{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Stackplots and streamgraphs\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Stackplots\n\nStackplots draw multiple datasets as vertically stacked areas. This is\nuseful when the individual data values and additionally their cumulative\nvalue are of interest.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\nimport numpy as np\n\nimport matplotlib.ticker as mticker\n\n# data from United Nations World Population Prospects (Revision 2019)\n# https://population.un.org/wpp/, license: CC BY 3.0 IGO\nyear = [1950, 1960, 1970, 1980, 1990, 2000, 2010, 2018]\npopulation_by_continent = {\n    'Africa': [.228, .284, .365, .477, .631, .814, 1.044, 1.275],\n    'the Americas': [.340, .425, .519, .619, .727, .840, .943, 1.006],\n    'Asia': [1.394, 1.686, 2.120, 2.625, 3.202, 3.714, 4.169, 4.560],\n    'Europe': [.220, .253, .276, .295, .310, .303, .294, .293],\n    'Oceania': [.012, .015, .019, .022, .026, .031, .036, .039],\n}\n\nfig, ax = plt.subplots()\nax.stackplot(year, population_by_continent.values(),\n             labels=population_by_continent.keys(), alpha=0.8)\nax.legend(loc='upper left', reverse=True)\nax.set_title('World population')\nax.set_xlabel('Year')\nax.set_ylabel('Number of people (billions)')\n# add tick at every 200 million people\nax.yaxis.set_minor_locator(mticker.MultipleLocator(.2))\n\nplt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Streamgraphs\n\nUsing the *baseline* parameter, you can turn an ordinary stacked area plot\nwith baseline 0 into a stream graph.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Fixing random state for reproducibility\nnp.random.seed(19680801)\n\n\ndef gaussian_mixture(x, n=5):\n    \"\"\"Return a random mixture of *n* Gaussians, evaluated at positions *x*.\"\"\"\n    def add_random_gaussian(a):\n        amplitude = 1 / (.1 + np.random.random())\n        dx = x[-1] - x[0]\n        x0 = (2 * np.random.random() - .5) * dx\n        z = 10 / (.1 + np.random.random()) / dx\n        a += amplitude * np.exp(-(z * (x - x0))**2)\n    a = np.zeros_like(x)\n    for j in range(n):\n        add_random_gaussian(a)\n    return a\n\n\nx = np.linspace(0, 100, 101)\nys = [gaussian_mixture(x) for _ in range(3)]\n\nfig, ax = plt.subplots()\nax.stackplot(x, ys, baseline='wiggle')\nplt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        ".. tags::\n\n   plot-type: stackplot\n   level: intermediate\n\n"
      ]
    }
  ],
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