charts.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Blank Slate UBI chart generation\n",
    "\n",
    "This notebook creates the charts for the report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
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         "anchor": "y",
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   ],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "from policyengine_core.charts import format_fig\n",
    "import plotly.express as px\n",
    "\n",
    "df = pd.read_csv('ubi_amounts.csv')\n",
    "\n",
    "with open(\"json_data.json\", \"r\") as file:\n",
    "    json_data = json.load(file)\n",
    "    \n",
    "\n",
    "# Melt the DataFrame to make it suitable for Plotly\n",
    "melted_df = df.sort_values(\"flat_tax_rate\").melt(id_vars=['flat_tax_rate'], \n",
    "                    value_vars=['ubi_0_5', 'ubi_6_17', 'ubi_18_64', 'ubi_65_plus', 'ubi_disability'],\n",
    "                    var_name='UBI Category', value_name='Amount')\n",
    "\n",
    "melted_df['UBI Category'] = melted_df['UBI Category'].map({\n",
    "    \"ubi_0_5\": \"0-5\",\n",
    "    \"ubi_6_17\": \"6-17\",\n",
    "    \"ubi_18_64\": \"18-64\",\n",
    "    \"ubi_65_plus\": \"65+\",\n",
    "    \"ubi_disability\": \"Disability\"\n",
    "})\n",
    "\n",
    "# Create the plot\n",
    "fig = px.line(melted_df, x='flat_tax_rate', y='Amount', color='UBI Category',\n",
    "              labels={'flat_tax_rate': 'Flat Tax Rate', 'Amount': 'UBI Amount'},\n",
    "              title='UBI Amounts by Flat Tax Rate', color_discrete_sequence=px.colors.qualitative.D3)\n",
    "\n",
    "# Show the plot\n",
    "format_fig(fig).update_layout(\n",
    "    legend_title=\"UBI category\",\n",
    "    yaxis_tickformat = \"$,.0f\",\n",
    "    yaxis_title=\"Yearly amount\",\n",
    "    xaxis_title=\"Flat tax rate\",\n",
    "    xaxis_tickformat=\".0%\",\n",
    "    title=\"UBI amounts by flat tax rate\"\n",
    ")"
   ]
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         "anchor": "y",
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     "metadata": {},
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   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def create_dataframes_from_json(json_data, df):\n",
    "    dataframes = {}\n",
    "    for api_id, metrics in json_data.items():\n",
    "        for metric_name, metric_data in metrics.items():\n",
    "            if metric_name not in dataframes:\n",
    "                dataframes[metric_name] = pd.DataFrame()\n",
    "\n",
    "            # Flatten the metric data and add API ID\n",
    "            flat_data = pd.DataFrame([{**{\"rounded_ubi_api_id\": api_id}, **metric_data}])\n",
    "            dataframes[metric_name] = pd.concat([dataframes[metric_name], flat_data], ignore_index=True)\n",
    "\n",
    "    # Ensure data type consistency for merging\n",
    "    df['rounded_ubi_api_id'] = df['rounded_ubi_api_id'].astype(str)\n",
    "\n",
    "    # Merge each dataframe with the df dataframe to add the flat_tax_rate\n",
    "    for metric_name, metric_df in dataframes.items():\n",
    "        metric_df['rounded_ubi_api_id'] = metric_df['rounded_ubi_api_id'].astype(str)\n",
    "        merged_df = metric_df.merge(df[['rounded_ubi_api_id', 'flat_tax_rate']], on='rounded_ubi_api_id', how='left')\n",
    "        # Sort the DataFrame by flat_tax_rate\n",
    "        dataframes[metric_name] = merged_df.sort_values(by='flat_tax_rate')\n",
    "\n",
    "    return dataframes\n",
    "\n",
    "# Assuming 'json_data' is your JSON data and 'df' is your existing DataFrame\n",
    "dfs = create_dataframes_from_json(json_data, df)\n",
    "\n",
    "# Accessing and printing a specific dataframe, for example, 'budget'\n",
    "budget_df = dfs.get('budget', None)\n",
    "\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "\n",
    "def calculate_percentage_change(dataframe, column_name):\n",
    "    data = dataframe[column_name].apply(pd.Series)\n",
    "    data['% change'] = ((data['reform'] - data['baseline']) / data['baseline'])\n",
    "    data['flat_tax_rate'] = dataframe['flat_tax_rate']\n",
    "    return data[['flat_tax_rate', '% change']].rename(columns={'% change': f'% change {column_name}'})\n",
    "\n",
    "# Assuming dfs['inequality'] is your DataFrame\n",
    "gini_change = calculate_percentage_change(dfs['inequality'], 'gini')\n",
    "top_10_pct_share_change = calculate_percentage_change(dfs['inequality'], 'top_10_pct_share')\n",
    "top_1_pct_share_change = calculate_percentage_change(dfs['inequality'], 'top_1_pct_share')\n",
    "\n",
    "labour_supply_response = dfs[\"labour_supply_response\"][[\"total_change\", \"flat_tax_rate\"]].rename(columns=dict(total_change=\"labor_supply\"))\n",
    "labour_supply_response.labor_supply /= 10320e9\n",
    "# Combine the data\n",
    "combined_data = gini_change\n",
    "combined_data = combined_data.merge(top_10_pct_share_change, on='flat_tax_rate')\n",
    "combined_data = combined_data.merge(top_1_pct_share_change, on='flat_tax_rate')\n",
    "combined_data = combined_data.merge(labour_supply_response, on='flat_tax_rate')\n",
    "\n",
    "combined_data.rename(columns={\n",
    "    \"flat_tax_rate\": \"Flat tax rate\",\n",
    "    \"labor_supply\": \"Labor supply\",\n",
    "    \"% change gini\": \"Gini index\",\n",
    "    \"% change top_10_pct_share\": \"Top 10% share\",\n",
    "    \"% change top_1_pct_share\": \"Top 1% share\",\n",
    "}, inplace=True)\n",
    "\n",
    "fig = px.line(combined_data, x='Flat tax rate', y=['Gini index', 'Top 10% share', 'Top 1% share'],\n",
    "              labels={'value': '% Change', 'variable': 'Metric'},\n",
    "              title='Change in inequality metrics by flat tax rate',\n",
    "              color_discrete_sequence=px.colors.qualitative.D3)\n",
    "fig = format_fig(fig).update_layout(\n",
    "    yaxis_tickformat = \"+.0%\",\n",
    "    yaxis_title=\"Percentage change\",\n",
    "    xaxis_title=\"Flat tax rate\",\n",
    "    xaxis_tickformat=\".0%\",\n",
    "    title=\"Change in inequality metrics by flat tax rate\"\n",
    ")\n",
    "\n",
    "# Add dashed line at y=0\n",
    "\n",
    "from policyengine_core.charts import *\n",
    "\n",
    "fig.add_shape(\n",
    "    type=\"line\",\n",
    "    x0=0,\n",
    "    y0=0,\n",
    "    x1=1,\n",
    "    y1=0,\n",
    "    line=dict(\n",
    "        color=DARK_GRAY,\n",
    "        width=1,\n",
    "        dash=\"dash\",\n",
    "    ),\n",
    "    xref=\"paper\",\n",
    "    yref=\"y\",\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
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    "def calculate_percentage_change_poverty(dataframe, column_name):\n",
    "    data = dataframe[column_name].apply(pd.Series)\n",
    "    for category in ['adult', 'all', 'child', 'senior']:\n",
    "        data[f'% change {category}'] = ((data[category].apply(lambda x: x['reform']) - \n",
    "                                         data[category].apply(lambda x: x['baseline'])) / \n",
    "                                         data[category].apply(lambda x: x['baseline']))\n",
    "    data['flat_tax_rate'] = dataframe['flat_tax_rate']\n",
    "    return data[['flat_tax_rate', f'% change adult', f'% change all', f'% change child', f'% change senior']]\n",
    "\n",
    "# Assuming dfs['poverty'] is your DataFrame\n",
    "poverty_change = calculate_percentage_change_poverty(dfs['poverty'], 'poverty')\n",
    "\n",
    "poverty_change.rename(columns={\n",
    "    \"flat_tax_rate\": \"Flat tax rate\",\n",
    "    \"% change all\": \"Poverty\",\n",
    "    \"% change adult\": \"Adult poverty\",\n",
    "    \"% change child\": \"Child poverty\",\n",
    "    \"% change senior\": \"Senior poverty\",\n",
    "}, inplace=True)\n",
    "\n",
    "\n",
    "fig = px.line(poverty_change, x='Flat tax rate', y=['Poverty', 'Adult poverty', 'Child poverty', 'Senior poverty'],\n",
    "              labels={'value': '% Change', 'variable': 'Category'},\n",
    "              title='Change in poverty metrics by flat tax rate',\n",
    "                color_discrete_sequence=px.colors.qualitative.D3)\n",
    "fig = format_fig(fig).update_layout(\n",
    "    yaxis_tickformat = \"+.0%\",\n",
    "    yaxis_title=\"Percentage change\",\n",
    "    xaxis_title=\"Flat tax rate\",\n",
    "    xaxis_tickformat=\".0%\",\n",
    "    title=\"Change in poverty metrics by flat tax rate\"\n",
    ")\n",
    "\n",
    "# Add dashed line at y=0\n",
    "\n",
    "fig.add_shape(\n",
    "    type=\"line\",\n",
    "    x0=0,\n",
    "    y0=0,\n",
    "    x1=1,\n",
    "    y1=0,\n",
    "    line=dict(\n",
    "        color=DARK_GRAY,\n",
    "        width=1,\n",
    "        dash=\"dash\",\n",
    "    ),\n",
    "    xref=\"paper\",\n",
    "    yref=\"y\",\n",
    ")\n"
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   "cell_type": "code",
   "execution_count": 34,
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    "deep_poverty_change = calculate_percentage_change_poverty(dfs['poverty'], 'deep_poverty')\n",
    "\n",
    "\n",
    "deep_poverty_change.rename(columns={\n",
    "    \"flat_tax_rate\": \"Flat tax rate\",\n",
    "    \"% change all\": \"Deep poverty\",\n",
    "    \"% change adult\": \"Adult deep poverty\",\n",
    "    \"% change child\": \"Child deep poverty\",\n",
    "    \"% change senior\": \"Senior deep poverty\",\n",
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    "fig = px.line(deep_poverty_change, x='Flat tax rate', y=['Deep poverty', 'Adult deep poverty', 'Child deep poverty', 'Senior deep poverty'],\n",
    "                labels={'value': '% Change', 'variable': 'Category'},\n",
    "                title='Change in deep poverty metrics by flat tax rate',\n",
    "                    color_discrete_sequence=px.colors.qualitative.D3)\n",
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    "fig = format_fig(fig).update_layout(\n",
    "    yaxis_tickformat = \"+.0%\",\n",
    "    yaxis_title=\"Percentage change\",\n",
    "    xaxis_title=\"Flat tax rate\",\n",
    "    xaxis_tickformat=\".0%\",\n",
    "    title=\"Change in deep poverty metrics by flat tax rate\"\n",
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    "fig.add_shape(\n",
    "    type=\"line\",\n",
    "    x0=0,\n",
    "    y0=0,\n",
    "    x1=1,\n",
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    "    line=dict(\n",
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    "        width=1,\n",
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    "    ),\n",
    "    xref=\"paper\",\n",
    "    yref=\"y\",\n",
    ")\n"
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         {
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         {
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         },
         {
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         {
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         },
         {
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         {
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         {
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         {
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        },
        "width": 1200,
        "xaxis": {
         "dtick": 1,
         "gridcolor": "rgb(0, 0, 0)",
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        "yaxis": {
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          "Top 10% income share",
          "Top 1% income share",
          "Adult poverty rate",
          "Poverty rate",
          "Child poverty rate",
          "Senior poverty rate"
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   "source": [
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "\n",
    "# Define a function to determine increase (1) or decrease (-1)\n",
    "def increase_or_decrease(value):\n",
    "    return 1 if value > 0 else -1\n",
    "\n",
    "labor_supply_change = combined_data[[\"Flat tax rate\", \"Labor supply\"]]\n",
    "\n",
    "gini_change.columns = [\"Flat tax rate\", \"Gini index\"]\n",
    "top_10_pct_share_change.columns = [\"Flat tax rate\", \"Top 10% share\"]\n",
    "top_1_pct_share_change.columns = [\"Flat tax rate\", \"Top 1% share\"]\n",
    "\n",
    "# Combine the percentage change data from poverty, deep poverty, and inequality\n",
    "# Assuming dfs['poverty'] and other DataFrames are already defined\n",
    "combined_change = dfs['poverty'][['flat_tax_rate']].copy().rename(columns={'flat_tax_rate': 'Flat tax rate'})\n",
    "combined_change_categorical = combined_change.copy()\n",
    "\n",
    "# Assume you have other dataframes like gini_change, top_10_pct_share_change, etc.\n",
    "for dataframe in [labor_supply_change, gini_change, top_10_pct_share_change, top_1_pct_share_change, poverty_change]:\n",
    "    dataframe.sort_values('Flat tax rate', inplace=True)\n",
    "    for col in dataframe.columns:\n",
    "        if col != 'Flat tax rate':\n",
    "            if col == \"Labor supply\":\n",
    "                combined_change_categorical[col] = dataframe[col].apply(lambda x: -1 if x > 0 else 1)\n",
    "            else:\n",
    "                combined_change_categorical[col] = dataframe[col].apply(increase_or_decrease)\n",
    "            combined_change[col] = dataframe[col]\n",
    "\n",
    "import pandas as pd\n",
    "import plotly.figure_factory as ff\n",
    "\n",
    "# Assume combined_change is a DataFrame with the given structure\n",
    "# Mapping the percentage change to colors\n",
    "combined_change_mapped = combined_change_categorical.copy()\n",
    "\n",
    "import pandas as pd\n",
    "import plotly.figure_factory as ff\n",
    "import numpy as np\n",
    "\n",
    "# Rest of your code where you define combined_change and combined_change_mapped...\n",
    "\n",
    "# Preparing the data for the heatmap\n",
    "heatmap_data = combined_change_mapped.set_index('Flat tax rate').T\n",
    "\n",
    "# The x and y coordinates for the heatmap should be lists or arrays\n",
    "x_values = combined_change_mapped['Flat tax rate'].tolist()\n",
    "y_values = heatmap_data.index.tolist()\n",
    "\n",
    "# The z values and annotations must be 2D arrays that match the length of x and y\n",
    "#heatmap_data = heatmap_data.replace({'Increase': \"+\", 'Decrease': \"-\"})\n",
    "z_values = heatmap_data.values.T  # Transposing to match the dimensions\n",
    "annotations = combined_change.set_index('Flat tax rate').T.values.T  # This should be the same shape as z_values\n",
    "\n",
    "heatmap_annotations = pd.DataFrame(annotations, columns=y_values, index=x_values)\n",
    "heatmap_annotations = heatmap_annotations.apply(lambda x: x.apply(lambda y: f\"{y:+.0%}\"))\n",
    "# Assume combined_change is a DataFrame with the given structure\n",
    "# Mapping the percentage change to colors\n",
    "\n",
    "from policyengine_core.charts import *\n",
    "y_values = [\n",
    "    \"Labor supply\",\n",
    "    \"Gini index\",\n",
    "    \"Top 10% income share\",\n",
    "    \"Top 1% income share\",\n",
    "    \"Adult poverty rate\",\n",
    "    \"Poverty rate\",\n",
    "    \"Child poverty rate\",\n",
    "    \"Senior poverty rate\",\n",
    "]\n",
    "\n",
    "# Creating the heatmap using Figure Factory to properly display categorical data\n",
    "fig = ff.create_annotated_heatmap(\n",
    "    z=z_values.T,  # Transposing to match the dimensions\n",
    "    x=x_values,\n",
    "    y=y_values,\n",
    "    annotation_text=heatmap_annotations.values.T,\n",
    "    colorscale=[BLUE, DARK_GRAY],\n",
    "    showscale=False\n",
    ")\n",
    "\n",
    "# Update the layout to remove the color axis scale (legend)\n",
    "format_fig(fig).update_layout(\n",
    "    title='Change in metric under optimal UBI by flat tax rate',\n",
    "    xaxis=dict(title='Flat tax rate', side='top', tickmode='array', tickvals=x_values, ticktext=[f\"{x:.0%}\" for x in x_values]),\n",
    "    yaxis=dict(title='Metric', tickmode='array', tickvals=list(range(len(y_values))), ticktext=y_values),\n",
    "    margin=dict(l=150, t=150),  # Adjust margins to fit y-axis labels\n",
    "    height=600,\n",
    "    width=1200\n",
    ")\n",
    "\n",
    "# Show the figure\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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