{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Adaptive histogram\n",
        "\nThis type of histogram automatically adapts bins when new values are added. Note that only fixed-width continuous binning scheme is currently supported."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Necessary import evil\n",
        "import physt\n",
        "from physt import h1, h2, histogramdd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create an empty histogram\n",
        "h = h1(None, \"fixed_width\", bin_width=10, name=\"People height\", axis_name=\"cm\", adaptive=True)\n",
        "h"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": [
              "Histogram1D(bins=(0,), total=0.0, dtype=None)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Adding single values"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Add a first value\n",
        "h.fill(157)\n",
        "h.plot()\n",
        "h"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": [
              "Histogram1D(bins=(1,), total=1.0, dtype=None)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
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            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Add a second value\n",
        "h.fill(173)\n",
        "h.plot();"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb8ea35f358>"
            ],
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            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "scrolled": true
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Add a few more values, including weights\n",
        "h.fill(173, 2)\n",
        "h.fill(186, 5)\n",
        "h.fill(188, 3)\n",
        "h.fill(193, 1)\n",
        "h.plot(errors=True, show_stats=True);"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb8e4985a58>"
            ],
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              "YGeHAAAAAElFTkSuQmCC\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 5,
      "metadata": {
        "collapsed": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Adding multiple values at once"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "ha = h1(None, \"fixed_width\", bin_width=10, adaptive=True)\n",
        "ha.plot(show_stats=True);"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb8e48e2e80>"
            ],
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            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Beginning\n",
        "ha.fill_n([10, 11, 34])\n",
        "ha.plot();"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb8e4875668>"
            ],
            "image/png": [
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              "AElFTkSuQmCC\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 7,
      "metadata": {
        "collapsed": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Add a distant value\n",
        "ha.fill_n([234], weights=[10])\n",
        "ha.plot(show_stats=True);"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb8e47c3668>"
            ],
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              "BBQAIEgEFAAgSAQUACBI/x/OgrGKV0p1tAAAAABJRU5ErkJggg==\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 8,
      "metadata": {
        "collapsed": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's create a huge dataset\n",
        "values = np.random.normal(130, 20, 100000)"
      ],
      "outputs": [],
      "execution_count": 9,
      "metadata": {
        "collapsed": true
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "# Add lots of values (no loop in Python)\n",
        "hn = h1(None, \"fixed_width\", bin_width=10, adaptive=True)\n",
        "hn.fill_n(values)\n",
        "# ha.plot()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 12.8 ms, sys: 3.62 ms, total: 16.4 ms\n",
            "Wall time: 15.3 ms\n"
          ]
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "# Comparison with Python loop\n",
        "hp = h1(None, \"fixed_width\", bin_width=10, adaptive=True)\n",
        "for value in values:\n",
        "    hp.fill(value)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 4.23 s, sys: 50.1 ms, total: 4.28 s\n",
            "Wall time: 4.29 s\n"
          ]
        }
      ],
      "execution_count": 11,
      "metadata": {
        "collapsed": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Hopefully equal results\n",
        "print(\"Equal?\", hp == hn)\n",
        "hp.plot(show_stats=True);"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Equal? True\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb8e48560f0>"
            ],
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            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 12,
      "metadata": {
        "collapsed": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Adding two adaptive histograms together"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "ha1 = h1(None, \"fixed_width\", 5, adaptive=True)\n",
        "ha1.fill_n(np.random.normal(100, 10, 1000))\n",
        "\n",
        "ha2 = h1(None, \"fixed_width\", 5, adaptive=True)\n",
        "ha2.fill_n(np.random.normal(70, 10, 500))\n",
        "\n",
        "ha = ha1 + ha2\n",
        "\n",
        "fig, ax= plt.subplots()\n",
        "\n",
        "ha1.plot(alpha=0.1, ax=ax, label=\"1\", color=\"red\")\n",
        "ha2.plot(alpha=0.1, ax=ax, label=\"2\")\n",
        "\n",
        "ha.plot(\"scatter\", label=\"sum\", ax=ax, errors=True)\n",
        "\nax.legend(loc=2);   # TODO? Why don't we show the sum???"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb8e47eff60>"
            ],
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          "metadata": {}
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      ],
      "execution_count": 13,
      "metadata": {
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      }
    }
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