_value']].copy()\n\n# Display the first few rows of the new dataset\nGDP_nominal_data.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nunit_mult\nobs_value\n\n\n\n\n0\nNE\n2005\n6\n2418864.0\n\n\n1\nNE\n2006\n6\n2596972.0\n\n\n2\nNE\n2007\n6\n2762961.0\n\n\n3\nNE\n2008\n6\n3247835.0\n\n\n4\nNE\n2009\n6\n3403683.0\n\n\n\n\n\n\n\n\n# GDP Population Data \n# Create a new dataframe with only the required columns\nGDP_population_data = GDP_population_data[['ref_area', 'time_period', 'unit_mult','obs_value']].copy()\n\n# Display the first few rows of the new dataset\nGDP_population_data.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nunit_mult\nobs_value\n\n\n\n\n0\nGA\n1950\n3\n473.296\n\n\n1\nGA\n1951\n3\n476.381\n\n\n2\nGA\n1952\n3\n478.655\n\n\n3\nGA\n1953\n3\n480.536\n\n\n4\nGA\n1954\n3\n482.332\n\n\n\n\n\n\n\n\n# Combine all the datasets above for further calculation\nmerged_df = pd.merge(pd.merge(GDP_deflator_data,GDP_nominal_data, on=['time_period', 'ref_area'], suffixes=('_index', '_nominal'), how='inner'), GDP_population_data, on=['time_period', 'ref_area'], how='inner')\n\n# Display the GDP data in a web browser\n# view_dataframe_in_browser(merged_df)\n\n# Display the first few rows of the dataset\nmerged_df.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nunit_mult_index\nobs_value_index\nunit_mult_nominal\nobs_value_nominal\nunit_mult\nobs_value\n\n\n\n\n0\nFI\n1990\n0\n0.732006\n6\n90964.0\n3\n4996.220\n\n\n1\nFI\n1991\n0\n0.739841\n6\n86913.0\n3\n5019.134\n\n\n2\nFI\n1992\n0\n0.746543\n6\n84786.0\n3\n5044.928\n\n\n3\nFI\n1993\n0\n0.756193\n6\n85610.0\n3\n5071.782\n\n\n4\nFI\n1994\n0\n0.779373\n6\n90646.0\n3\n5097.090\n\n\n\n\n\n\n\n\nmerged_df['adjusted_index'] = merged_df['obs_value_index'] * (10 ** (merged_df['unit_mult_index']))\nmerged_df['adjusted_nominal'] = merged_df['obs_value_nominal'] * (10 ** (merged_df['unit_mult_nominal']))\nmerged_df['adjusted_population'] = merged_df['obs_value'] * (10 ** (merged_df['unit_mult']))\n\n\n# Merged dataset\n# Create a new dataframe with only the required columns\nmerged_df = merged_df[['ref_area', 'time_period','adjusted_nominal', 'adjusted_index', 'adjusted_population']].copy()\n\n# Display the first few rows of the dataset\nmerged_df.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nadjusted_nominal\nadjusted_index\nadjusted_population\n\n\n\n\n0\nFI\n1990\n9.096400e+10\n0.732006\n4996220.0\n\n\n1\nFI\n1991\n8.691300e+10\n0.739841\n5019134.0\n\n\n2\nFI\n1992\n8.478600e+10\n0.746543\n5044928.0\n\n\n3\nFI\n1993\n8.561000e+10\n0.756193\n5071782.0\n\n\n4\nFI\n1994\n9.064600e+10\n0.779373\n5097090.0\n\n\n\n\n\n\n\n\n# Step 1: Real GDP = (Nominal GDP / GDP Deflator Index)\nmerged_df['Real_GDP_domestic'] = (merged_df['adjusted_nominal'] / merged_df['adjusted_index'])\n\n# Step 2: Real GDP per Capita = Real GDP / Population\nmerged_df['Real_GDP_per_capita'] = merged_df['Real_GDP_domestic'] / merged_df['adjusted_population']\n\n# Check the results\nmerged_df.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nadjusted_nominal\nadjusted_index\nadjusted_population\nReal_GDP_domestic\nReal_GDP_per_capita\n\n\n\n\n0\nFI\n1990\n9.096400e+10\n0.732006\n4996220.0\n1.242667e+11\n24872.143699\n\n\n1\nFI\n1991\n8.691300e+10\n0.739841\n5019134.0\n1.174753e+11\n23405.490395\n\n\n2\nFI\n1992\n8.478600e+10\n0.746543\n5044928.0\n1.135715e+11\n22512.011198\n\n\n3\nFI\n1993\n8.561000e+10\n0.756193\n5071782.0\n1.132119e+11\n22321.919259\n\n\n4\nFI\n1994\n9.064600e+10\n0.779373\n5097090.0\n1.163063e+11\n22818.181344\n\n\n\n\n\n\n\n\n# Calculate percent change for each ref_area\nmerged_df[f'GDP_change'] = merged_df.sort_values(['ref_area', 'time_period']).groupby('ref_area')['Real_GDP_per_capita'].pct_change()\n\n# Rename dataset\nGDP_data = merged_df\n\n# Display the first few rows of the dataset\nGDP_data.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\nadjusted_nominal\nadjusted_index\nadjusted_population\nReal_GDP_domestic\nReal_GDP_per_capita\nGDP_change\n\n\n\n\n0\nFI\n1990\n9.096400e+10\n0.732006\n4996220.0\n1.242667e+11\n24872.143699\nNaN\n\n\n1\nFI\n1991\n8.691300e+10\n0.739841\n5019134.0\n1.174753e+11\n23405.490395\n-0.058968\n\n\n2\nFI\n1992\n8.478600e+10\n0.746543\n5044928.0\n1.135715e+11\n22512.011198\n-0.038174\n\n\n3\nFI\n1993\n8.561000e+10\n0.756193\n5071782.0\n1.132119e+11\n22321.919259\n-0.008444\n\n\n4\nFI\n1994\n9.064600e+10\n0.779373\n5097090.0\n1.163063e+11\n22818.181344\n0.022232\n\n\n\n\n\n\n\n\n# GII and GDP\n# Merge the datasets\ncombined_data = pd.merge(GII_data, GDP_data, on=[\"ref_area\", \"time_period\"], how = \"inner\")\n\n# Check the combined dataset\ncombined_data.head()\n\n\n\n\n\n\n\n\nref_area\ntime_period\npct_change\nadjusted_nominal\nadjusted_index\nadjusted_population\nReal_GDP_domestic\nReal_GDP_per_capita\nGDP_change\n\n\n\n\n0\nAR\n2004\n-0.021896\n4.851153e+11\n0.409672\n38491970.0\n1.184155e+12\n30763.687776\nNaN\n\n\n1\nAR\n2005\n-0.008581\n5.825382e+11\n0.470108\n38892924.0\n1.239159e+12\n31860.772776\n0.035662\n\n\n2\nAR\n2006\n0.007379\n7.159043e+11\n0.526535\n39289876.0\n1.359651e+12\n34605.628084\n0.086152\n\n\n3\nAR\n2007\n-0.020117\n8.969801e+11\n0.629139\n39684303.0\n1.425727e+12\n35926.734575\n0.038176\n\n\n4\nAR\n2008\n0.000733\n1.149646e+12\n0.757753\n40080159.0\n1.517178e+12\n37853.600252\n0.053633\n\n\n\n\n\n\n\n\n# GII and GDP\n# Rename columns\ncombined_data = combined_data.rename(columns = {\n \"ref_area\": \"Country\",\n \"time_period\": \"Time\",\n \"pct_change\": \"GII_change\"\n})\n\n# Display the GDP data in a web browser\n# view_dataframe_in_browser(combined_data)\n\n# Check the combined dataset\ncombined_data.head()\n\n\n\n\n\n\n\n\nCountry\nTime\nGII_change\nadjusted_nominal\nadjusted_index\nadjusted_population\nReal_GDP_domestic\nReal_GDP_per_capita\nGDP_change\n\n\n\n\n0\nAR\n2004\n-0.021896\n4.851153e+11\n0.409672\n38491970.0\n1.184155e+12\n30763.687776\nNaN\n\n\n1\nAR\n2005\n-0.008581\n5.825382e+11\n0.470108\n38892924.0\n1.239159e+12\n31860.772776\n0.035662\n\n\n2\nAR\n2006\n0.007379\n7.159043e+11\n0.526535\n39289876.0\n1.359651e+12\n34605.628084\n0.086152\n\n\n3\nAR\n2007\n-0.020117\n8.969801e+11\n0.629139\n39684303.0\n1.425727e+12\n35926.734575\n0.038176\n\n\n4\nAR\n2008\n0.000733\n1.149646e+12\n0.757753\n40080159.0\n1.517178e+12\n37853.600252\n0.053633" - }, - { - "objectID": "Code/imfp_demo.html#data-visualization", - "href": "Code/imfp_demo.html#data-visualization", - "title": "imfp Demo Project", - "section": "Data Visualization", - "text": "Data Visualization\n\nScatterplot\nScatterplot use dots to represent values of two numeric variables. The horizontal axis is the percent change in Real GDP per capita. The vertical axis is the percent change in Gender Inequality Index(GII). Different colors represent different countries. We used a linear regression line to display the overall pattern.\nBased on the scatterplot, it seems like there is a slight positive relationship between GDP change and GII change as shown by the flat regression line. Gender inequality is decreasing (gender equality is improving) a little faster in country-years with low GDP growth and a little slower in country-years with high GDP growth.\n\n# Drop NAs\ncombined_data = combined_data.dropna(subset=['GDP_change', 'GII_change'])\n\n# Plot the data points\nplt.figure(figsize=(18, 10))\nfor country in combined_data['Country'].unique():\n country_data = combined_data[combined_data['Country'] == country]\n plt.scatter(country_data['GDP_change'], country_data['GII_change'],\n marker='o',linestyle='-', label=country)\nplt.title('Country-Year Analysis of GDP Change vs. GII Change')\nplt.xlabel('Percent Change in Real GDP per Capita (Country-Year)')\nplt.ylabel('Percent Change in GII (Country-Year)')\nplt.grid(True)\n\n# Prepare data for linear regression\nX = combined_data['GDP_change'].values.reshape(-1, 1)\ny = combined_data['GII_change'].values\n\n# Perform linear regression\nreg = LinearRegression().fit(X, y)\ny_pred = reg.predict(X)\n\n# Plot the regression line\nplt.plot(combined_data['GDP_change'], y_pred, color='red', linewidth=2)\n\nplt.show()\n\n\n\n\n\n\n\n\n\n\nTime Series Line Plot\nCreate separate line plots for GDP change and GII change over time for a few key countries might show the trends more clearly.\nUS: United States JP: Japan GB: United Kindom FR: France MX: Mexico\nBased on the line plots, we see GDP change and GII change have different patterns. For example, in Mexico, when there is a big change in real GDP per captia in 1995, the change in GII is pretty stable.\n\n# Time Series Line plot for a few key countries\nselected_countries = ['US', 'JP', 'GB', 'FR', 'MX']\ncombined_data_selected = combined_data[combined_data['Country'].isin(selected_countries)]\n\n# Set up the Plot Structure\nfig, ax = plt.subplots(2, 1, figsize=(18, 10), sharex=True)\n\n# Plot change in real GDP per capita over time\nsns.lineplot(data = combined_data_selected, x = \"Time\", y = \"GDP_change\", hue = \"Country\", ax = ax[0])\nax[0].set_title(\"Percent Change in Real GDP per Capita Over Time\")\nax[0].set_ylabel(\"Percent Change in Real GDP per Capita\")\n\n# Plot change in GII over time\nsns.lineplot(data = combined_data_selected, x = \"Time\", y = \"GII_change\", hue = \"Country\", ax = ax[1])\nax[1].set_title(\"Percent Change in GII over Time\")\nax[1].set_xlabel(\"Time\")\nax[1].set_ylabel(\"GII\")\n\nplt.tight_layout\nplt.show()\n\n\n\n\n\n\n\n\n\n\nBarplot\nUse a barplot to show average changes in GII and GDP percent change for each country to visualize regions where inequality is improving or worsening.\nThis plot supports our previous observation how GII change seems to be not be correlated with GDP change. We also saw that, for country SI, Solvenia, there seems to be a large improvement in gender inequality.\n\n# Barplot using average GII and GDP change\n# Calculate average change for each country\ncombined_data_avg = combined_data.groupby('Country')[['GII_change','GDP_change']].mean().reset_index()\n\n# Prepare to plot structure \nplt.figure(figsize = (18,10))\n\n# Create the barplot\ncombined_data_avg.plot(kind = 'bar', x = 'Country')\nplt.ylabel('Average Change')\nplt.xlabel('Country')\nplt.legend(['GII change', 'GDP change'])\nplt.grid(axis = 'y')\n\n# Show the plot\nplt.show()\n\n<Figure size 1728x960 with 0 Axes>\n\n\n\n\n\n\n\n\n\n\n\nBoxplot\nUse boxplot to visualize the distribution of GDP and GII change by country, providing information about spread, median, and potential outliers. To provide a more informative view, we sequence countries in an ascending order by the median of percent change in GDP.\nThe boxplot displays a slight upward trend with no obvious pattern between GDP and GII change. In coutries with higher GDP change median, they also tend to have a larger spread of the GDP change. The median of GII change remains stable regardless of the magnitude of GDP change, implying weak or no association between GDP and GII change. We observe a potential outlier for country SI, Solvenia, which may explains its large improvement in Gender inequality.\n\n# Box plot for GII and GDP change\n# Melt the dataframe to long format for combined boxplot\ncombined_data_melted = combined_data.melt(id_vars=['Country'], value_vars=['GII_change', 'GDP_change'], \n var_name='Change_Type', value_name='Value')\n\ngdp_medians = combined_data.groupby('Country')['GDP_change'].median().sort_values()\n\ncombined_data_melted['Country'] = pd.Categorical(combined_data_melted['Country'], categories=gdp_medians.index, ordered= True)\n\n# Prepare the plot structure\nplt.figure(figsize=(18, 10))\nsns.boxplot(data = combined_data_melted, x = \"Country\", y = 'Value', hue = 'Change_Type')\nplt.title('Distribution of GII and GDP change by Country')\nplt.xlabel('Country')\nplt.ylabel('Change')\nplt.legend(title = 'Change Type')\n\n# Show the plot\nplt.show()\n\n\n\n\n\n\n\n\n\n\nCorrelation Matrix\nCreate a heatmap to show the relationship between GII and GDP change.\nA positive correlation coefficient indicates a positive relationship: the larger the GDP change, the larger the GII change. A negative correlation coefficient indicates a negative relationship: the larger the GDP change, the smaller the GII change. A correlation coefficient closer to 0 indicates there is weak or no relationship.\nBased on the numeric values in the plot, there is a moderately strong positive correlation between GII and GDP change for country Estonia(EE) and Ireland(IE).\n\n# Calculate the correlation\ncountry_correlation = combined_data.groupby('Country')[['GII_change', 'GDP_change']].corr().iloc[0::2, -1].reset_index(name='Correlation')\n\n# Put the correlation value in a matrix format\ncorrelation_matrix = country_correlation.pivot(index='Country', columns='level_1', values='Correlation')\n\n# Check for NaN values in the correlation matrix\ncorrelation_matrix.fillna(0, inplace=True) # Replace NaNs with 0 or another value as appropriate\n\n# Set up the plot structure\nplt.figure(figsize=(18, 12)) # Adjust height to give more space for y-axis labels\n\n# Plot the heatmap\nsns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0, cbar_kws={\"shrink\": .8}, linewidths=.5)\n\n# Enhance axis labels and title\nplt.title('Heatmap for GII and GDP Change', fontsize=20)\nplt.xlabel('Variables', fontsize=16)\nplt.ylabel('Country', fontsize=16)\n\n# Improve readability of y-axis labels\nplt.yticks(fontsize=12) # Adjust the font size for y-axis labels\n\n# Show the plot\nplt.show()" - }, - { - "objectID": "Code/imfp_demo.html#statistical-analysis", - "href": "Code/imfp_demo.html#statistical-analysis", - "title": "imfp Demo Project", - "section": "Statistical Analysis", - "text": "Statistical Analysis\n\nDescriptive Statistics\nThere is a total of 915 data points. The mean of the GII change in -0.0314868, which indicates the overall grand mean percent change in gender inequality index is -3.15%. The mean of the GDP change is 0.0234633, showing the overall grand mean percent change in real GDP per capita is 2.35%.\n\n# Generate summary statistics\ndescriptive_stats = combined_data.describe()\nprint(tabulate(descriptive_stats, headers='keys', tablefmt='github'))\n\n| | GII_change | adjusted_nominal | adjusted_index | adjusted_population | Real_GDP_domestic | Real_GDP_per_capita | GDP_change |\n|-------|--------------|--------------------|------------------|-----------------------|---------------------|-----------------------|--------------|\n| count | 914 | 914 | 914 | 914 | 914 | 914 | 914 |\n| mean | -0.0314506 | 7.85541e+13 | 0.855504 | 4.50061e+07 | 7.60737e+13 | 1.00595e+06 | 0.023532 |\n| std | 0.054783 | 6.01311e+14 | 0.210057 | 1.24493e+08 | 5.52717e+14 | 3.88155e+06 | 0.0418127 |\n| min | -0.755003 | 2.18714e+09 | 0.0360636 | 396324 | 5.21733e+09 | 1798.42 | -0.285847 |\n| 25% | -0.0424105 | 1.07655e+11 | 0.743355 | 5.0737e+06 | 1.48206e+11 | 17176 | 0.00384809 |\n| 50% | -0.020248 | 7.50706e+11 | 0.888883 | 1.0337e+07 | 9.94933e+11 | 35401 | 0.0222486 |\n| 75% | -0.00451301 | 2.5911e+12 | 1.00175 | 4.5159e+07 | 2.9412e+12 | 120391 | 0.0427318 |\n| max | 0.156992 | 9.54613e+15 | 2.0788 | 1.28084e+09 | 7.92007e+15 | 3.14532e+07 | 0.241984 |\n\n\n\n\nRegression Analysis\nSimple linear regression as a foundational approach provide us with a basic understanding of the relationship between GDP change and GII change.\nBased on the summary, we can conclude the following: * Becasue p-value = 0.057, if we set alpha, the significance level, to be 0.05, we fail to reject the null hypothesis and conclude there is no significant relationship between percent change in real GDP per capita and gender inequality index.\n\nR-squared = 0.004. Only 0.4% of the variance in GII change can be explained by GDP change.\nWe are 95% confident that the interval from -0.003 to 0.169 captures the true slope of GDP change. Because 0 is included, we are uncertain about the effect of GDP change on GII chnage.\n\n\n# Define independent and depenent variables\nX = combined_data['GDP_change']\ny = combined_data['GII_change']\n\n# Add a constant to indepdent variable to include an intercept\nX = sm.add_constant(X)\n\n# Fit a simple linear regresion model and print out the summary\nmodel = sm.OLS(y, X).fit()\nprint(model.summary())\n\n OLS Regression Results \n==============================================================================\nDep. Variable: GII_change R-squared: 0.004\nModel: OLS Adj. R-squared: 0.003\nMethod: Least Squares F-statistic: 3.551\nDate: Wed, 25 Dec 2024 Prob (F-statistic): 0.0598\nTime: 16:32:05 Log-Likelihood: 1360.0\nNo. Observations: 914 AIC: -2716.\nDf Residuals: 912 BIC: -2706.\nDf Model: 1 \nCovariance Type: nonrobust \n==============================================================================\n coef std err t P>|t| [0.025 0.975]\n------------------------------------------------------------------------------\nconst -0.0334 0.002 -16.069 0.000 -0.037 -0.029\nGDP_change 0.0816 0.043 1.885 0.060 -0.003 0.167\n==============================================================================\nOmnibus: 841.660 Durbin-Watson: 1.384\nProb(Omnibus): 0.000 Jarque-Bera (JB): 53999.190\nSkew: -3.938 Prob(JB): 0.00\nKurtosis: 39.823 Cond. No. 23.9\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\n\n\n\nTime Series Analysis\nTime series analysis allows us to explore how the relationship between GII and GDP change vary across different time periods, accounting for lagged effects.\nHere is a quick summary of the result: * Both GII and GDP change time series are stationary.\n\nPast GII change values significantly influence cuurent GII change values.\nVAR model has good model performance on forecasting future values based on historical data.\nChanges in GDP does not cause/precde the changes in GII.\n\n\nADF Test: Stationality Assumption Check\nWe want to use Augmented Dickey-Fuller (ADF) test to check whether a time series is stationary, which is the model assumption for many time series models.\nStationarity implies constant mean and variance over time, making it more predictable and stable for forecasting.\nBased on the ADF test output, both GII and GDP change time series are stationary. We can proceed to the time series modeling section.\n\n# Augmented Dickey-Fuller (ADF) test for stationarity check\n# Create melted datasets\ncombined_data_time = combined_data.melt(id_vars=['Time', 'Country'], value_vars=['GII_change','GDP_change'], var_name = 'Change_Type', value_name = 'Value')\nGII = combined_data_time[(combined_data_time['Change_Type'] == 'GII_change')] \n\nGDP = combined_data_time[(combined_data_time['Change_Type'] == 'GDP_change')]\n\n# Stationary Check\ndef adf_test(series):\n result = adfuller(series.dropna())\n print(f'ADF Statistic: {result[0]}')\n print(f'p-value: {result[1]}')\n if result[1] < 0.05:\n print(\"Series is stationary\")\n else:\n print(\"Series is not stationary\")\n\n# Output the result\nadf_test(GII['Value'])\nadf_test(GDP['Value'])\n\nADF Statistic: -16.069430375623295\np-value: 5.527565277907626e-29\nSeries is stationary\nADF Statistic: -8.535683414310181\np-value: 1.0095278443825336e-13\nSeries is stationary\n\n\n\n\nVAR model: Examine variables separately\nWe fit a VAR (Vector Autoreression) model to see the relationship between GII and GDP change. VAR is particularly useful when dealing with multivariate time series data and allows us to examine the interdependence between variables.\nBased on summary, here are several interpretations we can make: - We used AIC as the criteria for model selection. Lower value suggests a better fit. - Given that we want to predict GII change, we will focus on the first set “Results for equation GII_change.” - Past GII_change values significantly influence current GII_change, as shown in the small p-values of lags 1 and 2. - Lag 2 of GDP_change has a relatively low p-value but is not statistically significant.\n\n# Split the dataset into training and testing sets\nsplit_ratio = 0.7\nsplit_index = int(len(combined_data) * split_ratio)\n\n# Training set is used to fit the model\ntrain_data = combined_data.iloc[:split_index]\n\n# Testing set is used for validation\ntest_data = combined_data.iloc[split_index:]\n\nprint(f\"Training data: {train_data.shape}\")\nprint(f\"Test data: {test_data.shape}\")\n\nTraining data: (639, 9)\nTest data: (275, 9)\n\n\n\n# Fit a VAR model \ntime_model = VAR(train_data[['GII_change', 'GDP_change']])\ntime_model_fitted = time_model.fit(maxlags = 15, ic=\"aic\")\n\n# Print out the model summary\nprint(time_model_fitted.summary())\n\n Summary of Regression Results \n==================================\nModel: VAR\nMethod: OLS\nDate: Wed, 25, Dec, 2024\nTime: 16:32:06\n--------------------------------------------------------------------\nNo. of Equations: 2.00000 BIC: -12.2270\nNobs: 636.000 HQIC: -12.2869\nLog likelihood: 2128.47 FPE: 4.43927e-06\nAIC: -12.3250 Det(Omega_mle): 4.34314e-06\n--------------------------------------------------------------------\nResults for equation GII_change\n================================================================================\n coefficient std. error t-stat prob\n--------------------------------------------------------------------------------\nconst -0.023760 0.003176 -7.481 0.000\nL1.GII_change 0.211342 0.039900 5.297 0.000\nL1.GDP_change -0.015713 0.046080 -0.341 0.733\nL2.GII_change 0.111672 0.040402 2.764 0.006\nL2.GDP_change 0.088293 0.046452 1.901 0.057\nL3.GII_change -0.021861 0.039774 -0.550 0.583\nL3.GDP_change 0.000651 0.046141 0.014 0.989\n================================================================================\n\nResults for equation GDP_change\n================================================================================\n coefficient std. error t-stat prob\n--------------------------------------------------------------------------------\nconst 0.014585 0.002720 5.362 0.000\nL1.GII_change 0.004128 0.034171 0.121 0.904\nL1.GDP_change 0.141566 0.039464 3.587 0.000\nL2.GII_change -0.009734 0.034600 -0.281 0.778\nL2.GDP_change 0.044282 0.039782 1.113 0.266\nL3.GII_change -0.037787 0.034063 -1.109 0.267\nL3.GDP_change 0.143870 0.039516 3.641 0.000\n================================================================================\n\nCorrelation matrix of residuals\n GII_change GDP_change\nGII_change 1.000000 0.040992\nGDP_change 0.040992 1.000000\n\n\n\n\n\n/Users/sammi/Library/Caches/pypoetry/virtualenvs/imfp-0AlIckOY-py3.13/lib/python3.13/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: An unsupported index was provided. As a result, forecasts cannot be generated. To use the model for forecasting, use one of the supported classes of index.\n self._init_dates(dates, freq)\n\n\n\n\nVAR Model: Forecasting\nWe apply the model learned above to the test data. Based on the plot, the forecast values seem to follow the actual data well, indicating a good model fit caputuring the underlying trends.\n\n# Number of steps to forecast (length of the test set)\nn_steps = len(test_data)\n\n# Get the last values from the training set for forecasting\nforecast_input = train_data[['GII_change', 'GDP_change']].values[-time_model_fitted.k_ar:]\n\n# Forecasting\nforecast = time_model_fitted.forecast(y=forecast_input, steps=n_steps)\n\n# Create a DataFrame for the forecasted values\nforecast_df = pd.DataFrame(forecast, index=test_data.index, columns=['GII_forecast', 'GDP_forecast'])\n\n# Ensure the index of the forecast_df matches the test_data index\nforecast_df.index = test_data.index\n\n\nplt.figure(figsize=(12, 6))\nplt.plot(train_data['GII_change'], label='Training GII', color='blue')\nplt.plot(test_data['GII_change'], label='Actual GII', color='orange')\nplt.plot(forecast_df['GII_forecast'], label='Forecasted GII', color='green')\nplt.title('GII Change Forecast vs Actual')\nplt.legend()\nplt.show()\n\nplt.figure(figsize=(12, 6))\nplt.plot(train_data['GDP_change'], label='Training GDP', color='blue')\nplt.plot(test_data['GDP_change'], label='Actual GDP', color='orange')\nplt.plot(forecast_df['GDP_forecast'], label='Forecasted GDP', color='green')\nplt.title('GDP Change Forecast vs Actual')\nplt.legend()\nplt.show()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nVAR Model: Model Performance\nLow values of both MAE and RMSE indicate good model performance with small average errors in predictions.\n\nmae_gii = mean_absolute_error(test_data['GII_change'], forecast_df['GII_forecast'])\nmae_gdp = mean_absolute_error(test_data['GDP_change'], forecast_df['GDP_forecast'])\n\nprint(f'Mean Absolute Error for GII: {mae_gii}')\nprint(f'Mean Absolute Error for GDP: {mae_gdp}')\n\nMean Absolute Error for GII: 0.03296113056900485\nMean Absolute Error for GDP: 0.027636232780964363\n\n\n\nrmse_gii = np.sqrt(mean_squared_error(test_data['GII_change'], forecast_df['GII_forecast']))\nrmse_gdp = np.sqrt(mean_squared_error(test_data['GDP_change'], forecast_df['GDP_forecast']))\n\nprint(f'RMSE for GII: {rmse_gii}')\nprint(f'RMSE for GDP: {rmse_gdp}')\n\nRMSE for GII: 0.06284754560659174\nRMSE for GDP: 0.038429216092797085\n\n\n\n\nVAR Model: Granger causality test\nGranger causality test evaluates whether one time series can predict another.\nBased on the output, the lowest p-value is when lag = 2. However, because p-value > 0.05, we fail to reject the null hypothesis and conclude the GDP_change does not Granger-cause the GII_change.\n\n# Perform the Granger causality test\nmax_lag = 3\ntest_result = grangercausalitytests(train_data[['GII_change', 'GDP_change']], max_lag, verbose=True)\n\n\nGranger Causality\nnumber of lags (no zero) 1\nssr based F test: F=0.0000 , p=0.9972 , df_denom=635, df_num=1\nssr based chi2 test: chi2=0.0000 , p=0.9972 , df=1\nlikelihood ratio test: chi2=0.0000 , p=0.9972 , df=1\nparameter F test: F=0.0000 , p=0.9972 , df_denom=635, df_num=1\n\nGranger Causality\nnumber of lags (no zero) 2\nssr based F test: F=1.8529 , p=0.1576 , df_denom=632, df_num=2\nssr based chi2 test: chi2=3.7351 , p=0.1545 , df=2\nlikelihood ratio test: chi2=3.7242 , p=0.1553 , df=2\nparameter F test: F=1.8529 , p=0.1576 , df_denom=632, df_num=2\n\nGranger Causality\nnumber of lags (no zero) 3\nssr based F test: F=1.2359 , p=0.2958 , df_denom=629, df_num=3\nssr based chi2 test: chi2=3.7489 , p=0.2899 , df=3\nlikelihood ratio test: chi2=3.7379 , p=0.2912 , df=3\nparameter F test: F=1.2359 , p=0.2958 , df_denom=629, df_num=3\n\n\n/Users/sammi/Library/Caches/pypoetry/virtualenvs/imfp-0AlIckOY-py3.13/lib/python3.13/site-packages/statsmodels/tsa/stattools.py:1556: FutureWarning: verbose is deprecated since functions should not print results\n warnings.warn(" - }, - { - "objectID": "Code/imfp_demo.html#conclusion", - "href": "Code/imfp_demo.html#conclusion", - "title": "imfp Demo Project", - "section": "Conclusion", - "text": "Conclusion\nIn wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research." - }, - { - "objectID": "Code/imfp_demo.html#future-directions", - "href": "Code/imfp_demo.html#future-directions", - "title": "imfp Demo Project", - "section": "Future Directions", - "text": "Future Directions\n\nAdditional Predictors: Gender Inequality Index (GII) is a composite measure shaped by a mariad of social factors. What about culture, policy, and environment?\nSpecific Country: Are there any specific historical contexts that sparked economic growth and improvement in gender equality?\nOpposite causation: Women entering workforce boost GDP?\nPredictive Power of VAR model: Our model performed well in forecasting trends for both variables independently.\n\nLet’s keep our curiosity alive and our questions flowing!" - }, - { - "objectID": "Blog/blog/Blog.html", - "href": "Blog/blog/Blog.html", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "", - "text": "Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders?\nOur main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables.\nReady for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!" - }, - { - "objectID": "Blog/blog/Blog.html#introduction", - "href": "Blog/blog/Blog.html#introduction", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "", - "text": "Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders?\nOur main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables.\nReady for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!" - }, - { - "objectID": "Blog/blog/Blog.html#data-preparation", - "href": "Blog/blog/Blog.html#data-preparation", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "Data Preparation", - "text": "Data Preparation\n​​To explore how economic growth connects with gender equality, we turned to the Imfp library to access a wealth of global economic data, ready to analyze through some straightforward API calls. Let’s break down our two main variables of interest. Here’s the lowdown on the 4 key data series we pulled together:\n\n\nDependent Variable: Percent Change in Gender Inequality Index (GII)\nOur gender equality measure, the Gender Inequality Index (GII), is a composite score that reflects inequality in three key areas: reproductive health, empowerment, and labor market, ranging from 0 (equality) to 1 (inequality). We calculated the percent change in GII over time to make it easier to compare GII change across countries with different baseline levels of inequality.\n\n\nIndependent Variable: Percent Change in Real GDP per capita\nOur economic growth measure is real GDP per capita, an indicator of a country’s economic output per person, adjusted for inflation. The higher the real GDP per capita, the wealthier and more prosperous a country is. Here is a peek at the formula:" - }, - { - "objectID": "Blog/blog/Blog.html#data-visualization", - "href": "Blog/blog/Blog.html#data-visualization", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "Data Visualization", - "text": "Data Visualization\n\nScatterplot\nNothing like a good scatterplot to help us understand the relationship between two numbers, right? For our project, the scatterplot lets us visually explore how Percent Change in Real GDP per capita relates to Percent Change in GII. Each dot on the plot represents a country-year combo. The gentle upward slope suggests that in years of low GDP growth, gender inequality tends to decrease a bit faster, while in years of high GDP growth, the inequality drops at a slightly slower rate. It’s a subtle clue, but slower-growing economies might be making slightly quicker strides in gender equality.\n \n\n\nTime Series Line plot\nThe time series line plot gives us the big picture of GDP and GII changes over time across a few select countries, including the United States, Japan, the United Kingdom, France, and Mexico. Mexico, for example, saw great GDP swings in 1995, yet its GII stayed relatively stable. Meanwhile, Japan and France saw some dramatic GII shifts. These fluctuations leave us wondering: what historical events sparked these changes? We may need to take a deeper look into each country’s unique timeline to find the answers.\n \n\n\nBoxplot\nNext, we have the boxplot to help us visualize the distribution. By sequencing countries in ascending order of median GDP change, we see an interesting trend: countries with higher median GDP growth also seem to show more variability in their GDP changes. The GII change, on the other hand, keeps a relatively stable median, hinting at a weaker association with GDP growth.\n \n\n\nHeatmap\nFinally, our heatmap wraps it all up with a quick, intuitive look at correlations between GDP and GII across countries. In countries like Estonia (EE) and Ireland (IE), Australia (AT), South Africa (ZA), Thailand (TH), Slovenia (SI), we see positive correlations in warm colors, indicating that as GDP growth rates increase, GII growth rates tend to rise as well. For countries like Cyprus (CY), Romania (RO), and the United States (US), we see cooler colors, representing negative correlations: as GDP growth rate rises, GII growth rates decrease, suggesting an improvement in gender equality alongside economic growth. This color-coded comparison leaves us curious—could the distinction between developed and developing countries play a role in these relationships? It’s a question that might inspire future research directions!" - }, - { - "objectID": "Blog/blog/Blog.html#statistical-analysis", - "href": "Blog/blog/Blog.html#statistical-analysis", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "Statistical Analysis", - "text": "Statistical Analysis\n\nDescriptive Statistics\nLet’s start with a quick overview of our descriptive statistics: our dataset includes 915 data points. On average, GII change is -3.15%, indicating an overall reduction in gender inequality over time. Meanwhile, GDP change averages 2.35%, hinting at modest economic growth. A full breakdown of these values can be found in the table below for more detail.\n\n\n\nRegression Analysis\nTo get a sense of any relationship between GDP change and GII change, we ran a simple linear regression. Here’s what we found:\n\nP-value: With a p-value of 0.057, we fail to reject the null hypothesis. There is no statistically significant relationship. In other words, we don’t find strong evidence of a link between GDP and GII changes.\nR-squared: GDP change explains only 0.4% of the variation in GII change—a pretty minimal effect.\n95% Confidence Interval: We’re 95% confident the interval from -0.003 to 0.169 captures the true slope. The effect of GDP change on GII is uncertain as 0 is within this range.\n\n\n\nTime Series Analysis\nTime series analysis allows us to dig deeper into how GII and GDP changes play out over time—and potentially influence one another. Here’s the rundown:\n\nStationarity check: Both GII and GDP change series are stationary, meaning their means and variances are stable over time, setting us up for reliable forecasting.\nVAR Model (Vector Autoregression): This multivariate model lets us explore how past values in both series relate to current values. Notably, past GII changes significantly impact current GII changes.\nModel Performance: The VAR model has good fit, as shown by the forecast closely tracking actual values. Low MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) confirm that our model predicts GII and GDP change with minimal error. Check out the plots and table below!\nGranger Causality Test: Curious whether GDP changes can predict changes in GII? The Granger causality test says no. With p-values above 0.05, we fail to find evidence that GDP changes drive GII changes. This suggests that while both variables evolve over time, they don’t seem to directly influence each other." - }, - { - "objectID": "Blog/blog/Blog.html#conclusion", - "href": "Blog/blog/Blog.html#conclusion", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "Conclusion", - "text": "Conclusion\nIn wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research.\nIn terms of the Imfp library, we gained valuable experience working with real-world economic indicators like GDP and the GII. The library’s function in fetching databases, parameters, and datasets made it straightforward to access IMF’s data, allowing us to focus on analysis instead. The integration of other Python tools—such as seaborn, matplotlib, and numpy—not only streamlined our computational processes but also ensured consistency across the project." - }, - { - "objectID": "Blog/blog/Blog.html#future-directions", - "href": "Blog/blog/Blog.html#future-directions", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "Future Directions", - "text": "Future Directions\nFirst, we must consider what other factors might influence the relationship between GDP and GII change. The GII is a composite index, shaped by a myriad of social factors, including cultural norms, legal frameworks, and environmental shifts. Future studies could benefit from incorporating additional predictors into the analysis and exploring the interaction between economic growth and gender equality within specific country contexts.\nSecond, there’s potential to enhance the predictive power of our Vector Autoregression (VAR) time series model. While we established that GDP change does not cause GII change, our model performed well in forecasting trends for both variables independently. In practice, policymakers may want to forecast GII trends independently of GDP if they are implementing gender-focused policies. Future research could investigate time series modeling to further unravel the dynamics of GII and GDP changes.\nSo, as we wrap up this chapter, let’s keep our curiosity alive and our questions flowing. After all, every end is just a new beginning in the quest for knowledge!" - }, - { - "objectID": "About/About.html", - "href": "About/About.html", - "title": "Jenny Xu", - "section": "", - "text": "Hi there! My name is Jenny, and I’m a third-year student at University of California, Davis, double majoring in Statistics and Psychology. I’ve always been interested in becoming a data analyst working in tech, internet, or research industries. Interning at Promptly Technologies helped me learn a ton. A quick fun fact for me is that my MBTI is ISFJ (Defender)!" - }, - { - "objectID": "Blog/blog/listing.html", - "href": "Blog/blog/listing.html", - "title": "Blog", - "section": "", - "text": "No matching items" - }, - { - "objectID": "Blog/post/listing.html", - "href": "Blog/post/listing.html", - "title": "Blog", - "section": "", - "text": "No matching items" - }, - { - "objectID": "Blog/post/Blog.html", - "href": "Blog/post/Blog.html", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "", - "text": "Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders?\nOur main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables.\nReady for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!" - }, - { - "objectID": "Blog/post/Blog.html#introduction", - "href": "Blog/post/Blog.html#introduction", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "", - "text": "Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders?\nOur main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables.\nReady for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!" - }, - { - "objectID": "Blog/post/Blog.html#data-preparation", - "href": "Blog/post/Blog.html#data-preparation", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "Data Preparation", - "text": "Data Preparation\n​​To explore how economic growth connects with gender equality, we turned to the Imfp library to access a wealth of global economic data, ready to analyze through some straightforward API calls. Let’s break down our two main variables of interest. Here’s the lowdown on the 4 key data series we pulled together:\n\n\nDependent Variable: Percent Change in Gender Inequality Index (GII)\nOur gender equality measure, the Gender Inequality Index (GII), is a composite score that reflects inequality in three key areas: reproductive health, empowerment, and labor market, ranging from 0 (equality) to 1 (inequality). We calculated the percent change in GII over time to make it easier to compare GII change across countries with different baseline levels of inequality.\n\n\nIndependent Variable: Percent Change in Real GDP per capita\nOur economic growth measure is real GDP per capita, an indicator of a country’s economic output per person, adjusted for inflation. The higher the real GDP per capita, the wealthier and more prosperous a country is. Here is a peek at the formula:" - }, - { - "objectID": "Blog/post/Blog.html#data-visualization", - "href": "Blog/post/Blog.html#data-visualization", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "Data Visualization", - "text": "Data Visualization\n\nScatterplot\nNothing like a good scatterplot to help us understand the relationship between two numbers, right? For our project, the scatterplot lets us visually explore how Percent Change in Real GDP per capita relates to Percent Change in GII. Each dot on the plot represents a country-year combo. The gentle upward slope suggests that in years of low GDP growth, gender inequality tends to decrease a bit faster, while in years of high GDP growth, the inequality drops at a slightly slower rate. It’s a subtle clue, but slower-growing economies might be making slightly quicker strides in gender equality.\n \n\n\nTime Series Line plot\nThe time series line plot gives us the big picture of GDP and GII changes over time across a few select countries, including the United States, Japan, the United Kingdom, France, and Mexico. Mexico, for example, saw great GDP swings in 1995, yet its GII stayed relatively stable. Meanwhile, Japan and France saw some dramatic GII shifts. These fluctuations leave us wondering: what historical events sparked these changes? We may need to take a deeper look into each country’s unique timeline to find the answers.\n \n\n\nBoxplot\nNext, we have the boxplot to help us visualize the distribution. By sequencing countries in ascending order of median GDP change, we see an interesting trend: countries with higher median GDP growth also seem to show more variability in their GDP changes. The GII change, on the other hand, keeps a relatively stable median, hinting at a weaker association with GDP growth.\n \n\n\nHeatmap\nFinally, our heatmap wraps it all up with a quick, intuitive look at correlations between GDP and GII across countries. In countries like Estonia (EE) and Ireland (IE), Australia (AT), South Africa (ZA), Thailand (TH), Slovenia (SI), we see positive correlations in warm colors, indicating that as GDP growth rates increase, GII growth rates tend to rise as well. For countries like Cyprus (CY), Romania (RO), and the United States (US), we see cooler colors, representing negative correlations: as GDP growth rate rises, GII growth rates decrease, suggesting an improvement in gender equality alongside economic growth. This color-coded comparison leaves us curious—could the distinction between developed and developing countries play a role in these relationships? It’s a question that might inspire future research directions!" - }, - { - "objectID": "Blog/post/Blog.html#statistical-analysis", - "href": "Blog/post/Blog.html#statistical-analysis", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "Statistical Analysis", - "text": "Statistical Analysis\n\nDescriptive Statistics\nLet’s start with a quick overview of our descriptive statistics: our dataset includes 915 data points. On average, GII change is -3.15%, indicating an overall reduction in gender inequality over time. Meanwhile, GDP change averages 2.35%, hinting at modest economic growth. A full breakdown of these values can be found in the table below for more detail.\n\n\n\nRegression Analysis\nTo get a sense of any relationship between GDP change and GII change, we ran a simple linear regression. Here’s what we found:\n\nP-value: With a p-value of 0.057, we fail to reject the null hypothesis. There is no statistically significant relationship. In other words, we don’t find strong evidence of a link between GDP and GII changes.\nR-squared: GDP change explains only 0.4% of the variation in GII change—a pretty minimal effect.\n95% Confidence Interval: We’re 95% confident the interval from -0.003 to 0.169 captures the true slope. The effect of GDP change on GII is uncertain as 0 is within this range.\n\n\n\nTime Series Analysis\nTime series analysis allows us to dig deeper into how GII and GDP changes play out over time—and potentially influence one another. Here’s the rundown:\n\nStationarity check: Both GII and GDP change series are stationary, meaning their means and variances are stable over time, setting us up for reliable forecasting.\nVAR Model (Vector Autoregression): This multivariate model lets us explore how past values in both series relate to current values. Notably, past GII changes significantly impact current GII changes.\nModel Performance: The VAR model has good fit, as shown by the forecast closely tracking actual values. Low MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) confirm that our model predicts GII and GDP change with minimal error. Check out the plots and table below!\nGranger Causality Test: Curious whether GDP changes can predict changes in GII? The Granger causality test says no. With p-values above 0.05, we fail to find evidence that GDP changes drive GII changes. This suggests that while both variables evolve over time, they don’t seem to directly influence each other." - }, - { - "objectID": "Blog/post/Blog.html#conclusion", - "href": "Blog/post/Blog.html#conclusion", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "Conclusion", - "text": "Conclusion\nIn wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research.\nIn terms of the Imfp library, we gained valuable experience working with real-world economic indicators like GDP and the GII. The library’s function in fetching databases, parameters, and datasets made it straightforward to access IMF’s data, allowing us to focus on analysis instead. The integration of other Python tools—such as seaborn, matplotlib, and numpy—not only streamlined our computational processes but also ensured consistency across the project." - }, - { - "objectID": "Blog/post/Blog.html#future-directions", - "href": "Blog/post/Blog.html#future-directions", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "Future Directions", - "text": "Future Directions\nFirst, we must consider what other factors might influence the relationship between GDP and GII change. The GII is a composite index, shaped by a myriad of social factors, including cultural norms, legal frameworks, and environmental shifts. Future studies could benefit from incorporating additional predictors into the analysis and exploring the interaction between economic growth and gender equality within specific country contexts.\nSecond, there’s potential to enhance the predictive power of our Vector Autoregression (VAR) time series model. While we established that GDP change does not cause GII change, our model performed well in forecasting trends for both variables independently. In practice, policymakers may want to forecast GII trends independently of GDP if they are implementing gender-focused policies. Future research could investigate time series modeling to further unravel the dynamics of GII and GDP changes.\nSo, as we wrap up this chapter, let’s keep our curiosity alive and our questions flowing. After all, every end is just a new beginning in the quest for knowledge!" - }, - { - "objectID": "posts/posts/Blog.html", - "href": "posts/posts/Blog.html", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "", - "text": "Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders?\nOur main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables.\nReady for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!" - }, - { - "objectID": "posts/posts/Blog.html#introduction", - "href": "posts/posts/Blog.html#introduction", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "", - "text": "Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders?\nOur main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables.\nReady for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!" - }, - { - "objectID": "posts/posts/Blog.html#data-preparation", - "href": "posts/posts/Blog.html#data-preparation", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "Data Preparation", - "text": "Data Preparation\n​​To explore how economic growth connects with gender equality, we turned to the Imfp library to access a wealth of global economic data, ready to analyze through some straightforward API calls. Let’s break down our two main variables of interest. Here’s the lowdown on the 4 key data series we pulled together:\n\n\nDependent Variable: Percent Change in Gender Inequality Index (GII)\nOur gender equality measure, the Gender Inequality Index (GII), is a composite score that reflects inequality in three key areas: reproductive health, empowerment, and labor market, ranging from 0 (equality) to 1 (inequality). We calculated the percent change in GII over time to make it easier to compare GII change across countries with different baseline levels of inequality.\n\n\nIndependent Variable: Percent Change in Real GDP per capita\nOur economic growth measure is real GDP per capita, an indicator of a country’s economic output per person, adjusted for inflation. The higher the real GDP per capita, the wealthier and more prosperous a country is. Here is a peek at the formula:" - }, - { - "objectID": "posts/posts/Blog.html#data-visualization", - "href": "posts/posts/Blog.html#data-visualization", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "Data Visualization", - "text": "Data Visualization\n\nScatterplot\nNothing like a good scatterplot to help us understand the relationship between two numbers, right? For our project, the scatterplot lets us visually explore how Percent Change in Real GDP per capita relates to Percent Change in GII. Each dot on the plot represents a country-year combo. The gentle upward slope suggests that in years of low GDP growth, gender inequality tends to decrease a bit faster, while in years of high GDP growth, the inequality drops at a slightly slower rate. It’s a subtle clue, but slower-growing economies might be making slightly quicker strides in gender equality.\n \n\n\nTime Series Line plot\nThe time series line plot gives us the big picture of GDP and GII changes over time across a few select countries, including the United States, Japan, the United Kingdom, France, and Mexico. Mexico, for example, saw great GDP swings in 1995, yet its GII stayed relatively stable. Meanwhile, Japan and France saw some dramatic GII shifts. These fluctuations leave us wondering: what historical events sparked these changes? We may need to take a deeper look into each country’s unique timeline to find the answers.\n \n\n\nBoxplot\nNext, we have the boxplot to help us visualize the distribution. By sequencing countries in ascending order of median GDP change, we see an interesting trend: countries with higher median GDP growth also seem to show more variability in their GDP changes. The GII change, on the other hand, keeps a relatively stable median, hinting at a weaker association with GDP growth.\n \n\n\nHeatmap\nFinally, our heatmap wraps it all up with a quick, intuitive look at correlations between GDP and GII across countries. In countries like Estonia (EE) and Ireland (IE), Australia (AT), South Africa (ZA), Thailand (TH), Slovenia (SI), we see positive correlations in warm colors, indicating that as GDP growth rates increase, GII growth rates tend to rise as well. For countries like Cyprus (CY), Romania (RO), and the United States (US), we see cooler colors, representing negative correlations: as GDP growth rate rises, GII growth rates decrease, suggesting an improvement in gender equality alongside economic growth. This color-coded comparison leaves us curious—could the distinction between developed and developing countries play a role in these relationships? It’s a question that might inspire future research directions!" - }, - { - "objectID": "posts/posts/Blog.html#statistical-analysis", - "href": "posts/posts/Blog.html#statistical-analysis", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "Statistical Analysis", - "text": "Statistical Analysis\n\nDescriptive Statistics\nLet’s start with a quick overview of our descriptive statistics: our dataset includes 915 data points. On average, GII change is -3.15%, indicating an overall reduction in gender inequality over time. Meanwhile, GDP change averages 2.35%, hinting at modest economic growth. A full breakdown of these values can be found in the table below for more detail.\n\n\n\nRegression Analysis\nTo get a sense of any relationship between GDP change and GII change, we ran a simple linear regression. Here’s what we found:\n\nP-value: With a p-value of 0.057, we fail to reject the null hypothesis. There is no statistically significant relationship. In other words, we don’t find strong evidence of a link between GDP and GII changes.\nR-squared: GDP change explains only 0.4% of the variation in GII change—a pretty minimal effect.\n95% Confidence Interval: We’re 95% confident the interval from -0.003 to 0.169 captures the true slope. The effect of GDP change on GII is uncertain as 0 is within this range.\n\n\n\nTime Series Analysis\nTime series analysis allows us to dig deeper into how GII and GDP changes play out over time—and potentially influence one another. Here’s the rundown:\n\nStationarity check: Both GII and GDP change series are stationary, meaning their means and variances are stable over time, setting us up for reliable forecasting.\nVAR Model (Vector Autoregression): This multivariate model lets us explore how past values in both series relate to current values. Notably, past GII changes significantly impact current GII changes.\nModel Performance: The VAR model has good fit, as shown by the forecast closely tracking actual values. Low MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) confirm that our model predicts GII and GDP change with minimal error. Check out the plots and table below!\nGranger Causality Test: Curious whether GDP changes can predict changes in GII? The Granger causality test says no. With p-values above 0.05, we fail to find evidence that GDP changes drive GII changes. This suggests that while both variables evolve over time, they don’t seem to directly influence each other." - }, - { - "objectID": "posts/posts/Blog.html#conclusion", - "href": "posts/posts/Blog.html#conclusion", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "Conclusion", - "text": "Conclusion\nIn wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research.\nIn terms of the Imfp library, we gained valuable experience working with real-world economic indicators like GDP and the GII. The library’s function in fetching databases, parameters, and datasets made it straightforward to access IMF’s data, allowing us to focus on analysis instead. The integration of other Python tools—such as seaborn, matplotlib, and numpy—not only streamlined our computational processes but also ensured consistency across the project." - }, - { - "objectID": "posts/posts/Blog.html#future-directions", - "href": "posts/posts/Blog.html#future-directions", - "title": "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data", - "section": "Future Directions", - "text": "Future Directions\nFirst, we must consider what other factors might influence the relationship between GDP and GII change. The GII is a composite index, shaped by a myriad of social factors, including cultural norms, legal frameworks, and environmental shifts. Future studies could benefit from incorporating additional predictors into the analysis and exploring the interaction between economic growth and gender equality within specific country contexts.\nSecond, there’s potential to enhance the predictive power of our Vector Autoregression (VAR) time series model. While we established that GDP change does not cause GII change, our model performed well in forecasting trends for both variables independently. In practice, policymakers may want to forecast GII trends independently of GDP if they are implementing gender-focused policies. Future research could investigate time series modeling to further unravel the dynamics of GII and GDP changes.\nSo, as we wrap up this chapter, let’s keep our curiosity alive and our questions flowing. After all, every end is just a new beginning in the quest for knowledge!" - }, - { - "objectID": "posts/posts/listing.html", - "href": "posts/posts/listing.html", - "title": "Blog", - "section": "", - "text": "No matching items" - } -] \ No newline at end of file diff --git a/demo/demo/_site/site_libs/bootstrap/bootstrap-8bda806db8b623a4dafb352339cca83f.min.css b/demo/demo/_site/site_libs/bootstrap/bootstrap-8bda806db8b623a4dafb352339cca83f.min.css deleted file mode 100644 index 65ea065..0000000 --- a/demo/demo/_site/site_libs/bootstrap/bootstrap-8bda806db8b623a4dafb352339cca83f.min.css +++ /dev/null @@ -1,12 +0,0 @@ -/*! - * Bootstrap v5.3.1 (https://getbootstrap.com/) - * Copyright 2011-2023 The Bootstrap Authors - * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) - */@import"https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@300;400;700&display=swap";:root,[data-bs-theme=light]{--bs-blue: #2780e3;--bs-indigo: #6610f2;--bs-purple: 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!important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: #545555;--bs-navbar-hover-color: rgba(31, 78, 182, 0.8);--bs-navbar-disabled-color: rgba(84, 85, 85, 0.75);--bs-navbar-active-color: #1f4eb6;--bs-navbar-brand-color: #545555;--bs-navbar-brand-hover-color: #1f4eb6;--bs-navbar-toggler-border-color: rgba(84, 85, 85, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 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0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0}.card>.list-group:last-child{border-bottom-width:0}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #343a40;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #343a40;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23343a40'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%2310335b'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #93c0f1;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #10335b;--bs-accordion-active-bg: #d4e6f9}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #2761e3;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #1f4eb6;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #1f4eb6;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(52, 58, 64, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #343a40;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(52, 58, 64, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #343a40;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(52, 58, 64, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(52, 58, 64, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color)}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static 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0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen 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1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #fff;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:"Source Sans Pro",-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI 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var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #757b7a;color:#fff}.bg-gradient-cyan-teal{--bslib-color-fg: #fff;--bslib-color-bg: #6983ad;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #6983ad;color:#fff}:root{--bslib-value-box-shadow: none;--bslib-value-box-border-width-auto-yes: var(--bslib-value-box-border-width-baseline);--bslib-value-box-border-width-auto-no: 0;--bslib-value-box-border-width-baseline: 1px}.bslib-value-box{border-width:var(--bslib-value-box-border-width-auto-no, var(--bslib-value-box-border-width-baseline));container-name:bslib-value-box;container-type:inline-size}.bslib-value-box.card{box-shadow:var(--bslib-value-box-shadow)}.bslib-value-box.border-auto{border-width:var(--bslib-value-box-border-width-auto-yes, var(--bslib-value-box-border-width-baseline))}.bslib-value-box.default{--bslib-value-box-bg-default: var(--bs-card-bg, #fff);--bslib-value-box-border-color-default: var(--bs-card-border-color, rgba(0, 0, 0, 0.175));color:var(--bslib-value-box-color);background-color:var(--bslib-value-box-bg, var(--bslib-value-box-bg-default));border-color:var(--bslib-value-box-border-color, var(--bslib-value-box-border-color-default))}.bslib-value-box .value-box-grid{display:grid;grid-template-areas:"left right";align-items:center;overflow:hidden}.bslib-value-box .value-box-showcase{height:100%;max-height:var(---bslib-value-box-showcase-max-h, 100%)}.bslib-value-box .value-box-showcase,.bslib-value-box .value-box-showcase>.html-fill-item{width:100%}.bslib-value-box[data-full-screen=true] .value-box-showcase{max-height:var(---bslib-value-box-showcase-max-h-fs, 100%)}@media screen and (min-width: 575.98px){@container bslib-value-box (max-width: 300px){.bslib-value-box:not(.showcase-bottom) .value-box-grid{grid-template-columns:1fr !important;grid-template-rows:auto auto;grid-template-areas:"top" 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#3d224b;--quarto-scss-export-warning-text-emphasis: #662f0a;--quarto-scss-export-danger-text-emphasis: #660017;--quarto-scss-export-light-text-emphasis: #495057;--quarto-scss-export-dark-text-emphasis: #495057;--quarto-scss-export-primary-bg-subtle: #d4e6f9;--quarto-scss-export-secondary-bg-subtle: #d6d8d9;--quarto-scss-export-success-bg-subtle: #d9f0d1;--quarto-scss-export-info-bg-subtle: #ebddf1;--quarto-scss-export-warning-bg-subtle: #ffe3d1;--quarto-scss-export-danger-bg-subtle: #ffccd7;--quarto-scss-export-light-bg-subtle: #fcfcfd;--quarto-scss-export-dark-bg-subtle: #ced4da;--quarto-scss-export-primary-border-subtle: #a9ccf4;--quarto-scss-export-secondary-border-subtle: #aeb0b3;--quarto-scss-export-success-border-subtle: #b2e2a3;--quarto-scss-export-info-border-subtle: #d6bbe4;--quarto-scss-export-warning-border-subtle: #ffc8a3;--quarto-scss-export-danger-border-subtle: #ff99b0;--quarto-scss-export-light-border-subtle: #e9ecef;--quarto-scss-export-dark-border-subtle: #adb5bd;--quarto-scss-export-body-text-align: ;--quarto-scss-export-body-bg: #fff;--quarto-scss-export-body-secondary-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-body-secondary-bg: #e9ecef;--quarto-scss-export-body-tertiary-color: rgba(52, 58, 64, 0.5);--quarto-scss-export-body-tertiary-bg: #f8f9fa;--quarto-scss-export-body-emphasis-color: #000;--quarto-scss-export-link-hover-color: #1f4eb6;--quarto-scss-export-link-hover-decoration: ;--quarto-scss-export-border-color-translucent: rgba(0, 0, 0, 0.175);--quarto-scss-export-component-active-bg: #2780e3;--quarto-scss-export-component-active-color: #fff;--quarto-scss-export-focus-ring-color: rgba(39, 128, 227, 0.25);--quarto-scss-export-headings-font-family: ;--quarto-scss-export-headings-font-style: ;--quarto-scss-export-display-font-family: ;--quarto-scss-export-display-font-style: ;--quarto-scss-export-blockquote-footer-color: #6c757d;--quarto-scss-export-blockquote-border-color: #e9ecef;--quarto-scss-export-hr-bg-color: ;--quarto-scss-export-hr-height: ;--quarto-scss-export-hr-border-color: ;--quarto-scss-export-legend-font-weight: ;--quarto-scss-export-mark-bg: #ffe3d1;--quarto-scss-export-table-color: #343a40;--quarto-scss-export-table-bg: #fff;--quarto-scss-export-table-accent-bg: transparent;--quarto-scss-export-table-th-font-weight: ;--quarto-scss-export-table-striped-color: #343a40;--quarto-scss-export-table-striped-bg: rgba(0, 0, 0, 0.05);--quarto-scss-export-table-active-color: #343a40;--quarto-scss-export-table-active-bg: rgba(0, 0, 0, 0.1);--quarto-scss-export-table-hover-color: #343a40;--quarto-scss-export-table-hover-bg: rgba(0, 0, 0, 0.075);--quarto-scss-export-table-caption-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-input-btn-font-family: ;--quarto-scss-export-input-btn-focus-color: rgba(39, 128, 227, 0.25);--quarto-scss-export-btn-color: #343a40;--quarto-scss-export-btn-font-family: ;--quarto-scss-export-btn-white-space: ;--quarto-scss-export-btn-link-color: #2761e3;--quarto-scss-export-btn-link-hover-color: #1f4eb6;--quarto-scss-export-btn-link-disabled-color: #6c757d;--quarto-scss-export-form-text-font-style: ;--quarto-scss-export-form-text-font-weight: ;--quarto-scss-export-form-text-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-form-label-font-size: ;--quarto-scss-export-form-label-font-style: ;--quarto-scss-export-form-label-font-weight: ;--quarto-scss-export-form-label-color: ;--quarto-scss-export-input-font-family: ;--quarto-scss-export-input-disabled-color: ;--quarto-scss-export-input-disabled-bg: #e9ecef;--quarto-scss-export-input-disabled-border-color: ;--quarto-scss-export-input-color: #343a40;--quarto-scss-export-input-focus-bg: #fff;--quarto-scss-export-input-focus-border-color: #93c0f1;--quarto-scss-export-input-focus-color: #343a40;--quarto-scss-export-input-placeholder-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-input-plaintext-color: #343a40;--quarto-scss-export-form-check-label-color: ;--quarto-scss-export-form-check-transition: ;--quarto-scss-export-form-check-input-bg: #fff;--quarto-scss-export-form-check-input-focus-border: #93c0f1;--quarto-scss-export-form-check-input-checked-color: #fff;--quarto-scss-export-form-check-input-checked-bg-color: #2780e3;--quarto-scss-export-form-check-input-checked-border-color: #2780e3;--quarto-scss-export-form-check-input-indeterminate-color: #fff;--quarto-scss-export-form-check-input-indeterminate-bg-color: #2780e3;--quarto-scss-export-form-check-input-indeterminate-border-color: #2780e3;--quarto-scss-export-form-switch-color: rgba(0, 0, 0, 0.25);--quarto-scss-export-form-switch-focus-color: #93c0f1;--quarto-scss-export-form-switch-checked-color: #fff;--quarto-scss-export-input-group-addon-color: #343a40;--quarto-scss-export-input-group-addon-bg: #f8f9fa;--quarto-scss-export-input-group-addon-border-color: #dee2e6;--quarto-scss-export-form-select-font-family: ;--quarto-scss-export-form-select-color: #343a40;--quarto-scss-export-form-select-bg: #fff;--quarto-scss-export-form-select-disabled-color: ;--quarto-scss-export-form-select-disabled-bg: #e9ecef;--quarto-scss-export-form-select-disabled-border-color: ;--quarto-scss-export-form-select-indicator-color: #343a40;--quarto-scss-export-form-select-border-color: #dee2e6;--quarto-scss-export-form-select-focus-border-color: #93c0f1;--quarto-scss-export-form-range-track-bg: #f8f9fa;--quarto-scss-export-form-range-thumb-bg: #2780e3;--quarto-scss-export-form-range-thumb-active-bg: #bed9f7;--quarto-scss-export-form-range-thumb-disabled-bg: rgba(52, 58, 64, 0.75);--quarto-scss-export-form-file-button-color: #343a40;--quarto-scss-export-form-file-button-bg: #f8f9fa;--quarto-scss-export-form-file-button-hover-bg: #e9ecef;--quarto-scss-export-form-floating-label-disabled-color: #6c757d;--quarto-scss-export-form-feedback-font-style: ;--quarto-scss-export-form-feedback-valid-color: #3fb618;--quarto-scss-export-form-feedback-invalid-color: #ff0039;--quarto-scss-export-form-feedback-icon-valid-color: #3fb618;--quarto-scss-export-form-feedback-icon-invalid-color: #ff0039;--quarto-scss-export-form-valid-color: #3fb618;--quarto-scss-export-form-valid-border-color: #3fb618;--quarto-scss-export-form-invalid-color: #ff0039;--quarto-scss-export-form-invalid-border-color: #ff0039;--quarto-scss-export-nav-link-font-size: ;--quarto-scss-export-nav-link-font-weight: ;--quarto-scss-export-nav-link-color: #2761e3;--quarto-scss-export-nav-link-hover-color: #1f4eb6;--quarto-scss-export-nav-link-disabled-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-nav-tabs-border-color: #dee2e6;--quarto-scss-export-nav-tabs-link-hover-border-color: #e9ecef #e9ecef #dee2e6;--quarto-scss-export-nav-tabs-link-active-color: #000;--quarto-scss-export-nav-tabs-link-active-bg: #fff;--quarto-scss-export-nav-pills-link-active-bg: #2780e3;--quarto-scss-export-nav-pills-link-active-color: #fff;--quarto-scss-export-nav-underline-link-active-color: #000;--quarto-scss-export-navbar-padding-x: ;--quarto-scss-export-navbar-light-contrast: #000;--quarto-scss-export-navbar-dark-contrast: #000;--quarto-scss-export-navbar-light-icon-color: rgba(0, 0, 0, 0.75);--quarto-scss-export-navbar-dark-icon-color: rgba(0, 0, 0, 0.75);--quarto-scss-export-dropdown-color: #343a40;--quarto-scss-export-dropdown-bg: #fff;--quarto-scss-export-dropdown-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-link-color: #343a40;--quarto-scss-export-dropdown-link-hover-color: #343a40;--quarto-scss-export-dropdown-link-hover-bg: #f8f9fa;--quarto-scss-export-dropdown-link-active-bg: #2780e3;--quarto-scss-export-dropdown-link-active-color: #fff;--quarto-scss-export-dropdown-link-disabled-color: rgba(52, 58, 64, 0.5);--quarto-scss-export-dropdown-header-color: #6c757d;--quarto-scss-export-dropdown-dark-color: #dee2e6;--quarto-scss-export-dropdown-dark-bg: #343a40;--quarto-scss-export-dropdown-dark-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-box-shadow: ;--quarto-scss-export-dropdown-dark-link-color: #dee2e6;--quarto-scss-export-dropdown-dark-link-hover-color: #fff;--quarto-scss-export-dropdown-dark-link-hover-bg: rgba(255, 255, 255, 0.15);--quarto-scss-export-dropdown-dark-link-active-color: #fff;--quarto-scss-export-dropdown-dark-link-active-bg: #2780e3;--quarto-scss-export-dropdown-dark-link-disabled-color: #adb5bd;--quarto-scss-export-dropdown-dark-header-color: #adb5bd;--quarto-scss-export-pagination-color: #2761e3;--quarto-scss-export-pagination-bg: #fff;--quarto-scss-export-pagination-border-color: #dee2e6;--quarto-scss-export-pagination-focus-color: #1f4eb6;--quarto-scss-export-pagination-focus-bg: #e9ecef;--quarto-scss-export-pagination-hover-color: #1f4eb6;--quarto-scss-export-pagination-hover-bg: #f8f9fa;--quarto-scss-export-pagination-hover-border-color: #dee2e6;--quarto-scss-export-pagination-active-color: #fff;--quarto-scss-export-pagination-active-bg: #2780e3;--quarto-scss-export-pagination-active-border-color: #2780e3;--quarto-scss-export-pagination-disabled-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-pagination-disabled-bg: #e9ecef;--quarto-scss-export-pagination-disabled-border-color: #dee2e6;--quarto-scss-export-card-title-color: ;--quarto-scss-export-card-subtitle-color: ;--quarto-scss-export-card-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-card-box-shadow: ;--quarto-scss-export-card-cap-color: ;--quarto-scss-export-card-height: ;--quarto-scss-export-card-color: ;--quarto-scss-export-card-bg: #fff;--quarto-scss-export-accordion-color: #343a40;--quarto-scss-export-accordion-bg: #fff;--quarto-scss-export-accordion-border-color: #dee2e6;--quarto-scss-export-accordion-button-color: #343a40;--quarto-scss-export-accordion-button-bg: #fff;--quarto-scss-export-accordion-button-active-bg: #d4e6f9;--quarto-scss-export-accordion-button-active-color: #10335b;--quarto-scss-export-accordion-button-focus-border-color: #93c0f1;--quarto-scss-export-accordion-icon-color: #343a40;--quarto-scss-export-accordion-icon-active-color: #10335b;--quarto-scss-export-tooltip-color: #fff;--quarto-scss-export-tooltip-bg: #000;--quarto-scss-export-tooltip-margin: ;--quarto-scss-export-tooltip-arrow-color: ;--quarto-scss-export-form-feedback-tooltip-line-height: ;--quarto-scss-export-popover-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-popover-header-bg: #e9ecef;--quarto-scss-export-popover-body-color: #343a40;--quarto-scss-export-popover-arrow-color: #fff;--quarto-scss-export-popover-arrow-outer-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-color: ;--quarto-scss-export-toast-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-header-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-toast-header-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-header-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-badge-color: #fff;--quarto-scss-export-modal-content-color: ;--quarto-scss-export-modal-content-bg: #fff;--quarto-scss-export-modal-content-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-modal-backdrop-bg: #000;--quarto-scss-export-modal-header-border-color: #dee2e6;--quarto-scss-export-modal-footer-bg: ;--quarto-scss-export-modal-footer-border-color: #dee2e6;--quarto-scss-export-progress-bg: #e9ecef;--quarto-scss-export-progress-bar-color: #fff;--quarto-scss-export-progress-bar-bg: #2780e3;--quarto-scss-export-list-group-color: #343a40;--quarto-scss-export-list-group-bg: #fff;--quarto-scss-export-list-group-border-color: #dee2e6;--quarto-scss-export-list-group-hover-bg: #f8f9fa;--quarto-scss-export-list-group-active-bg: #2780e3;--quarto-scss-export-list-group-active-color: #fff;--quarto-scss-export-list-group-active-border-color: #2780e3;--quarto-scss-export-list-group-disabled-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-list-group-disabled-bg: #fff;--quarto-scss-export-list-group-action-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-list-group-action-hover-color: #000;--quarto-scss-export-list-group-action-active-color: #343a40;--quarto-scss-export-list-group-action-active-bg: #e9ecef;--quarto-scss-export-thumbnail-bg: #fff;--quarto-scss-export-thumbnail-border-color: #dee2e6;--quarto-scss-export-figure-caption-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-breadcrumb-font-size: ;--quarto-scss-export-breadcrumb-bg: ;--quarto-scss-export-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-breadcrumb-active-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-breadcrumb-border-radius: ;--quarto-scss-export-carousel-control-color: #fff;--quarto-scss-export-carousel-indicator-active-bg: #fff;--quarto-scss-export-carousel-caption-color: #fff;--quarto-scss-export-carousel-dark-indicator-active-bg: #000;--quarto-scss-export-carousel-dark-caption-color: #000;--quarto-scss-export-btn-close-color: #000;--quarto-scss-export-offcanvas-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-offcanvas-bg-color: #fff;--quarto-scss-export-offcanvas-color: #343a40;--quarto-scss-export-offcanvas-backdrop-bg: #000;--quarto-scss-export-code-color-dark: white;--quarto-scss-export-kbd-color: #fff;--quarto-scss-export-kbd-bg: #343a40;--quarto-scss-export-nested-kbd-font-weight: ;--quarto-scss-export-pre-bg: #f8f9fa;--quarto-scss-export-pre-color: #000;--quarto-scss-export-bslib-page-sidebar-title-bg: #f8f9fa;--quarto-scss-export-bslib-page-sidebar-title-color: #000;--quarto-scss-export-bslib-sidebar-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.05);--quarto-scss-export-bslib-sidebar-toggle-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.1);--quarto-scss-export-sidebar-color: #595959;--quarto-scss-export-sidebar-hover-color: rgba(33, 81, 191, 0.8);--quarto-scss-export-sidebar-disabled-color: rgba(89, 89, 89, 0.75);--quarto-scss-export-valuebox-bg-primary: #5397e9;--quarto-scss-export-valuebox-bg-secondary: #343a40;--quarto-scss-export-valuebox-bg-success: #3aa716;--quarto-scss-export-valuebox-bg-info: rgba(153, 84, 187, 0.7019607843);--quarto-scss-export-valuebox-bg-warning: #fa6400;--quarto-scss-export-valuebox-bg-danger: rgba(255, 0, 57, 0.7019607843);--quarto-scss-export-valuebox-bg-light: #f8f9fa;--quarto-scss-export-valuebox-bg-dark: #343a40;--quarto-scss-export-mermaid-bg-color: #fff;--quarto-scss-export-mermaid-edge-color: #343a40;--quarto-scss-export-mermaid-node-fg-color: #343a40;--quarto-scss-export-mermaid-fg-color: #343a40;--quarto-scss-export-mermaid-fg-color--lighter: #4b545c;--quarto-scss-export-mermaid-fg-color--lightest: #626d78;--quarto-scss-export-mermaid-label-bg-color: #fff;--quarto-scss-export-mermaid-label-fg-color: #2780e3;--quarto-scss-export-mermaid-node-bg-color: rgba(39, 128, 227, 0.1);--quarto-scss-export-code-block-border-left-color: #dee2e6;--quarto-scss-export-callout-color-note: #2780e3;--quarto-scss-export-callout-color-tip: #3fb618;--quarto-scss-export-callout-color-important: #ff0039;--quarto-scss-export-callout-color-caution: #f0ad4e;--quarto-scss-export-callout-color-warning: #ff7518} diff --git a/demo/demo/_site/site_libs/bootstrap/bootstrap-icons.css b/demo/demo/_site/site_libs/bootstrap/bootstrap-icons.css deleted file mode 100644 index 285e444..0000000 --- a/demo/demo/_site/site_libs/bootstrap/bootstrap-icons.css +++ /dev/null @@ -1,2078 +0,0 @@ -/*! - * Bootstrap Icons v1.11.1 (https://icons.getbootstrap.com/) - * Copyright 2019-2023 The Bootstrap Authors - * Licensed under MIT (https://github.com/twbs/icons/blob/main/LICENSE) - */ - -@font-face { - font-display: block; - font-family: "bootstrap-icons"; - src: -url("./bootstrap-icons.woff?2820a3852bdb9a5832199cc61cec4e65") 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t.defaultInterval=t.interval,t}_addEventListeners(){this._config.keyboard&&N.on(this._element,ft,(t=>this._keydown(t))),"hover"===this._config.pause&&(N.on(this._element,pt,(()=>this.pause())),N.on(this._element,mt,(()=>this._maybeEnableCycle()))),this._config.touch&&st.isSupported()&&this._addTouchEventListeners()}_addTouchEventListeners(){for(const t of z.find(".carousel-item img",this._element))N.on(t,gt,(t=>t.preventDefault()));const t={leftCallback:()=>this._slide(this._directionToOrder(ct)),rightCallback:()=>this._slide(this._directionToOrder(ht)),endCallback:()=>{"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((()=>this._maybeEnableCycle()),500+this._config.interval))}};this._swipeHelper=new st(this._element,t)}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=Tt[t.key];e&&(t.preventDefault(),this._slide(this._directionToOrder(e)))}_getItemIndex(t){return this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=z.findOne(wt,this._indicatorsElement);e.classList.remove(yt),e.removeAttribute("aria-current");const i=z.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(yt),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===at,s=e||b(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>N.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(dt).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),d(s),i.classList.add(l),s.classList.add(l),this._queueCallback((()=>{s.classList.remove(l,c),s.classList.add(yt),i.classList.remove(yt,c,l),this._isSliding=!1,r(ut)}),i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return z.findOne(Et,this._element)}_getItems(){return z.find(At,this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return p()?t===ct?lt:at:t===ct?at:lt}_orderToDirection(t){return p()?t===lt?ct:ht:t===lt?ht:ct}static jQueryInterface(t){return this.each((function(){const e=xt.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}N.on(document,bt,"[data-bs-slide], 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i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(Mt),this._element.classList.add(Pt,Nt),this._element.style[e]="",N.trigger(this._element,St)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(N.trigger(this._element,Dt).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,d(this._element),this._element.classList.add(Mt),this._element.classList.remove(Pt,Nt);for(const t of this._triggerArray){const e=z.getElementFromSelector(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(Mt),this._element.classList.add(Pt),N.trigger(this._element,$t)}),this._element,!0)}_isShown(t=this._element){return 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J,Z="x"===y?zt:Vt,tt="x"===y?Rt:qt,et=A[w],it="y"===w?"height":"width",nt=et+g[Z],st=et-g[tt],ot=-1!==[zt,Vt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=Ne(t,e,i);return n>i?i:n}(at,et,lt):Ne(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:["offset"]};function di(t,e,i){void 0===i&&(i=!1);var n,s,o=me(e),r=me(e)&&function(t){var e=t.getBoundingClientRect(),i=we(e.width)/t.offsetWidth||1,n=we(e.height)/t.offsetHeight||1;return 1!==i||1!==n}(e),a=Le(e),l=Te(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&(("body"!==ue(e)||Ue(a))&&(c=(n=e)!==fe(n)&&me(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Xe(n)),me(e)?((h=Te(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ye(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function ui(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;iNumber.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu 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e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"

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")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(us);const fs=".bs.scrollspy",ps=`activate${fs}`,ms=`click${fs}`,gs=`load${fs}.data-api`,_s="active",bs="[href]",vs=".nav-link",ys=`${vs}, .nav-item > ${vs}, .list-group-item`,ws={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},As={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Es extends W{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new 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e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=z.find(bs,this._config.target);for(const e of t){if(!e.hash||l(e))continue;const t=z.findOne(decodeURI(e.hash),this._element);a(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),N.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))z.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of z.parents(t,".nav, .list-group"))for(const t of z.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=z.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=Es.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}N.on(window,gs,(()=>{for(const t of z.find('[data-bs-spy="scroll"]'))Es.getOrCreateInstance(t)})),m(Es);const Ts=".bs.tab",Cs=`hide${Ts}`,Os=`hidden${Ts}`,xs=`show${Ts}`,ks=`shown${Ts}`,Ls=`click${Ts}`,Ss=`keydown${Ts}`,Ds=`load${Ts}`,$s="ArrowLeft",Is="ArrowRight",Ns="ArrowUp",Ps="ArrowDown",Ms="Home",js="End",Fs="active",Hs="fade",Ws="show",Bs=":not(.dropdown-toggle)",zs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Rs=`.nav-link${Bs}, .list-group-item${Bs}, [role="tab"]${Bs}, ${zs}`,qs=`.${Fs}[data-bs-toggle="tab"], .${Fs}[data-bs-toggle="pill"], .${Fs}[data-bs-toggle="list"]`;class Vs extends W{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),N.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const 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t=e.state,n=e.name,r=t.rects.reference,o=t.rects.popper,i=t.modifiersData.preventOverflow,a=J(t,{elementContext:"reference"}),s=J(t,{altBoundary:!0}),f=ve(a,r),c=ve(s,o,i),p=ye(f),u=ye(c);t.modifiersData[n]={referenceClippingOffsets:f,popperEscapeOffsets:c,isReferenceHidden:p,hasPopperEscaped:u},t.attributes.popper=Object.assign({},t.attributes.popper,{"data-popper-reference-hidden":p,"data-popper-escaped":u})}},be=Z({defaultModifiers:[ee,te,oe,ie]}),xe=[ee,te,oe,ie,ae,le,he,me,ge],we=Z({defaultModifiers:xe});e.applyStyles=ie,e.arrow=me,e.computeStyles=oe,e.createPopper=we,e.createPopperLite=be,e.defaultModifiers=xe,e.detectOverflow=J,e.eventListeners=ee,e.flip=le,e.hide=ge,e.offset=ae,e.popperGenerator=Z,e.popperOffsets=te,e.preventOverflow=he,Object.defineProperty(e,"__esModule",{value:!0})})); - diff --git a/demo/demo/_site/site_libs/quarto-html/quarto-syntax-highlighting-07ba0ad10f5680c660e360ac31d2f3b6.css b/demo/demo/_site/site_libs/quarto-html/quarto-syntax-highlighting-07ba0ad10f5680c660e360ac31d2f3b6.css deleted file mode 100644 index 254faed..0000000 --- a/demo/demo/_site/site_libs/quarto-html/quarto-syntax-highlighting-07ba0ad10f5680c660e360ac31d2f3b6.css +++ /dev/null @@ -1,205 +0,0 @@ -/* quarto syntax highlight colors */ -:root { - --quarto-hl-ot-color: #003B4F; - --quarto-hl-at-color: #657422; - --quarto-hl-ss-color: #20794D; - --quarto-hl-an-color: #5E5E5E; - --quarto-hl-fu-color: #4758AB; - --quarto-hl-st-color: #20794D; - --quarto-hl-cf-color: #003B4F; - --quarto-hl-op-color: #5E5E5E; - --quarto-hl-er-color: #AD0000; - --quarto-hl-bn-color: #AD0000; - --quarto-hl-al-color: #AD0000; - --quarto-hl-va-color: #111111; - --quarto-hl-bu-color: inherit; - --quarto-hl-ex-color: inherit; - --quarto-hl-pp-color: #AD0000; - --quarto-hl-in-color: #5E5E5E; - --quarto-hl-vs-color: #20794D; - --quarto-hl-wa-color: #5E5E5E; - --quarto-hl-do-color: #5E5E5E; - --quarto-hl-im-color: #00769E; - --quarto-hl-ch-color: #20794D; - --quarto-hl-dt-color: #AD0000; - --quarto-hl-fl-color: #AD0000; - --quarto-hl-co-color: #5E5E5E; - --quarto-hl-cv-color: #5E5E5E; - --quarto-hl-cn-color: #8f5902; - --quarto-hl-sc-color: #5E5E5E; - --quarto-hl-dv-color: #AD0000; - --quarto-hl-kw-color: #003B4F; -} - -/* other quarto variables */ -:root { - --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; -} - -pre > code.sourceCode > span { - color: #003B4F; -} - -code span { - color: #003B4F; -} - -code.sourceCode > span { - color: #003B4F; -} - -div.sourceCode, -div.sourceCode pre.sourceCode { - color: #003B4F; -} - -code span.ot { - color: #003B4F; - font-style: inherit; -} - -code span.at { - color: #657422; - font-style: inherit; -} - -code span.ss { - color: #20794D; - font-style: inherit; -} - -code span.an { - color: #5E5E5E; - font-style: inherit; -} - -code span.fu { - color: #4758AB; - font-style: inherit; -} - -code span.st { - color: #20794D; - font-style: inherit; -} - -code span.cf { - color: #003B4F; - font-weight: bold; - font-style: inherit; -} - -code span.op { - color: #5E5E5E; - font-style: inherit; -} - -code span.er { - color: #AD0000; - font-style: inherit; -} - -code span.bn { - color: #AD0000; - font-style: inherit; -} - -code span.al { - color: #AD0000; - font-style: inherit; -} - -code span.va { - color: #111111; - font-style: inherit; -} - -code span.bu { - font-style: inherit; -} - -code span.ex { - font-style: inherit; -} - -code span.pp { - color: #AD0000; - font-style: inherit; -} - -code span.in { - color: #5E5E5E; - font-style: inherit; -} - -code span.vs { - color: #20794D; - font-style: inherit; -} - -code span.wa { - color: #5E5E5E; - font-style: italic; -} - -code span.do { - color: #5E5E5E; - font-style: italic; -} - -code span.im { - color: #00769E; - font-style: inherit; -} - -code span.ch { - color: #20794D; - font-style: inherit; -} - -code span.dt { - color: #AD0000; - font-style: inherit; -} - -code span.fl { - color: #AD0000; - font-style: inherit; -} - -code span.co { - color: #5E5E5E; - font-style: inherit; -} - -code span.cv { - color: #5E5E5E; - font-style: italic; -} - -code span.cn { - color: #8f5902; - font-style: inherit; -} - -code span.sc { - color: #5E5E5E; - font-style: inherit; -} - -code span.dv { - color: #AD0000; - font-style: inherit; -} - -code span.kw { - color: #003B4F; - font-weight: bold; - font-style: inherit; -} - -.prevent-inlining { - content: " { - // Find any conflicting margin elements and add margins to the - // top to prevent overlap - const marginChildren = window.document.querySelectorAll( - ".column-margin.column-container > *, .margin-caption, .aside" - ); - - let lastBottom = 0; - for (const marginChild of marginChildren) { - if (marginChild.offsetParent !== null) { - // clear the top margin so we recompute it - marginChild.style.marginTop = null; - const top = marginChild.getBoundingClientRect().top + window.scrollY; - if (top < lastBottom) { - const marginChildStyle = window.getComputedStyle(marginChild); - const marginBottom = parseFloat(marginChildStyle["marginBottom"]); - const margin = lastBottom - top + marginBottom; - marginChild.style.marginTop = `${margin}px`; - } - const styles = window.getComputedStyle(marginChild); - const marginTop = parseFloat(styles["marginTop"]); - lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; - } - } -}; - -window.document.addEventListener("DOMContentLoaded", function (_event) { - // Recompute the position of margin elements anytime the body size changes - if (window.ResizeObserver) { - const resizeObserver = new window.ResizeObserver( - throttle(() => { - layoutMarginEls(); - if ( - window.document.body.getBoundingClientRect().width < 990 && - isReaderMode() - ) { - quartoToggleReader(); - } - }, 50) - ); - resizeObserver.observe(window.document.body); - } - - const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); - const sidebarEl = window.document.getElementById("quarto-sidebar"); - const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); - const marginSidebarEl = window.document.getElementById( - "quarto-margin-sidebar" - ); - // function to determine whether the element has a previous sibling that is active - const prevSiblingIsActiveLink = (el) => { - const sibling = el.previousElementSibling; - if (sibling && sibling.tagName === "A") { - return sibling.classList.contains("active"); - } else { - return false; - } - }; - - // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) - function fireSlideEnter(e) { - const event = window.document.createEvent("Event"); - event.initEvent("slideenter", true, true); - window.document.dispatchEvent(event); - } - const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); - tabs.forEach((tab) => { - tab.addEventListener("shown.bs.tab", fireSlideEnter); - }); - - // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) - document.addEventListener("tabby", fireSlideEnter, false); - - // Track scrolling and mark TOC links as active - // get table of contents and sidebar (bail if we don't have at least one) - const tocLinks = tocEl - ? [...tocEl.querySelectorAll("a[data-scroll-target]")] - : []; - const makeActive = (link) => tocLinks[link].classList.add("active"); - const removeActive = (link) => tocLinks[link].classList.remove("active"); - const removeAllActive = () => - [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); - - // activate the anchor for a section associated with this TOC entry - tocLinks.forEach((link) => { - link.addEventListener("click", () => { - if (link.href.indexOf("#") !== -1) { - const anchor = link.href.split("#")[1]; - const heading = window.document.querySelector( - `[data-anchor-id="${anchor}"]` - ); - if (heading) { - // Add the class - heading.classList.add("reveal-anchorjs-link"); - - // function to show the anchor - const handleMouseout = () => { - heading.classList.remove("reveal-anchorjs-link"); - heading.removeEventListener("mouseout", handleMouseout); - }; - - // add a function to clear the anchor when the user mouses out of it - heading.addEventListener("mouseout", handleMouseout); - } - } - }); - }); - - const sections = tocLinks.map((link) => { - const target = link.getAttribute("data-scroll-target"); - if (target.startsWith("#")) { - return window.document.getElementById(decodeURI(`${target.slice(1)}`)); - } else { - return window.document.querySelector(decodeURI(`${target}`)); - } - }); - - const sectionMargin = 200; - let currentActive = 0; - // track whether we've initialized state the first time - let init = false; - - const updateActiveLink = () => { - // The index from bottom to top (e.g. reversed list) - let sectionIndex = -1; - if ( - window.innerHeight + window.pageYOffset >= - window.document.body.offsetHeight - ) { - // This is the no-scroll case where last section should be the active one - sectionIndex = 0; - } else { - // This finds the last section visible on screen that should be made active - sectionIndex = [...sections].reverse().findIndex((section) => { - if (section) { - return window.pageYOffset >= section.offsetTop - sectionMargin; - } else { - return false; - } - }); - } - if (sectionIndex > -1) { - const current = sections.length - sectionIndex - 1; - if (current !== currentActive) { - removeAllActive(); - currentActive = current; - makeActive(current); - if (init) { - window.dispatchEvent(sectionChanged); - } - init = true; - } - } - }; - - const inHiddenRegion = (top, bottom, hiddenRegions) => { - for (const region of hiddenRegions) { - if (top <= region.bottom && bottom >= region.top) { - return true; - } - } - return false; - }; - - const categorySelector = "header.quarto-title-block .quarto-category"; - const activateCategories = (href) => { - // Find any categories - // Surround them with a link pointing back to: - // #category=Authoring - try { - const categoryEls = window.document.querySelectorAll(categorySelector); - for (const categoryEl of categoryEls) { - const categoryText = categoryEl.textContent; - if (categoryText) { - const link = `${href}#category=${encodeURIComponent(categoryText)}`; - const linkEl = window.document.createElement("a"); - linkEl.setAttribute("href", link); - for (const child of categoryEl.childNodes) { - linkEl.append(child); - } - categoryEl.appendChild(linkEl); - } - } - } catch { - // Ignore errors - } - }; - function hasTitleCategories() { - return window.document.querySelector(categorySelector) !== null; - } - - function offsetRelativeUrl(url) { - const offset = getMeta("quarto:offset"); - return offset ? offset + url : url; - } - - function offsetAbsoluteUrl(url) { - const offset = getMeta("quarto:offset"); - const baseUrl = new URL(offset, window.location); - - const projRelativeUrl = url.replace(baseUrl, ""); - if (projRelativeUrl.startsWith("/")) { - return projRelativeUrl; - } else { - return "/" + projRelativeUrl; - } - } - - // read a meta tag value - function getMeta(metaName) { - const metas = window.document.getElementsByTagName("meta"); - for (let i = 0; i < metas.length; i++) { - if (metas[i].getAttribute("name") === metaName) { - return metas[i].getAttribute("content"); - } - } - return ""; - } - - async function findAndActivateCategories() { - // Categories search with listing only use path without query - const currentPagePath = offsetAbsoluteUrl( - window.location.origin + window.location.pathname - ); - const response = await fetch(offsetRelativeUrl("listings.json")); - if (response.status == 200) { - return response.json().then(function (listingPaths) { - const listingHrefs = []; - for (const listingPath of listingPaths) { - const pathWithoutLeadingSlash = listingPath.listing.substring(1); - for (const item of listingPath.items) { - if ( - item === currentPagePath || - item === currentPagePath + "index.html" - ) { - // Resolve this path against the offset to be sure - // we already are using the correct path to the listing - // (this adjusts the listing urls to be rooted against - // whatever root the page is actually running against) - const relative = offsetRelativeUrl(pathWithoutLeadingSlash); - const baseUrl = window.location; - const resolvedPath = new URL(relative, baseUrl); - listingHrefs.push(resolvedPath.pathname); - break; - } - } - } - - // Look up the tree for a nearby linting and use that if we find one - const nearestListing = findNearestParentListing( - offsetAbsoluteUrl(window.location.pathname), - listingHrefs - ); - if (nearestListing) { - activateCategories(nearestListing); - } else { - // See if the referrer is a listing page for this item - const referredRelativePath = offsetAbsoluteUrl(document.referrer); - const referrerListing = listingHrefs.find((listingHref) => { - const isListingReferrer = - listingHref === referredRelativePath || - listingHref === referredRelativePath + "index.html"; - return isListingReferrer; - }); - - if (referrerListing) { - // Try to use the referrer if possible - activateCategories(referrerListing); - } else if (listingHrefs.length > 0) { - // Otherwise, just fall back to the first listing - activateCategories(listingHrefs[0]); - } - } - }); - } - } - if (hasTitleCategories()) { - findAndActivateCategories(); - } - - const findNearestParentListing = (href, listingHrefs) => { - if (!href || !listingHrefs) { - return undefined; - } - // Look up the tree for a nearby linting and use that if we find one - const relativeParts = href.substring(1).split("/"); - while (relativeParts.length > 0) { - const path = relativeParts.join("/"); - for (const listingHref of listingHrefs) { - if (listingHref.startsWith(path)) { - return listingHref; - } - } - relativeParts.pop(); - } - - return undefined; - }; - - const manageSidebarVisiblity = (el, placeholderDescriptor) => { - let isVisible = true; - let elRect; - - return (hiddenRegions) => { - if (el === null) { - return; - } - - // Find the last element of the TOC - const lastChildEl = el.lastElementChild; - - if (lastChildEl) { - // Converts the sidebar to a menu - const convertToMenu = () => { - for (const child of el.children) { - child.style.opacity = 0; - child.style.overflow = "hidden"; - child.style.pointerEvents = "none"; - } - - nexttick(() => { - const toggleContainer = window.document.createElement("div"); - toggleContainer.style.width = "100%"; - toggleContainer.classList.add("zindex-over-content"); - toggleContainer.classList.add("quarto-sidebar-toggle"); - toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom - toggleContainer.id = placeholderDescriptor.id; - toggleContainer.style.position = "fixed"; - - const toggleIcon = window.document.createElement("i"); - toggleIcon.classList.add("quarto-sidebar-toggle-icon"); - toggleIcon.classList.add("bi"); - toggleIcon.classList.add("bi-caret-down-fill"); - - const toggleTitle = window.document.createElement("div"); - const titleEl = window.document.body.querySelector( - placeholderDescriptor.titleSelector - ); - if (titleEl) { - toggleTitle.append( - titleEl.textContent || titleEl.innerText, - toggleIcon - ); - } - toggleTitle.classList.add("zindex-over-content"); - toggleTitle.classList.add("quarto-sidebar-toggle-title"); - toggleContainer.append(toggleTitle); - - const toggleContents = window.document.createElement("div"); - toggleContents.classList = el.classList; - toggleContents.classList.add("zindex-over-content"); - toggleContents.classList.add("quarto-sidebar-toggle-contents"); - for (const child of el.children) { - if (child.id === "toc-title") { - continue; - } - - const clone = child.cloneNode(true); - clone.style.opacity = 1; - clone.style.pointerEvents = null; - clone.style.display = null; - toggleContents.append(clone); - } - toggleContents.style.height = "0px"; - const positionToggle = () => { - // position the element (top left of parent, same width as parent) - if (!elRect) { - elRect = el.getBoundingClientRect(); - } - toggleContainer.style.left = `${elRect.left}px`; - toggleContainer.style.top = `${elRect.top}px`; - toggleContainer.style.width = `${elRect.width}px`; - }; - positionToggle(); - - toggleContainer.append(toggleContents); - el.parentElement.prepend(toggleContainer); - - // Process clicks - let tocShowing = false; - // Allow the caller to control whether this is dismissed - // when it is clicked (e.g. sidebar navigation supports - // opening and closing the nav tree, so don't dismiss on click) - const clickEl = placeholderDescriptor.dismissOnClick - ? toggleContainer - : toggleTitle; - - const closeToggle = () => { - if (tocShowing) { - toggleContainer.classList.remove("expanded"); - toggleContents.style.height = "0px"; - tocShowing = false; - } - }; - - // Get rid of any expanded toggle if the user scrolls - window.document.addEventListener( - "scroll", - throttle(() => { - closeToggle(); - }, 50) - ); - - // Handle positioning of the toggle - window.addEventListener( - "resize", - throttle(() => { - elRect = undefined; - positionToggle(); - }, 50) - ); - - window.addEventListener("quarto-hrChanged", () => { - elRect = undefined; - }); - - // Process the click - clickEl.onclick = () => { - if (!tocShowing) { - toggleContainer.classList.add("expanded"); - toggleContents.style.height = null; - tocShowing = true; - } else { - closeToggle(); - } - }; - }); - }; - - // Converts a sidebar from a menu back to a sidebar - const convertToSidebar = () => { - for (const child of el.children) { - child.style.opacity = 1; - child.style.overflow = null; - child.style.pointerEvents = null; - } - - const placeholderEl = window.document.getElementById( - placeholderDescriptor.id - ); - if (placeholderEl) { - placeholderEl.remove(); - } - - el.classList.remove("rollup"); - }; - - if (isReaderMode()) { - convertToMenu(); - isVisible = false; - } else { - // Find the top and bottom o the element that is being managed - const elTop = el.offsetTop; - const elBottom = - elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; - - if (!isVisible) { - // If the element is current not visible reveal if there are - // no conflicts with overlay regions - if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { - convertToSidebar(); - isVisible = true; - } - } else { - // If the element is visible, hide it if it conflicts with overlay regions - // and insert a placeholder toggle (or if we're in reader mode) - if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { - convertToMenu(); - isVisible = false; - } - } - } - } - }; - }; - - const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); - for (const tabEl of tabEls) { - const id = tabEl.getAttribute("data-bs-target"); - if (id) { - const columnEl = document.querySelector( - `${id} .column-margin, .tabset-margin-content` - ); - if (columnEl) - tabEl.addEventListener("shown.bs.tab", function (event) { - const el = event.srcElement; - if (el) { - const visibleCls = `${el.id}-margin-content`; - // walk up until we find a parent tabset - let panelTabsetEl = el.parentElement; - while (panelTabsetEl) { - if (panelTabsetEl.classList.contains("panel-tabset")) { - break; - } - panelTabsetEl = panelTabsetEl.parentElement; - } - - if (panelTabsetEl) { - const prevSib = panelTabsetEl.previousElementSibling; - if ( - prevSib && - prevSib.classList.contains("tabset-margin-container") - ) { - const childNodes = prevSib.querySelectorAll( - ".tabset-margin-content" - ); - for (const childEl of childNodes) { - if (childEl.classList.contains(visibleCls)) { - childEl.classList.remove("collapse"); - } else { - childEl.classList.add("collapse"); 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- const leftSideConflictEls = arrConflictingEls.filter((el) => { - if (el.tagName === "ASIDE") { - return false; - } - return Array.from(el.classList).find((className) => { - return ( - className !== "column-body" && - className.startsWith("column-") && - !className.endsWith("right") && - !className.endsWith("container") && - className !== "column-margin" - ); - }); - }); - const rightSideConflictEls = arrConflictingEls.filter((el) => { - if (el.tagName === "ASIDE") { - return true; - } - - const hasMarginCaption = Array.from(el.classList).find((className) => { - return className == "margin-caption"; - }); - if (hasMarginCaption) { - return true; - } - - return Array.from(el.classList).find((className) => { - return ( - className !== "column-body" && - !className.endsWith("container") && - className.startsWith("column-") && - !className.endsWith("left") - ); - }); - }); - - const kOverlapPaddingSize = 10; - function toRegions(els) { - return els.map((el) => { - const boundRect = el.getBoundingClientRect(); - const top = - boundRect.top + - document.documentElement.scrollTop - - kOverlapPaddingSize; - return { - top, - bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, - }; - }); - } - - let hasObserved = false; - const visibleItemObserver = (els) => { - let visibleElements = [...els]; - const intersectionObserver = new IntersectionObserver( - (entries, _observer) => { - entries.forEach((entry) => { - if (entry.isIntersecting) { - if (visibleElements.indexOf(entry.target) === -1) { - visibleElements.push(entry.target); - } - } else { - visibleElements = visibleElements.filter((visibleEntry) => { - return visibleEntry !== entry; - }); - } - }); - - if (!hasObserved) { - hideOverlappedSidebars(); - } - hasObserved = true; - }, - {} - ); - els.forEach((el) => { - intersectionObserver.observe(el); - }); - - return { - getVisibleEntries: () => { - return visibleElements; - }, - }; - }; - - const rightElementObserver = visibleItemObserver(rightSideConflictEls); - const leftElementObserver = visibleItemObserver(leftSideConflictEls); - - const hideOverlappedSidebars = () => { - marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); - sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); - if (tocLeftScrollVisibility) { - tocLeftScrollVisibility( - toRegions(leftElementObserver.getVisibleEntries()) - ); - } - }; - - window.quartoToggleReader = () => { - // Applies a slow class (or removes it) - // to update the transition speed - const slowTransition = (slow) => { - const manageTransition = (id, slow) => { - const el = document.getElementById(id); - if (el) { - if (slow) { - el.classList.add("slow"); - } else { - el.classList.remove("slow"); - } - } - }; - - manageTransition("TOC", slow); - manageTransition("quarto-sidebar", slow); - }; - const readerMode = !isReaderMode(); - setReaderModeValue(readerMode); - - // If we're entering reader mode, slow the transition - if (readerMode) { - slowTransition(readerMode); - } - highlightReaderToggle(readerMode); - hideOverlappedSidebars(); - - // If we're exiting reader mode, restore the non-slow transition - if (!readerMode) { - slowTransition(!readerMode); - } - }; - - const highlightReaderToggle = (readerMode) => { - const els = document.querySelectorAll(".quarto-reader-toggle"); - if (els) { - els.forEach((el) => { - if (readerMode) { - el.classList.add("reader"); - } else { - el.classList.remove("reader"); - } - }); - } - }; - - const setReaderModeValue = (val) => { - if (window.location.protocol !== "file:") { - window.localStorage.setItem("quarto-reader-mode", val); - } else { - localReaderMode = val; - } - }; - - const isReaderMode = () => { - if (window.location.protocol !== "file:") { - return window.localStorage.getItem("quarto-reader-mode") === "true"; - } else { - return localReaderMode; - } - }; - let localReaderMode = null; - - const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); - const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; - - // Walk the TOC and collapse/expand nodes - // Nodes are expanded if: - // - they are top level - // - they have children that are 'active' links - // - they are directly below an link that is 'active' - const walk = (el, depth) => { - // Tick depth when we enter a UL - if (el.tagName === "UL") { - depth = depth + 1; - } - - // It this is active link - let isActiveNode = false; - if (el.tagName === "A" && el.classList.contains("active")) { - isActiveNode = true; - } - - // See if there is an active child to this element - let hasActiveChild = false; - for (child of el.children) { - hasActiveChild = walk(child, depth) || hasActiveChild; - } - - // Process the collapse state if this is an UL - if (el.tagName === "UL") { - if (tocOpenDepth === -1 && depth > 1) { - // toc-expand: false - el.classList.add("collapse"); - } else if ( - depth <= tocOpenDepth || - hasActiveChild || - prevSiblingIsActiveLink(el) - ) { - el.classList.remove("collapse"); - } else { - el.classList.add("collapse"); - } - - // untick depth when we leave a UL - depth = depth - 1; - } - return hasActiveChild || isActiveNode; - }; - - // walk the TOC and expand / collapse any items that should be shown - if (tocEl) { - updateActiveLink(); - walk(tocEl, 0); - } - - // Throttle the scroll event and walk peridiocally - window.document.addEventListener( - "scroll", - throttle(() => { - if (tocEl) { - updateActiveLink(); - walk(tocEl, 0); - } - if (!isReaderMode()) { - hideOverlappedSidebars(); - } - }, 5) - ); - window.addEventListener( - "resize", - throttle(() => { - if (tocEl) { - updateActiveLink(); - walk(tocEl, 0); - } - if (!isReaderMode()) { - hideOverlappedSidebars(); - } - }, 10) - ); - hideOverlappedSidebars(); - highlightReaderToggle(isReaderMode()); -}); - -// grouped tabsets -window.addEventListener("pageshow", (_event) => { - function getTabSettings() { - const data = localStorage.getItem("quarto-persistent-tabsets-data"); - if (!data) { - localStorage.setItem("quarto-persistent-tabsets-data", "{}"); - return {}; - } - if (data) { - return JSON.parse(data); - } - } - - function setTabSettings(data) { - localStorage.setItem( - "quarto-persistent-tabsets-data", - JSON.stringify(data) - ); - } - - function setTabState(groupName, groupValue) { - const data = getTabSettings(); - data[groupName] = groupValue; - setTabSettings(data); - } - - function toggleTab(tab, active) { - const tabPanelId = tab.getAttribute("aria-controls"); - const tabPanel = document.getElementById(tabPanelId); - if (active) { - tab.classList.add("active"); - tabPanel.classList.add("active"); - } else { - tab.classList.remove("active"); - tabPanel.classList.remove("active"); - } - } - - function toggleAll(selectedGroup, selectorsToSync) { - for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { - const active = selectedGroup === thisGroup; - for (const tab of tabs) { - toggleTab(tab, active); - } - } - } - - function findSelectorsToSyncByLanguage() { - const result = {}; - const tabs = Array.from( - document.querySelectorAll(`div[data-group] a[id^='tabset-']`) - ); - for (const item of tabs) { - const div = item.parentElement.parentElement.parentElement; - const group = div.getAttribute("data-group"); - if (!result[group]) { - result[group] = {}; - } - const selectorsToSync = result[group]; - const value = item.innerHTML; - if (!selectorsToSync[value]) { - selectorsToSync[value] = []; - } - selectorsToSync[value].push(item); - } - return result; - } - - function setupSelectorSync() { - const selectorsToSync = findSelectorsToSyncByLanguage(); - Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { - Object.entries(tabSetsByValue).forEach(([value, items]) => { - items.forEach((item) => { - item.addEventListener("click", (_event) => { - setTabState(group, value); - toggleAll(value, selectorsToSync[group]); - }); - }); - }); - }); - return selectorsToSync; - } - - const selectorsToSync 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hash.split(kAnd); - const hashValues = hasValuesStrs - .map((hashValueStr) => { - const vals = hashValueStr.split(kEquals); - if (vals.length === 2) { - return { name: vals[0], value: vals[1] }; - } else { - return undefined; - } - }) - .filter((value) => { - return value !== undefined; - }); - - const hashObj = {}; - hashValues.forEach((hashValue) => { - hashObj[hashValue.name] = decodeURIComponent(hashValue.value); - }); - return hashObj; -} - -function makeHash(obj) { - return Object.keys(obj) - .map((key) => { - return `${key}${kEquals}${obj[key]}`; - }) - .join(kAnd); -} - -function setHash(obj) { - const hash = makeHash(obj); - window.history.pushState(null, null, `#${hash}`); -} - -function showPage(listingId, page) { - const list = window["quarto-listings"][listingId]; - if (list) { - list.show((page - 1) * list.page + 1, list.page); - } -} - -function activateCategory(category) { - // Deactivate existing categories - const activeEls = window.document.querySelectorAll( - ".quarto-listing-category .category.active" - ); - for (const activeEl of activeEls) { - activeEl.classList.remove("active"); - } - - // Activate this category - const categoryEl = window.document.querySelector( - `.quarto-listing-category .category[data-category='${btoa(category)}']` - ); - if (categoryEl) { - categoryEl.classList.add("active"); - } - - // Filter the listings to this category - filterListingCategory(category); -} - -function filterListingCategory(category) { - const listingIds = Object.keys(window["quarto-listings"]); - for (const listingId of listingIds) { - const list = window["quarto-listings"][listingId]; - if (list) { - if (category === "") { - // resets the filter - list.filter(); - } else { - // filter to this category - list.filter(function (item) { - const itemValues = item.values(); - if (itemValues.categories !== null) { - const categories = atob(itemValues.categories).split(","); - return categories.includes(category); - } else { - return false; - } - }); 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38cc430..0000000 --- a/demo/demo/_site/site_libs/quarto-nav/quarto-nav.js +++ /dev/null @@ -1,325 +0,0 @@ -const headroomChanged = new CustomEvent("quarto-hrChanged", { - detail: {}, - bubbles: true, - cancelable: false, - composed: false, -}); - -const announceDismiss = () => { - const annEl = window.document.getElementById("quarto-announcement"); - if (annEl) { - annEl.remove(); - - const annId = annEl.getAttribute("data-announcement-id"); - window.localStorage.setItem(`quarto-announce-${annId}`, "true"); - } -}; - -const announceRegister = () => { - const annEl = window.document.getElementById("quarto-announcement"); - if (annEl) { - const annId = annEl.getAttribute("data-announcement-id"); - const isDismissed = - window.localStorage.getItem(`quarto-announce-${annId}`) || false; - if (isDismissed) { - announceDismiss(); - return; - } else { - annEl.classList.remove("hidden"); - } - - const actionEl = annEl.querySelector(".quarto-announcement-action"); - if (actionEl) { - 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a/demo/demo/_site/site_libs/quarto-search/quarto-search.js b/demo/demo/_site/site_libs/quarto-search/quarto-search.js deleted file mode 100644 index d788a95..0000000 --- a/demo/demo/_site/site_libs/quarto-search/quarto-search.js +++ /dev/null @@ -1,1290 +0,0 @@ -const kQueryArg = "q"; -const kResultsArg = "show-results"; - -// If items don't provide a URL, then both the navigator and the onSelect -// function aren't called (and therefore, the default implementation is used) -// -// We're using this sentinel URL to signal to those handlers that this -// item is a more item (along with the type) and can be handled appropriately -const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; - -window.document.addEventListener("DOMContentLoaded", function (_event) { - // Ensure that search is available on this page. If it isn't, - // should return early and not do anything - var searchEl = window.document.getElementById("quarto-search"); - if (!searchEl) return; - - const { autocomplete } = window["@algolia/autocomplete-js"]; - - let quartoSearchOptions = {}; - let language = {}; - const searchOptionEl = window.document.getElementById( - "quarto-search-options" - ); - if (searchOptionEl) { - const jsonStr = searchOptionEl.textContent; - quartoSearchOptions = JSON.parse(jsonStr); - language = quartoSearchOptions.language; - } - - // note the search mode - if (quartoSearchOptions.type === "overlay") { - searchEl.classList.add("type-overlay"); - } else { - searchEl.classList.add("type-textbox"); - } - - // Used to determine highlighting behavior for this page - // A `q` query param is expected when the user follows a search - // to this page - const currentUrl = new URL(window.location); - const query = currentUrl.searchParams.get(kQueryArg); - const showSearchResults = currentUrl.searchParams.get(kResultsArg); - const mainEl = window.document.querySelector("main"); - - // highlight matches on the page - if (query && mainEl) { - // perform any highlighting - highlight(escapeRegExp(query), mainEl); - - // fix up the URL to remove the q query param - const replacementUrl = new URL(window.location); - replacementUrl.searchParams.delete(kQueryArg); - window.history.replaceState({}, "", replacementUrl); - } - - // function to clear highlighting on the page when the search query changes - // (e.g. if the user edits the query or clears it) - let highlighting = true; - const resetHighlighting = (searchTerm) => { - if (mainEl && highlighting && query && searchTerm !== query) { - clearHighlight(query, mainEl); - highlighting = false; - } - }; - - // Clear search highlighting when the user scrolls sufficiently - const resetFn = () => { - resetHighlighting(""); - window.removeEventListener("quarto-hrChanged", resetFn); - window.removeEventListener("quarto-sectionChanged", resetFn); - }; - - // Register this event after the initial scrolling and settling of events - // on the page - window.addEventListener("quarto-hrChanged", resetFn); - window.addEventListener("quarto-sectionChanged", resetFn); - - // Responsively switch to overlay mode if the search is present on the navbar - // Note that switching the sidebar to overlay mode requires more coordinate (not just - // the media query since we generate different HTML for sidebar overlays than we do - // for sidebar input UI) - const detachedMediaQuery = - quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; - - // If configured, include the analytics client to send insights - const plugins = configurePlugins(quartoSearchOptions); - - let lastState = null; - const { setIsOpen, setQuery, setCollections } = autocomplete({ - container: searchEl, - detachedMediaQuery: detachedMediaQuery, - defaultActiveItemId: 0, - panelContainer: "#quarto-search-results", - panelPlacement: quartoSearchOptions["panel-placement"], - debug: false, - openOnFocus: true, - plugins, - classNames: { - form: "d-flex", - }, - placeholder: language["search-text-placeholder"], - translations: { - clearButtonTitle: language["search-clear-button-title"], - detachedCancelButtonText: language["search-detached-cancel-button-title"], - submitButtonTitle: language["search-submit-button-title"], - }, - initialState: { - query, - }, - getItemUrl({ item }) { - return item.href; - }, - onStateChange({ state }) { - // If this is a file URL, note that - - // Perhaps reset highlighting - resetHighlighting(state.query); - - // If the panel just opened, ensure the panel is positioned properly - if (state.isOpen) { - if (lastState && !lastState.isOpen) { - setTimeout(() => { - positionPanel(quartoSearchOptions["panel-placement"]); - }, 150); - } - } - - // Perhaps show the copy link - showCopyLink(state.query, quartoSearchOptions); - - lastState = state; - }, - reshape({ sources, state }) { - return sources.map((source) => { - try { - const items = source.getItems(); - - // Validate the items - validateItems(items); - - // group the items by document - const groupedItems = new Map(); - items.forEach((item) => { - const hrefParts = item.href.split("#"); - const baseHref = hrefParts[0]; - const isDocumentItem = hrefParts.length === 1; - - const items = groupedItems.get(baseHref); - if (!items) { - groupedItems.set(baseHref, [item]); - } else { - // If the href for this item matches the document - // exactly, place this item first as it is the item that represents - // the document itself - if (isDocumentItem) { - items.unshift(item); - } else { - items.push(item); - } - groupedItems.set(baseHref, items); - } - }); - - const reshapedItems = []; - let count = 1; - for (const [_key, value] of groupedItems) { - const firstItem = value[0]; - reshapedItems.push({ - ...firstItem, - type: kItemTypeDoc, - }); - - const collapseMatches = quartoSearchOptions["collapse-after"]; - const collapseCount = - typeof collapseMatches === "number" ? collapseMatches : 1; - - if (value.length > 1) { - const target = `search-more-${count}`; - const isExpanded = - state.context.expanded && - state.context.expanded.includes(target); - - const remainingCount = value.length - collapseCount; - - for (let i = 1; i < value.length; i++) { - if (collapseMatches && i === collapseCount) { - reshapedItems.push({ - target, - title: isExpanded - ? language["search-hide-matches-text"] - : remainingCount === 1 - ? `${remainingCount} ${language["search-more-match-text"]}` - : `${remainingCount} ${language["search-more-matches-text"]}`, - type: kItemTypeMore, - href: kItemTypeMoreHref, - }); - } - - if (isExpanded || !collapseMatches || i < collapseCount) { - reshapedItems.push({ - ...value[i], - type: kItemTypeItem, - target, - }); - } - } - } - count += 1; - } - - return { - ...source, - getItems() { - return reshapedItems; - }, - }; - } catch (error) { - // Some form of error occurred - return { - ...source, - getItems() { - return [ - { - title: error.name || "An Error Occurred While Searching", - text: - error.message || - "An unknown error occurred while attempting to perform the requested search.", - type: kItemTypeError, - }, - ]; - }, - }; - } - }); - }, - navigator: { - navigate({ itemUrl }) { - if (itemUrl !== offsetURL(kItemTypeMoreHref)) { - window.location.assign(itemUrl); - } - }, - navigateNewTab({ itemUrl }) { - if (itemUrl !== offsetURL(kItemTypeMoreHref)) { - const windowReference = window.open(itemUrl, "_blank", "noopener"); - if (windowReference) { - windowReference.focus(); - } - } - }, - navigateNewWindow({ itemUrl }) { - if (itemUrl !== offsetURL(kItemTypeMoreHref)) { - window.open(itemUrl, "_blank", "noopener"); - } - }, - }, - getSources({ state, setContext, setActiveItemId, refresh }) { - return [ - { - sourceId: "documents", - getItemUrl({ item }) { - if (item.href) { - return offsetURL(item.href); - } else { - return undefined; - } - }, - onSelect({ - item, - state, - setContext, - setIsOpen, - setActiveItemId, - refresh, - }) { - if (item.type === kItemTypeMore) { - toggleExpanded(item, state, setContext, setActiveItemId, refresh); - - // Toggle more - setIsOpen(true); - } - }, - getItems({ query }) { - if (query === null || query === "") { - return []; - } - - const limit = quartoSearchOptions.limit; - if (quartoSearchOptions.algolia) { - return algoliaSearch(query, limit, quartoSearchOptions.algolia); - } else { - // Fuse search options - const fuseSearchOptions = { - isCaseSensitive: false, - shouldSort: true, - minMatchCharLength: 2, - limit: limit, - }; - - return readSearchData().then(function (fuse) { - return fuseSearch(query, fuse, fuseSearchOptions); - }); - } - }, - templates: { - noResults({ createElement }) { - const hasQuery = lastState.query; - - return createElement( - "div", - { - class: `quarto-search-no-results${ - hasQuery ? "" : " no-query" - }`, - }, - language["search-no-results-text"] - ); - }, - header({ items, createElement }) { - // count the documents - const count = items.filter((item) => { - return item.type === kItemTypeDoc; - }).length; - - if (count > 0) { - return createElement( - "div", - { class: "search-result-header" }, - `${count} ${language["search-matching-documents-text"]}` - ); - } else { - return createElement( - "div", - { class: "search-result-header-no-results" }, - `` - ); - } - }, - footer({ _items, createElement }) { - if ( - quartoSearchOptions.algolia && - quartoSearchOptions.algolia["show-logo"] - ) { - const libDir = quartoSearchOptions.algolia["libDir"]; - const logo = createElement("img", { - src: offsetURL( - `${libDir}/quarto-search/search-by-algolia.svg` - ), - class: "algolia-search-logo", - }); - return createElement( - "a", - { href: "http://www.algolia.com/" }, - logo - ); - } - }, - - item({ item, createElement }) { - return renderItem( - item, - createElement, - state, - setActiveItemId, - setContext, - refresh, - quartoSearchOptions - ); - }, - }, - }, - ]; - }, - }); - - window.quartoOpenSearch = () => { - setIsOpen(false); - setIsOpen(true); - focusSearchInput(); - }; - - document.addEventListener("keyup", (event) => { - const { key } = event; - const kbds = quartoSearchOptions["keyboard-shortcut"]; - const focusedEl = document.activeElement; - - const isFormElFocused = [ - "input", - "select", - "textarea", - "button", - "option", - ].find((tag) => { - return focusedEl.tagName.toLowerCase() === tag; - }); - - if ( - kbds && - kbds.includes(key) && - !isFormElFocused && - !document.activeElement.isContentEditable - ) { - event.preventDefault(); - window.quartoOpenSearch(); - } - }); - - // Remove the labeleledby attribute since it is pointing - // to a non-existent label - if (quartoSearchOptions.type === "overlay") { - const inputEl = window.document.querySelector( - "#quarto-search .aa-Autocomplete" - ); - if (inputEl) { - inputEl.removeAttribute("aria-labelledby"); - } - } - - function throttle(func, wait) { - let waiting = false; - return function () { - if (!waiting) { - func.apply(this, arguments); - waiting = true; - setTimeout(function () { - waiting = false; - }, wait); - } - }; - } - - // If the main document scrolls dismiss the search results - // (otherwise, since they're floating in the document they can scroll with the document) - window.document.body.onscroll = throttle(() => { - // Only do this if we're not detached - // Bug #7117 - // This will happen when the keyboard is shown on ios (resulting in a scroll) - // which then closed the search UI - if (!window.matchMedia(detachedMediaQuery).matches) { - setIsOpen(false); - } - }, 50); - - if (showSearchResults) { - setIsOpen(true); - focusSearchInput(); - } -}); - -function configurePlugins(quartoSearchOptions) { - const autocompletePlugins = []; - const algoliaOptions = quartoSearchOptions.algolia; - if ( - algoliaOptions && - algoliaOptions["analytics-events"] && - algoliaOptions["search-only-api-key"] && - algoliaOptions["application-id"] - ) { - const apiKey = algoliaOptions["search-only-api-key"]; - const appId = algoliaOptions["application-id"]; - - // Aloglia insights may not be loaded because they require cookie consent - // Use deferred loading so events will start being recorded when/if consent - // is granted. - const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { - if ( - window.aa && - window["@algolia/autocomplete-plugin-algolia-insights"] - ) { - window.aa("init", { - appId, - apiKey, - useCookie: true, - }); - - const { createAlgoliaInsightsPlugin } = - window["@algolia/autocomplete-plugin-algolia-insights"]; - // Register the insights client - const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ - insightsClient: window.aa, - onItemsChange({ insights, insightsEvents }) { - const events = insightsEvents.flatMap((event) => { - // This API limits the number of items per event to 20 - const chunkSize = 20; - const itemChunks = []; - const eventItems = event.items; - for (let i = 0; i < eventItems.length; i += chunkSize) { - itemChunks.push(eventItems.slice(i, i + chunkSize)); - } - // Split the items into multiple events that can be sent - const events = itemChunks.map((items) => { - return { - ...event, - items, - }; - }); - return events; - }); - - for (const event of events) { - insights.viewedObjectIDs(event); - } - }, - }); - return algoliaInsightsPlugin; - } - }); - - // Add the plugin - autocompletePlugins.push(algoliaInsightsDeferredPlugin); - return autocompletePlugins; - } -} - -// For plugins that may not load immediately, create a wrapper -// plugin and forward events and plugin data once the plugin -// is initialized. This is useful for cases like cookie consent -// which may prevent the analytics insights event plugin from initializing -// immediately. -function deferredLoadPlugin(createPlugin) { - let plugin = undefined; - let subscribeObj = undefined; - const wrappedPlugin = () => { - if (!plugin && subscribeObj) { - plugin = createPlugin(); - if (plugin && plugin.subscribe) { - plugin.subscribe(subscribeObj); - } - } - return plugin; - }; - - return { - subscribe: (obj) => { - subscribeObj = obj; - }, - onStateChange: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.onStateChange) { - plugin.onStateChange(obj); - } - }, - onSubmit: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.onSubmit) { - plugin.onSubmit(obj); - } - }, - onReset: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.onReset) { - plugin.onReset(obj); - } - }, - getSources: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.getSources) { - return plugin.getSources(obj); - } else { - return Promise.resolve([]); - } - }, - data: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.data) { - plugin.data(obj); - } - }, - }; -} - -function validateItems(items) { - // Validate the first item - if (items.length > 0) { - const item = items[0]; - const missingFields = []; - if (item.href == undefined) { - missingFields.push("href"); - } - if (!item.title == undefined) { - missingFields.push("title"); - } - if (!item.text == undefined) { - missingFields.push("text"); - } - - if (missingFields.length === 1) { - throw { - name: `Error: Search index is missing the ${missingFields[0]} field.`, - message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, - }; - } else if (missingFields.length > 1) { - const missingFieldList = missingFields - .map((field) => { - return `${field}`; - }) - .join(", "); - - throw { - name: `Error: Search index is missing the following fields: ${missingFieldList}.`, - message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, - }; - } - } -} - -let lastQuery = null; -function showCopyLink(query, options) { - const language = options.language; - lastQuery = query; - // Insert share icon - const inputSuffixEl = window.document.body.querySelector( - ".aa-Form .aa-InputWrapperSuffix" - ); - - if (inputSuffixEl) { - let copyButtonEl = window.document.body.querySelector( - ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" - ); - - if (copyButtonEl === null) { - copyButtonEl = window.document.createElement("button"); - copyButtonEl.setAttribute("class", "aa-CopyButton"); - copyButtonEl.setAttribute("type", "button"); - copyButtonEl.setAttribute("title", language["search-copy-link-title"]); - copyButtonEl.onmousedown = (e) => { - e.preventDefault(); - e.stopPropagation(); - }; - - const linkIcon = "bi-clipboard"; - const checkIcon = "bi-check2"; - - const shareIconEl = window.document.createElement("i"); - shareIconEl.setAttribute("class", `bi ${linkIcon}`); - copyButtonEl.appendChild(shareIconEl); - inputSuffixEl.prepend(copyButtonEl); - - const clipboard = new window.ClipboardJS(".aa-CopyButton", { - text: function (_trigger) { - const copyUrl = new URL(window.location); - copyUrl.searchParams.set(kQueryArg, lastQuery); - copyUrl.searchParams.set(kResultsArg, "1"); - return copyUrl.toString(); - }, - }); - clipboard.on("success", function (e) { - // Focus the input - - // button target - const button = e.trigger; - const icon = button.querySelector("i.bi"); - - // flash "checked" - icon.classList.add(checkIcon); - icon.classList.remove(linkIcon); - setTimeout(function () { - icon.classList.remove(checkIcon); - icon.classList.add(linkIcon); - }, 1000); - }); - } - - // If there is a query, show the link icon - if (copyButtonEl) { - if (lastQuery && options["copy-button"]) { - copyButtonEl.style.display = "flex"; - } else { - copyButtonEl.style.display = "none"; - } - } - } -} - -/* Search Index Handling */ -// create the index -var fuseIndex = undefined; -var shownWarning = false; - -// fuse index options -const kFuseIndexOptions = { - keys: [ - { name: "title", weight: 20 }, - { name: "section", weight: 20 }, - { name: "text", weight: 10 }, - ], - ignoreLocation: true, - threshold: 0.1, -}; - -async function readSearchData() { - // Initialize the search index on demand - if (fuseIndex === undefined) { - if (window.location.protocol === "file:" && !shownWarning) { - window.alert( - "Search requires JavaScript features disabled when running in file://... URLs. In order to use search, please run this document in a web server." - ); - shownWarning = true; - return; - } - const fuse = new window.Fuse([], kFuseIndexOptions); - - // fetch the main search.json - const response = await fetch(offsetURL("search.json")); - if (response.status == 200) { - return response.json().then(function (searchDocs) { - searchDocs.forEach(function (searchDoc) { - fuse.add(searchDoc); - }); - fuseIndex = fuse; - return fuseIndex; - }); - } else { - return Promise.reject( - new Error( - "Unexpected status from search index request: " + response.status - ) - ); - } - } - - return fuseIndex; -} - -function inputElement() { - return window.document.body.querySelector(".aa-Form .aa-Input"); -} - -function focusSearchInput() { - setTimeout(() => { - const inputEl = inputElement(); - if (inputEl) { - inputEl.focus(); - } - }, 50); -} - -/* Panels */ -const kItemTypeDoc = "document"; -const kItemTypeMore = "document-more"; -const kItemTypeItem = "document-item"; -const kItemTypeError = "error"; - -function renderItem( - item, - createElement, - state, - setActiveItemId, - setContext, - refresh, - quartoSearchOptions -) { - switch (item.type) { - case kItemTypeDoc: - return createDocumentCard( - createElement, - "file-richtext", - item.title, - item.section, - item.text, - item.href, - item.crumbs, - quartoSearchOptions - ); - case kItemTypeMore: - return createMoreCard( - createElement, - item, - state, - setActiveItemId, - setContext, - refresh - ); - case kItemTypeItem: - return createSectionCard( - createElement, - item.section, - item.text, - item.href - ); - case kItemTypeError: - return createErrorCard(createElement, item.title, item.text); - default: - return undefined; - } -} - -function createDocumentCard( - createElement, - icon, - title, - section, - text, - href, - crumbs, - quartoSearchOptions -) { - const iconEl = createElement("i", { - class: `bi bi-${icon} search-result-icon`, - }); - const titleEl = createElement("p", { class: "search-result-title" }, title); - const titleContents = [iconEl, titleEl]; - const showParent = quartoSearchOptions["show-item-context"]; - if (crumbs && showParent) { - let crumbsOut = undefined; - const crumbClz = ["search-result-crumbs"]; - if (showParent === "root") { - crumbsOut = crumbs.length > 1 ? crumbs[0] : undefined; - } else if (showParent === "parent") { - crumbsOut = crumbs.length > 1 ? crumbs[crumbs.length - 2] : undefined; - } else { - crumbsOut = crumbs.length > 1 ? crumbs.join(" > ") : undefined; - crumbClz.push("search-result-crumbs-wrap"); - } - - const crumbEl = createElement( - "p", - { class: crumbClz.join(" ") }, - crumbsOut - ); - titleContents.push(crumbEl); - } - - const titleContainerEl = createElement( - "div", - { class: "search-result-title-container" }, - titleContents - ); - - const textEls = []; - if (section) { - const sectionEl = createElement( - "p", - { class: "search-result-section" }, - section - ); - textEls.push(sectionEl); - } - const descEl = createElement("p", { - class: "search-result-text", - dangerouslySetInnerHTML: { - __html: text, - }, - }); - textEls.push(descEl); - - const textContainerEl = createElement( - "div", - { class: "search-result-text-container" }, - textEls - ); - - const containerEl = createElement( - "div", - { - class: "search-result-container", - }, - [titleContainerEl, textContainerEl] - ); - - const linkEl = createElement( - "a", - { - href: offsetURL(href), - class: "search-result-link", - }, - containerEl - ); - - const classes = ["search-result-doc", "search-item"]; - if (!section) { - classes.push("document-selectable"); - } - - return createElement( - "div", - { - class: classes.join(" "), - }, - linkEl - ); -} - -function createMoreCard( - createElement, - item, - state, - setActiveItemId, - setContext, - refresh -) { - const moreCardEl = createElement( - "div", - { - class: "search-result-more search-item", - onClick: (e) => { - // Handle expanding the sections by adding the expanded - // section to the list of expanded sections - toggleExpanded(item, state, setContext, setActiveItemId, refresh); - e.stopPropagation(); - }, - }, - item.title - ); - - return moreCardEl; -} - -function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { - const expanded = state.context.expanded || []; - if (expanded.includes(item.target)) { - setContext({ - expanded: expanded.filter((target) => target !== item.target), - }); - } else { - setContext({ expanded: [...expanded, item.target] }); - } - - refresh(); - setActiveItemId(item.__autocomplete_id); -} - -function createSectionCard(createElement, section, text, href) { - const sectionEl = createSection(createElement, section, text, href); - return createElement( - "div", - { - class: "search-result-doc-section search-item", - }, - sectionEl - ); -} - -function createSection(createElement, title, text, href) { - const descEl = createElement("p", { - class: "search-result-text", - dangerouslySetInnerHTML: { - __html: text, - }, - }); - - const titleEl = createElement("p", { class: "search-result-section" }, title); - const linkEl = createElement( - "a", - { - href: offsetURL(href), - class: "search-result-link", - }, - [titleEl, descEl] - ); - return linkEl; -} - -function createErrorCard(createElement, title, text) { - const descEl = createElement("p", { - class: "search-error-text", - dangerouslySetInnerHTML: { - __html: text, - }, - }); - - const titleEl = createElement("p", { - class: "search-error-title", - dangerouslySetInnerHTML: { - __html: ` ${title}`, - }, - }); - const errorEl = createElement("div", { class: "search-error" }, [ - titleEl, - descEl, - ]); - return errorEl; -} - -function positionPanel(pos) { - const panelEl = window.document.querySelector( - "#quarto-search-results .aa-Panel" - ); - const inputEl = window.document.querySelector( - "#quarto-search .aa-Autocomplete" - ); - - if (panelEl && inputEl) { - panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; - if (pos === "start") { - panelEl.style.left = `${Math.round(inputEl.left)}px`; - } else { - panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; - } - } -} - -/* Highlighting */ -// highlighting functions -function highlightMatch(query, text) { - if (text) { - const start = text.toLowerCase().indexOf(query.toLowerCase()); - if (start !== -1) { - const startMark = ""; - const endMark = ""; - - const end = start + query.length; - text = - text.slice(0, start) + - startMark + - text.slice(start, end) + - endMark + - text.slice(end); - const startInfo = clipStart(text, start); - const endInfo = clipEnd( - text, - startInfo.position + startMark.length + endMark.length - ); - text = - startInfo.prefix + - text.slice(startInfo.position, endInfo.position) + - endInfo.suffix; - - return text; - } else { - return text; - } - } else { - return text; - } -} - -function clipStart(text, pos) { - const clipStart = pos - 50; - if (clipStart < 0) { - // This will just return the start of the string - return { - position: 0, - prefix: "", - }; - } else { - // We're clipping before the start of the string, walk backwards to the first space. - const spacePos = findSpace(text, pos, -1); - return { - position: spacePos.position, - prefix: "", - }; - } -} - -function clipEnd(text, pos) { - const clipEnd = pos + 200; - if (clipEnd > text.length) { - return { - position: text.length, - suffix: "", - }; - } else { - const spacePos = findSpace(text, clipEnd, 1); - return { - position: spacePos.position, - suffix: spacePos.clipped ? "…" : "", - }; - } -} - -function findSpace(text, start, step) { - let stepPos = start; - while (stepPos > -1 && stepPos < text.length) { - const char = text[stepPos]; - if (char === " " || char === "," || char === ":") { - return { - position: step === 1 ? stepPos : stepPos - step, - clipped: stepPos > 1 && stepPos < text.length, - }; - } - stepPos = stepPos + step; - } - - return { - position: stepPos - step, - clipped: false, - }; -} - -// removes highlighting as implemented by the mark tag -function clearHighlight(searchterm, el) { - const childNodes = el.childNodes; - for (let i = childNodes.length - 1; i >= 0; i--) { - const node = childNodes[i]; - if (node.nodeType === Node.ELEMENT_NODE) { - if ( - node.tagName === "MARK" && - node.innerText.toLowerCase() === searchterm.toLowerCase() - ) { - el.replaceChild(document.createTextNode(node.innerText), node); - } else { - clearHighlight(searchterm, node); - } - } - } -} - -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string -} - -// highlight matches -function highlight(term, el) { - const termRegex = new RegExp(term, "ig"); - const childNodes = el.childNodes; - - // walk back to front avoid mutating elements in front of us - for (let i = childNodes.length - 1; i >= 0; i--) { - const node = childNodes[i]; - - if (node.nodeType === Node.TEXT_NODE) { - // Search text nodes for text to highlight - const text = node.nodeValue; - - let startIndex = 0; - let matchIndex = text.search(termRegex); - if (matchIndex > -1) { - const markFragment = document.createDocumentFragment(); - while (matchIndex > -1) { - const prefix = text.slice(startIndex, matchIndex); - markFragment.appendChild(document.createTextNode(prefix)); - - const mark = document.createElement("mark"); - mark.appendChild( - document.createTextNode( - text.slice(matchIndex, matchIndex + term.length) - ) - ); - markFragment.appendChild(mark); - - startIndex = matchIndex + term.length; - matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); - if (matchIndex > -1) { - matchIndex = startIndex + matchIndex; - } - } - if (startIndex < text.length) { - markFragment.appendChild( - document.createTextNode(text.slice(startIndex, text.length)) - ); - } - - el.replaceChild(markFragment, node); - } - } else if (node.nodeType === Node.ELEMENT_NODE) { - // recurse through elements - highlight(term, node); - } - } -} - -/* Link Handling */ -// get the offset from this page for a given site root relative url -function offsetURL(url) { - var offset = getMeta("quarto:offset"); - return offset ? offset + url : url; -} - -// read a meta tag value -function getMeta(metaName) { - var metas = window.document.getElementsByTagName("meta"); - for (let i = 0; i < metas.length; i++) { - if (metas[i].getAttribute("name") === metaName) { - return metas[i].getAttribute("content"); - } - } - return ""; -} - -function algoliaSearch(query, limit, algoliaOptions) { - const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; - - const applicationId = algoliaOptions["application-id"]; - const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; - const indexName = algoliaOptions["index-name"]; - const indexFields = algoliaOptions["index-fields"]; - const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); - const searchParams = algoliaOptions["params"]; - const searchAnalytics = !!algoliaOptions["analytics-events"]; - - return getAlgoliaResults({ - searchClient, - queries: [ - { - indexName: indexName, - query, - params: { - hitsPerPage: limit, - clickAnalytics: searchAnalytics, - ...searchParams, - }, - }, - ], - transformResponse: (response) => { - if (!indexFields) { - return response.hits.map((hit) => { - return hit.map((item) => { - return { - ...item, - text: highlightMatch(query, item.text), - }; - }); - }); - } else { - const remappedHits = response.hits.map((hit) => { - return hit.map((item) => { - const newItem = { ...item }; - ["href", "section", "title", "text", "crumbs"].forEach( - (keyName) => { - const mappedName = indexFields[keyName]; - if ( - mappedName && - item[mappedName] !== undefined && - mappedName !== keyName - ) { - newItem[keyName] = item[mappedName]; - delete newItem[mappedName]; - } - } - ); - newItem.text = highlightMatch(query, newItem.text); - return newItem; - }); - }); - return remappedHits; - } - }, - }); -} - -let subSearchTerm = undefined; -let subSearchFuse = undefined; -const kFuseMaxWait = 125; - -async function fuseSearch(query, fuse, fuseOptions) { - let index = fuse; - // Fuse.js using the Bitap algorithm for text matching which runs in - // O(nm) time (no matter the structure of the text). In our case this - // means that long search terms mixed with large index gets very slow - // - // This injects a subIndex that will be used once the terms get long enough - // Usually making this subindex is cheap since there will typically be - // a subset of results matching the existing query - if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) { - // Use the existing subSearchFuse - index = subSearchFuse; - } else if (subSearchFuse !== undefined) { - // The term changed, discard the existing fuse - subSearchFuse = undefined; - subSearchTerm = undefined; - } - - // Search using the active fuse - const then = performance.now(); - const resultsRaw = await index.search(query, fuseOptions); - const now = performance.now(); - - const results = resultsRaw.map((result) => { - const addParam = (url, name, value) => { - const anchorParts = url.split("#"); - const baseUrl = anchorParts[0]; - const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; - anchorParts[0] = baseUrl + sep + name + "=" + value; - return anchorParts.join("#"); - }; - - return { - title: result.item.title, - section: result.item.section, - href: addParam(result.item.href, kQueryArg, query), - text: highlightMatch(query, result.item.text), - crumbs: result.item.crumbs, - }; - }); - - // If we don't have a subfuse and the query is long enough, go ahead - // and create a subfuse to use for subsequent queries - if ( - now - then > kFuseMaxWait && - subSearchFuse === undefined && - resultsRaw.length < fuseOptions.limit - ) { - subSearchTerm = query; - subSearchFuse = new window.Fuse([], kFuseIndexOptions); - resultsRaw.forEach((rr) => { - subSearchFuse.add(rr.item); - }); - } - return results; -} diff --git a/demo/demo/_site/styles.css b/demo/demo/_site/styles.css deleted file mode 100644 index 2ddf50c..0000000 --- a/demo/demo/_site/styles.css +++ /dev/null @@ -1 +0,0 @@ -/* css styles */ diff --git a/demo/demo/listing.qmd b/demo/demo/listing.qmd deleted file mode 100644 index 7373b15..0000000 --- a/demo/demo/listing.qmd +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: "Blog" -listing: - contents: posts - sort: "date desc" - type: default - categories: true - sort-ui: false - filter-ui: false -page-layout: full -title-block-banner: true ---- - - diff --git a/demo/demo/posts/_metadata.yml b/demo/demo/posts/_metadata.yml deleted file mode 100644 index 3e9dd01..0000000 --- a/demo/demo/posts/_metadata.yml +++ /dev/null @@ -1,8 +0,0 @@ -# options specified here will apply to all posts in this folder - -# freeze computational output -# (see https://quarto.org/docs/projects/code-execution.html#freeze) -freeze: true - -# Enable banner style title blocks -title-block-banner: true diff --git a/demo/demo/posts/posts/Blog.qmd b/demo/demo/posts/posts/Blog.qmd deleted file mode 100644 index 5448b44..0000000 --- a/demo/demo/posts/posts/Blog.qmd +++ /dev/null @@ -1,106 +0,0 @@ ---- -title: "Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data" -description: Apply imfp in a real world data-analysis project. -author: "Jenny Xu" -date: "2024-12-25" -categories: [Data Analysis] -image: "Pictures/pic.png" ---- - -## Introduction - -Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders? - -Our main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables. - -Ready for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in! - -## Data Preparation -​​To explore how economic growth connects with gender equality, we turned to the Imfp library to access a wealth of global economic data, ready to analyze through some straightforward API calls. Let’s break down our two main variables of interest. Here’s the lowdown on the 4 key data series we pulled together: - -![](Pictures/Table 1.png) - -### Dependent Variable: Percent Change in Gender Inequality Index (GII) -Our gender equality measure, the Gender Inequality Index (GII), is a composite score that reflects inequality in three key areas: reproductive health, empowerment, and labor market, ranging from 0 (equality) to 1 (inequality). We calculated the percent change in GII over time to make it easier to compare GII change across countries with different baseline levels of inequality. - -### Independent Variable: Percent Change in Real GDP per capita -Our economic growth measure is real GDP per capita, an indicator of a country’s economic output per person, adjusted for inflation. The higher the real GDP per capita, the wealthier and more prosperous a country is. Here is a peek at the formula: - -![](Pictures/Formula.png) - -## Data Visualization -### Scatterplot - -Nothing like a good scatterplot to help us understand the relationship between two numbers, right? For our project, the scatterplot lets us visually explore how Percent Change in Real GDP per capita relates to Percent Change in GII. Each dot on the plot represents a country-year combo. The gentle upward slope suggests that in years of low GDP growth, gender inequality tends to decrease a bit faster, while in years of high GDP growth, the inequality drops at a slightly slower rate. It’s a subtle clue, but slower-growing economies might be making slightly quicker strides in gender equality. - -![](Pictures/Scatterplot copy.png) -![](Pictures/Scatterplot title.png) - - -### Time Series Line plot - - -The time series line plot gives us the big picture of GDP and GII changes over time across a few select countries, including the United States, Japan, the United Kingdom, France, and Mexico. Mexico, for example, saw great GDP swings in 1995, yet its GII stayed relatively stable. Meanwhile, Japan and France saw some dramatic GII shifts. These fluctuations leave us wondering: what historical events sparked these changes? We may need to take a deeper look into each country’s unique timeline to find the answers. - -![](Pictures/Time.png) -![](Pictures/Time%20title.png) - -### Boxplot - - -Next, we have the boxplot to help us visualize the distribution. By sequencing countries in ascending order of median GDP change, we see an interesting trend: countries with higher median GDP growth also seem to show more variability in their GDP changes. The GII change, on the other hand, keeps a relatively stable median, hinting at a weaker association with GDP growth. - -![](Pictures/boxplot.png) -![](Pictures/box%20title.png) - -### Heatmap - - -Finally, our heatmap wraps it all up with a quick, intuitive look at correlations between GDP and GII across countries. In countries like Estonia (EE) and Ireland (IE), Australia (AT), South Africa (ZA), Thailand (TH), Slovenia (SI), we see positive correlations in warm colors, indicating that as GDP growth rates increase, GII growth rates tend to rise as well. For countries like Cyprus (CY), Romania (RO), and the United States (US), we see cooler colors, representing negative correlations: as GDP growth rate rises, GII growth rates decrease, suggesting an improvement in gender equality alongside economic growth. This color-coded comparison leaves us curious—could the distinction between developed and developing countries play a role in these relationships? It’s a question that might inspire future research directions! - -![](Pictures/heatmap.png) -![](Pictures/heatmap%20title.png) - -## Statistical Analysis -### Descriptive Statistics -Let’s start with a quick overview of our descriptive statistics: our dataset includes 915 data points. On average, GII change is -3.15%, indicating an overall reduction in gender inequality over time. Meanwhile, GDP change averages 2.35%, hinting at modest economic growth. A full breakdown of these values can be found in the table below for more detail. - -![](Pictures/Table%202.png) - -### Regression Analysis -To get a sense of any relationship between GDP change and GII change, we ran a simple linear regression. Here’s what we found: - -* P-value: With a p-value of 0.057, we fail to reject the null hypothesis. There is no statistically significant relationship. In other words, we don't find strong evidence of a link between GDP and GII changes. - -* R-squared: GDP change explains only 0.4% of the variation in GII change—a pretty minimal effect. - -* 95% Confidence Interval: We’re 95% confident the interval from -0.003 to 0.169 captures the true slope. The effect of GDP change on GII is uncertain as 0 is within this range. - -### Time Series Analysis -Time series analysis allows us to dig deeper into how GII and GDP changes play out over time—and potentially influence one another. Here’s the rundown: - -* Stationarity check: Both GII and GDP change series are stationary, meaning their means and variances are stable over time, setting us up for reliable forecasting. - -* VAR Model (Vector Autoregression): This multivariate model lets us explore how past values in both series relate to current values. Notably, past GII changes significantly impact current GII changes. - -* Model Performance: The VAR model has good fit, as shown by the forecast closely tracking actual values. Low MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) confirm that our model predicts GII and GDP change with minimal error. Check out the plots and table below! - -* Granger Causality Test: Curious whether GDP changes can predict changes in GII? The Granger causality test says no. With p-values above 0.05, we fail to find evidence that GDP changes drive GII changes. This suggests that while both variables evolve over time, they don’t seem to directly influence each other. - -![](Pictures/Table%203.png) -![](Pictures/Forecast.png) - -## Conclusion -In wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research. - -In terms of the Imfp library, we gained valuable experience working with real-world economic indicators like GDP and the GII. The library’s function in fetching databases, parameters, and datasets made it straightforward to access IMF’s data, allowing us to focus on analysis instead. The integration of other Python tools—such as seaborn, matplotlib, and numpy—not only streamlined our computational processes but also ensured consistency across the project. - -## Future Directions -First, we must consider what other factors might influence the relationship between GDP and GII change. The GII is a composite index, shaped by a myriad of social factors, including cultural norms, legal frameworks, and environmental shifts. Future studies could benefit from incorporating additional predictors into the analysis and exploring the interaction between economic growth and gender equality within specific country contexts. - -Second, there's potential to enhance the predictive power of our Vector Autoregression (VAR) time series model. While we established that GDP change does not cause GII change, our model performed well in forecasting trends for both variables independently. In practice, policymakers may want to forecast GII trends independently of GDP if they are implementing gender-focused policies. Future research could investigate time series modeling to further unravel the dynamics of GII and GDP changes. - -So, as we wrap up this chapter, let's keep our curiosity alive and our questions flowing. 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demo/demo/imfp_demo.qmd rename to docs/demo.qmd index e27d613..580e2ff 100644 --- a/demo/demo/imfp_demo.qmd +++ b/docs/demo.qmd @@ -1,5 +1,6 @@ --- -title: "imfp Demo Project" +title: "Economic Growth and Gender Equality: An Analysis Using IMF Data" +author: "Jenny Xu" --- This data analysis project aims to explore the relationship between economic growth and gender equality using `imfp`, which allows us to download data from IMF (International Monetary Fund). `imfp` can be integrated with other python tools to streamline the computational process. To demonstrate its functionality, the project experimented with a variety of visualization and analysis methods. @@ -192,7 +193,7 @@ for dataset in datasets: # view_dataframe_in_browser(params.get("IFS").get("indicator")) ``` -“freq” stands for Frequency, such as Annual, Monthly, or Quarterly. "ref_area" stands for Geogrpahical Area, such as US (United States), JP (Japan), and GB (United Kindom). “indicator" refers to the specific dataset in the database, including input code and description. For example, if we display all the indicators for IFS database, the GDP deflator dataset has an input code of "NGDP_D_SA_IX" with a full name description of Gross Domestic Product, Deflator, Seasonally Adjusted, Index. +"freq" stands for Frequency, such as Annual, Monthly, or Quarterly. "ref_area" stands for Geogrpahical Area, such as US (United States), JP (Japan), and GB (United Kindom). "indicator" refers to the specific dataset in the database, including input code and description. For example, if we display all the indicators for IFS database, the GDP deflator dataset has an input code of "NGDP_D_SA_IX" with a full name description of Gross Domestic Product, Deflator, Seasonally Adjusted, Index. We paired the database with the specific dataset indicator to read and store the csv file. @@ -255,7 +256,9 @@ We kept percents as decimals to make them easy to work with for calculation. Dif ```{python} # Calculate percent change for each ref_area -GII_data[f'pct_change'] = GII_data.sort_values(['ref_area', 'time_period']).groupby('ref_area')['obs_value'].pct_change() +# First, create a copy and reset the index to avoid duplicate index issues +GII_data_sorted = GII_data.sort_values(['ref_area', 'time_period']).reset_index(drop=True) +GII_data['pct_change'] = GII_data_sorted.groupby('ref_area')['obs_value'].pct_change() # Display the first few rows of the updated dataset GII_data.head() @@ -726,4 +729,27 @@ test_result = grangercausalitytests(train_data[['GII_change', 'GDP_change']], ma ## Conclusion In wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research. Check out the blog for full analysis! - +## About the Author + +

    diff --git a/demo/demo/About/Headshot.jpg b/docs/static/Headshot.jpg similarity index 100% rename from demo/demo/About/Headshot.jpg rename to docs/static/Headshot.jpg diff --git a/pyproject.toml b/pyproject.toml index a056636..411e5bb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "imfp" -version = "1.1.3" +version = "1.1.4" description = "Python package for downloading economic data from the International Monetary Fund JSON RESTful API endpoint." authors = ["Christopher C. Smith "] license = "MIT"

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diff --git a/demo/demo/_site/index.html b/demo/demo/_site/index.html deleted file mode 100644 index 0e0f963..0000000 --- a/demo/demo/_site/index.html +++ /dev/null @@ -1,669 +0,0 @@ - - - - - - - - - -blog – imfp Demo - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data

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    Data Analysis
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    Jenny Xu

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    December 25, 2024

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    Introduction

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    Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders?

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    Our main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables.

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    Ready for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!

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    Data Preparation

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    ​​To explore how economic growth connects with gender equality, we turned to the Imfp library to access a wealth of global economic data, ready to analyze through some straightforward API calls. Let’s break down our two main variables of interest. Here’s the lowdown on the 4 key data series we pulled together:

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    Dependent Variable: Percent Change in Gender Inequality Index (GII)

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    Our gender equality measure, the Gender Inequality Index (GII), is a composite score that reflects inequality in three key areas: reproductive health, empowerment, and labor market, ranging from 0 (equality) to 1 (inequality). We calculated the percent change in GII over time to make it easier to compare GII change across countries with different baseline levels of inequality.

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    Independent Variable: Percent Change in Real GDP per capita

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    Our economic growth measure is real GDP per capita, an indicator of a country’s economic output per person, adjusted for inflation. The higher the real GDP per capita, the wealthier and more prosperous a country is. Here is a peek at the formula:

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    Data Visualization

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    Scatterplot

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    Nothing like a good scatterplot to help us understand the relationship between two numbers, right? For our project, the scatterplot lets us visually explore how Percent Change in Real GDP per capita relates to Percent Change in GII. Each dot on the plot represents a country-year combo. The gentle upward slope suggests that in years of low GDP growth, gender inequality tends to decrease a bit faster, while in years of high GDP growth, the inequality drops at a slightly slower rate. It’s a subtle clue, but slower-growing economies might be making slightly quicker strides in gender equality.

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    Time Series Line plot

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    The time series line plot gives us the big picture of GDP and GII changes over time across a few select countries, including the United States, Japan, the United Kingdom, France, and Mexico. Mexico, for example, saw great GDP swings in 1995, yet its GII stayed relatively stable. Meanwhile, Japan and France saw some dramatic GII shifts. These fluctuations leave us wondering: what historical events sparked these changes? We may need to take a deeper look into each country’s unique timeline to find the answers.

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    Boxplot

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    Next, we have the boxplot to help us visualize the distribution. By sequencing countries in ascending order of median GDP change, we see an interesting trend: countries with higher median GDP growth also seem to show more variability in their GDP changes. The GII change, on the other hand, keeps a relatively stable median, hinting at a weaker association with GDP growth.

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    Heatmap

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    Finally, our heatmap wraps it all up with a quick, intuitive look at correlations between GDP and GII across countries. In countries like Estonia (EE) and Ireland (IE), Australia (AT), South Africa (ZA), Thailand (TH), Slovenia (SI), we see positive correlations in warm colors, indicating that as GDP growth rates increase, GII growth rates tend to rise as well. For countries like Cyprus (CY), Romania (RO), and the United States (US), we see cooler colors, representing negative correlations: as GDP growth rate rises, GII growth rates decrease, suggesting an improvement in gender equality alongside economic growth. This color-coded comparison leaves us curious—could the distinction between developed and developing countries play a role in these relationships? It’s a question that might inspire future research directions!

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    Statistical Analysis

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    Descriptive Statistics

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    Let’s start with a quick overview of our descriptive statistics: our dataset includes 915 data points. On average, GII change is -3.15%, indicating an overall reduction in gender inequality over time. Meanwhile, GDP change averages 2.35%, hinting at modest economic growth. A full breakdown of these values can be found in the table below for more detail.

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    Regression Analysis

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    To get a sense of any relationship between GDP change and GII change, we ran a simple linear regression. Here’s what we found:

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    • P-value: With a p-value of 0.057, we fail to reject the null hypothesis. There is no statistically significant relationship. In other words, we don’t find strong evidence of a link between GDP and GII changes.

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    • R-squared: GDP change explains only 0.4% of the variation in GII change—a pretty minimal effect.

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    • 95% Confidence Interval: We’re 95% confident the interval from -0.003 to 0.169 captures the true slope. The effect of GDP change on GII is uncertain as 0 is within this range.

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    Time Series Analysis

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    Time series analysis allows us to dig deeper into how GII and GDP changes play out over time—and potentially influence one another. Here’s the rundown:

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    • Stationarity check: Both GII and GDP change series are stationary, meaning their means and variances are stable over time, setting us up for reliable forecasting.

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    • VAR Model (Vector Autoregression): This multivariate model lets us explore how past values in both series relate to current values. Notably, past GII changes significantly impact current GII changes.

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    • Model Performance: The VAR model has good fit, as shown by the forecast closely tracking actual values. Low MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) confirm that our model predicts GII and GDP change with minimal error. Check out the plots and table below!

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    • Granger Causality Test: Curious whether GDP changes can predict changes in GII? The Granger causality test says no. With p-values above 0.05, we fail to find evidence that GDP changes drive GII changes. This suggests that while both variables evolve over time, they don’t seem to directly influence each other.

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    Conclusion

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    In wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research.

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    In terms of the Imfp library, we gained valuable experience working with real-world economic indicators like GDP and the GII. The library’s function in fetching databases, parameters, and datasets made it straightforward to access IMF’s data, allowing us to focus on analysis instead. The integration of other Python tools—such as seaborn, matplotlib, and numpy—not only streamlined our computational processes but also ensured consistency across the project.

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    Future Directions

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    First, we must consider what other factors might influence the relationship between GDP and GII change. The GII is a composite index, shaped by a myriad of social factors, including cultural norms, legal frameworks, and environmental shifts. Future studies could benefit from incorporating additional predictors into the analysis and exploring the interaction between economic growth and gender equality within specific country contexts.

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    Second, there’s potential to enhance the predictive power of our Vector Autoregression (VAR) time series model. While we established that GDP change does not cause GII change, our model performed well in forecasting trends for both variables independently. In practice, policymakers may want to forecast GII trends independently of GDP if they are implementing gender-focused policies. Future research could investigate time series modeling to further unravel the dynamics of GII and GDP changes.

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    So, as we wrap up this chapter, let’s keep our curiosity alive and our questions flowing. After all, every end is just a new beginning in the quest for knowledge!

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literal 0 HcmV?d00001 diff --git a/demo/demo/_site/index.html b/demo/demo/_site/index.html new file mode 100644 index 0000000..0e0f963 --- /dev/null +++ b/demo/demo/_site/index.html @@ -0,0 +1,669 @@ + + + + + + + + + +blog – imfp Demo + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    Blog

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    Exploring the Relationship Between Economic Growth and Gender Equality Using IMF Data

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    + Apply imfp in a real world data-analysis project. +
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    Data Analysis
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    + + +
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    Author
    +
    +

    Jenny Xu

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    + +
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    Published
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    December 25, 2024

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    Introduction

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    Why should we care about the connection between economic growth and gender equality? It’s a big question! As we explore today’s global challenges, we can see how economies and societies are woven together—economic growth isn’t just about numbers; it impacts lives, opportunities, and well-being. One aspect we’re especially curious about is gender equality: Does a stronger economy mean more equal opportunities for all genders?

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    Our main research question is: How might changes in Real GDP per capita predict changes in the Gender Inequality Index (GII) across different countries? Our hypothesis is that economic growth should support progress in gender equality: as GDP per capita increases, the GII decreases, signaling improvements in equality. We decided to dig into the data to find out. Using Promptly Technologies’s open-source Python library Imfp, our aim is to see if there’s a real, measurable connection between the two variables.

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    Ready for some insights? Spoiler alert: our results didn’t exactly match our hypothesis, but they did open up fascinating questions about what really drives gender equality beyond just economic growth. Let’s dive in!

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    Data Preparation

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    ​​To explore how economic growth connects with gender equality, we turned to the Imfp library to access a wealth of global economic data, ready to analyze through some straightforward API calls. Let’s break down our two main variables of interest. Here’s the lowdown on the 4 key data series we pulled together:

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    Dependent Variable: Percent Change in Gender Inequality Index (GII)

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    Our gender equality measure, the Gender Inequality Index (GII), is a composite score that reflects inequality in three key areas: reproductive health, empowerment, and labor market, ranging from 0 (equality) to 1 (inequality). We calculated the percent change in GII over time to make it easier to compare GII change across countries with different baseline levels of inequality.

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    Independent Variable: Percent Change in Real GDP per capita

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    Our economic growth measure is real GDP per capita, an indicator of a country’s economic output per person, adjusted for inflation. The higher the real GDP per capita, the wealthier and more prosperous a country is. Here is a peek at the formula:

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    Data Visualization

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    Scatterplot

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    Nothing like a good scatterplot to help us understand the relationship between two numbers, right? For our project, the scatterplot lets us visually explore how Percent Change in Real GDP per capita relates to Percent Change in GII. Each dot on the plot represents a country-year combo. The gentle upward slope suggests that in years of low GDP growth, gender inequality tends to decrease a bit faster, while in years of high GDP growth, the inequality drops at a slightly slower rate. It’s a subtle clue, but slower-growing economies might be making slightly quicker strides in gender equality.

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    Time Series Line plot

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    The time series line plot gives us the big picture of GDP and GII changes over time across a few select countries, including the United States, Japan, the United Kingdom, France, and Mexico. Mexico, for example, saw great GDP swings in 1995, yet its GII stayed relatively stable. Meanwhile, Japan and France saw some dramatic GII shifts. These fluctuations leave us wondering: what historical events sparked these changes? We may need to take a deeper look into each country’s unique timeline to find the answers.

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    Boxplot

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    Next, we have the boxplot to help us visualize the distribution. By sequencing countries in ascending order of median GDP change, we see an interesting trend: countries with higher median GDP growth also seem to show more variability in their GDP changes. The GII change, on the other hand, keeps a relatively stable median, hinting at a weaker association with GDP growth.

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    Heatmap

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    Finally, our heatmap wraps it all up with a quick, intuitive look at correlations between GDP and GII across countries. In countries like Estonia (EE) and Ireland (IE), Australia (AT), South Africa (ZA), Thailand (TH), Slovenia (SI), we see positive correlations in warm colors, indicating that as GDP growth rates increase, GII growth rates tend to rise as well. For countries like Cyprus (CY), Romania (RO), and the United States (US), we see cooler colors, representing negative correlations: as GDP growth rate rises, GII growth rates decrease, suggesting an improvement in gender equality alongside economic growth. This color-coded comparison leaves us curious—could the distinction between developed and developing countries play a role in these relationships? It’s a question that might inspire future research directions!

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    Statistical Analysis

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    Descriptive Statistics

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    Let’s start with a quick overview of our descriptive statistics: our dataset includes 915 data points. On average, GII change is -3.15%, indicating an overall reduction in gender inequality over time. Meanwhile, GDP change averages 2.35%, hinting at modest economic growth. A full breakdown of these values can be found in the table below for more detail.

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    Regression Analysis

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    To get a sense of any relationship between GDP change and GII change, we ran a simple linear regression. Here’s what we found:

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    • P-value: With a p-value of 0.057, we fail to reject the null hypothesis. There is no statistically significant relationship. In other words, we don’t find strong evidence of a link between GDP and GII changes.

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    • R-squared: GDP change explains only 0.4% of the variation in GII change—a pretty minimal effect.

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    • 95% Confidence Interval: We’re 95% confident the interval from -0.003 to 0.169 captures the true slope. The effect of GDP change on GII is uncertain as 0 is within this range.

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    Time Series Analysis

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    Time series analysis allows us to dig deeper into how GII and GDP changes play out over time—and potentially influence one another. Here’s the rundown:

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    • Stationarity check: Both GII and GDP change series are stationary, meaning their means and variances are stable over time, setting us up for reliable forecasting.

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    • VAR Model (Vector Autoregression): This multivariate model lets us explore how past values in both series relate to current values. Notably, past GII changes significantly impact current GII changes.

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    • Model Performance: The VAR model has good fit, as shown by the forecast closely tracking actual values. Low MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) confirm that our model predicts GII and GDP change with minimal error. Check out the plots and table below!

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    • Granger Causality Test: Curious whether GDP changes can predict changes in GII? The Granger causality test says no. With p-values above 0.05, we fail to find evidence that GDP changes drive GII changes. This suggests that while both variables evolve over time, they don’t seem to directly influence each other.

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    Conclusion

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    In wrapping up our analysis, we found no evidence to support a significant relationship between the Change in Real GDP per capita and the Change in the Gender Inequality Index (GII). This suggests that economic growth may not have a direct impact on gender equality. However, our findings open the door to questions for future research.

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    In terms of the Imfp library, we gained valuable experience working with real-world economic indicators like GDP and the GII. The library’s function in fetching databases, parameters, and datasets made it straightforward to access IMF’s data, allowing us to focus on analysis instead. The integration of other Python tools—such as seaborn, matplotlib, and numpy—not only streamlined our computational processes but also ensured consistency across the project.

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    Future Directions

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    First, we must consider what other factors might influence the relationship between GDP and GII change. The GII is a composite index, shaped by a myriad of social factors, including cultural norms, legal frameworks, and environmental shifts. Future studies could benefit from incorporating additional predictors into the analysis and exploring the interaction between economic growth and gender equality within specific country contexts.

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    Second, there’s potential to enhance the predictive power of our Vector Autoregression (VAR) time series model. While we established that GDP change does not cause GII change, our model performed well in forecasting trends for both variables independently. In practice, policymakers may want to forecast GII trends independently of GDP if they are implementing gender-focused policies. Future research could investigate time series modeling to further unravel the dynamics of GII and GDP changes.

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    So, as we wrap up this chapter, let’s keep our curiosity alive and our questions flowing. After all, every end is just a new beginning in the quest for knowledge!

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