fill_histogram.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "37f409e4-7d48-4a13-97e0-a21288ef2a7a",
   "metadata": {},
   "source": [
    "# Histogram Example\n",
    "\n",
    "In coffea, histograms should use the `hist` package. This notebook walks through some basics of creating and filling a `hist` histogram, and also some basic plotting commands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b3d09f47-a732-4915-bb04-3feb504a4214",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from hist import Hist, axis\n",
    "import awkward as ak\n",
    "import numpy as np\n",
    "import mplhep as hep"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be5da9a2-3314-4e84-9e9f-4128167c32b2",
   "metadata": {},
   "source": [
    "Most of the work in creating a histogram is in making the axes. This is where you decide binning, whether you want continuous or categorical variables, axis names, axis labels, and other things.\n",
    "\n",
    "Here, we define an axis for a continuous variable with `hist.axis.Regular`. We also define a categorical axis with fixed, defined possible entries (`FixedCategoryAxis`) and another without pre-defined possible entries (`VariableAxis`). These latter two are created with `hist.axis.StrCategory`, and accept strings when filling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f0efa63f-1113-49fb-a45e-3dbb2d4168fa",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "RealNumberAxis = axis.Regular(10,0.0,100.0,name=\"data\")\n",
    "FixedCategoryAxis = axis.StrCategory(['Cat','Dog'],name='animal',label='My Animal Axis')\n",
    "VariableAxis = axis.StrCategory([],growth=True,name='color')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5ac7492-c7c2-4254-9439-5717be03b980",
   "metadata": {},
   "source": [
    "Now, we can create histograms of different dimensions by passing in the axes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b01b6b1c-d61b-42cb-8752-d56a8bda2021",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "h1D = Hist(RealNumberAxis)\n",
    "h2D = Hist(RealNumberAxis,FixedCategoryAxis)\n",
    "h3D = Hist(RealNumberAxis,FixedCategoryAxis,VariableAxis)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0f66fe8-a4f8-41c1-977d-bd479e766392",
   "metadata": {},
   "source": [
    "Let's fill in some data. First, we'll fill the 1D histogram by passing it a 1D array of numbers. Notice that when we defined the axis `RealNumberAxis`, its name was `data`. That decides what the keyword argument should be when filling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4848760f-ac56-47c3-b2cc-2fce13f56642",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<html>\n",
       "<div style=\"display:flex; align-items:center;\">\n",
       "<div style=\"width:290px;\">\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"-10 -105 270 120\">\n",
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       "0\n",
       "</text>\n",
       "<text text-anchor=\"middle\" x=\"250\" y=\"15\" style=\"fill:currentColor;\">\n",
       "100\n",
       "</text>\n",
       "<text text-anchor=\"middle\" x=\"125.0\" y=\"15\" style=\"fill:currentColor;\">\n",
       "data\n",
       "</text>\n",
       "<polyline points=\"  0,0   0,-100  25,-100  25,-0  50,-0  50,-0  75,-0  75,-0 100,-0 100,-0 125,-0 125,-0 150,-0 150,-0 175,-0 175,-0 200,-0 200,-0 225,-0 225,-33.3 250,-33.3 250,0\" style=\"fill:none; stroke:currentColor;\"/>\n",
       "</svg>\n",
       "</div>\n",
       "<div style=\"flex=grow:1;\">\n",
       "Regular(10, 0, 100, name='data')<br/>\n",
       "<hr style=\"margin-top:.2em; margin-bottom:.2em;\"/>\n",
       "Double() Σ=4.0\n",
       "\n",
       "</div>\n",
       "</div>\n",
       "</html>"
      ],
      "text/plain": [
       "Hist(Regular(10, 0, 100, name='data'), storage=Double()) # Sum: 4.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flat_data = ak.Array([1,2,3,95])\n",
    "h1D.fill(data=flat_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aaafb03-7468-425a-a3d8-787e6a1f9bda",
   "metadata": {},
   "source": [
    "When working with Awkward Array, we frequently end up with jagged (nested) arrays. We can only fill with 1D arrays of data, so the Awkward Array function `ak.ravel` is useful. This flattens the data and removes empty arrays, formatting it well for filling histograms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "721db009-ee93-4022-acce-5d9cd6ce988e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<html>\n",
       "<div style=\"display:flex; align-items:center;\">\n",
       "<div style=\"width:290px;\">\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"-10 -105 270 120\">\n",
       "<line x1=\"-5\" y1=\"0\" x2=\"255\" y2=\"0\" style=\"fill:none;stroke-width:2;stroke:currentColor\"/>\n",
       "<text text-anchor=\"middle\" x=\"0\" y=\"15\" style=\"fill:currentColor;\">\n",
       "0\n",
       "</text>\n",
       "<text text-anchor=\"middle\" x=\"250\" y=\"15\" style=\"fill:currentColor;\">\n",
       "100\n",
       "</text>\n",
       "<text text-anchor=\"middle\" x=\"125.0\" y=\"15\" style=\"fill:currentColor;\">\n",
       "data\n",
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       "<polyline points=\"  0,0   0,-60  25,-60  25,-20  50,-20  50,-20  75,-20  75,-100 100,-100 100,-40 125,-40 125,-0 150,-0 150,-20 175,-20 175,-0 200,-0 200,-0 225,-0 225,-20 250,-20 250,0\" style=\"fill:none; stroke:currentColor;\"/>\n",
       "</svg>\n",
       "</div>\n",
       "<div style=\"flex=grow:1;\">\n",
       "Regular(10, 0, 100, name='data')<br/>\n",
       "<hr style=\"margin-top:.2em; margin-bottom:.2em;\"/>\n",
       "Double() Σ=14.0\n",
       "\n",
       "</div>\n",
       "</div>\n",
       "</html>"
      ],
      "text/plain": [
       "Hist(Regular(10, 0, 100, name='data'), storage=Double()) # Sum: 14.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jagged_data = ak.Array([[10,20,30],[45.3,47],[61],[],[37,37,38,39]])\n",
    "h1D.fill(data=ak.ravel(jagged_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e282b546-2196-40d3-bbea-72c5469ba12a",
   "metadata": {},
   "source": [
    "The `mplhep` package is useful for plotting HEP-style plots. It is built on top of matplotlib, but offers things like style sheets for some LHC experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "da801b6b-0932-4258-b0c0-2c31a0417f5a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[StairsArtists(stairs=<matplotlib.patches.StepPatch object at 0x7f781e9c36d0>, errorbar=None, legend_artist=None)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hep.histplot(h1D,yerr=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "678cdc47-40d1-435d-9f57-7e11c66c3896",
   "metadata": {},
   "source": [
    "To fill the 2D histogram, we can either pass in one 1D array and a single value for the other axis, or two 1D arrays of equal length. In the former case, the single value is broadcasted to match every entry in the 1D array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b4ae296c-9f0f-4004-b677-dd2dfbe7864b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ColormeshArtists(pcolormesh=<matplotlib.collections.QuadMesh object at 0x7f781685f310>, cbar=<matplotlib.colorbar.Colorbar object at 0x7f7816885d50>, text=[])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oql/XGShJ6t69u6677jr9+9//Vn5+viZPnkzwBAAArElNTVVycnLwyMzMPOG1ubm5SkhIkM/n05AhQ/T666+7jmNcZ6C2bdumvn37ateuXXrvvfe0fPly9e7dW3fccYcmTZqkKlWquG0SAABEKg8LuSu8f0n5+flKSkoKnj5Z9ql58+Zav369fvjhB82dO1cZGRlavny5qyDKdQDVtm1b9ezZU++9955q1Kihbt26qUePHhowYIAWLVqkdevWuW0SAABEKC/rkCq6f0nBp+pCUbVqVTVt2lSS1KFDB61evVqPP/64/vd//zfkfl2X8GbMmKHs7GzVqFEjeK5jx45at26dzj33XLfNAQAAWGWMUXFxsat7XGeg+vfvf9zzp512mnr16uW2OQAAEMkibCPN++67T1dddZVSU1O1f/9+ZWdna9myZVqwYIGrdjy9TPjnNm/erOeff15///vftXfvXh06dKi8TQIAgAgRLk/hheqbb75R//79VVBQoOTkZLVp00YLFixQt27dXLXjKYA6cOCAXnnlFT333HNasWKFunTpokmTJunaa6/10hwAAMBv4rnnnquQdlwFUP/3f/+nZ599VnPmzNFZZ52lfv36aeXKlZo2bRrbGAAAEK1slvAsCTmAatWqlQ4ePKi+fftq5cqVwYBp3LhxlTY4AAAQ3iKthFdRQn4Kb+vWrbrkkkvUpUsXtWzZsjLHBAAAIoUJg8OCkAOovLw8NW/eXLfffrsaNmyoO++8U+vWrZPjWNw8CwAAwIKQA6gGDRro/vvv19atW/XCCy9o165duuiii1RSUqJZs2bps88+q8xxAgCAsOSEwfHb8/QuvK5du2r27NkqKCjQE088oaVLl6pFixZq06ZNRY8PAACEM9vlu3Av4R1PcnKyhg4dqjVr1mjt2rXq3LlzBQ0LAAAgfJV7I81Sbdu21bRp0yqqOQAAEAkibCfyilJhARQAAIhCxjly2OzfgnKV8AAAAKIRGSgAAOCZMUcOm/3bQAAFAAC8Yw3UiblZHH7HHXd4HgwAAEAkCCmAmjp1akiNOY5DAAUAQDSJ0kXkIQVQeXl5lT0OAAAQgRxz5LDZvw2sgQIAAN6xBip0O3fu1Pz587Vjxw4dOnSozGdTpkypkIEBAACEK9cB1JIlS3TNNdcoLS1NW7ZsUXp6ur744gsZY3TuuedWxhgBAEC4itI1UK430rz33ns1duxYbdy4UfHx8Zo7d67y8/N16aWX6oYbbqiMMQIAgHBl+0XCkfIy4U2bNikjI0OSFBcXpx9//FEJCQl68MEH9fDDD1f4AAEAAMKN6wCqevXqKi4uliTVr19f27ZtC362Z8+eihsZAAAIf7azT5GyiPyCCy7Qv//9b7Vq1Uo9e/bU2LFjlZubq3nz5umCCy6ojDECAIBwxVN4oZkyZYqKiookSRMmTFBRUZFeeeUVNW3aNOQNNwEAACKZ6wCqSZMmwT+fdtppmjFjRoUOCAAARJAofQqvXBtpFhUVKRAIlDmXlJRUrgEBAIDIEa07kbteRJ6Xl6eePXuqevXqSk5OVs2aNVWzZk3VqFFDNWvWrIwxAgAAhBXXGah+/fpJkp5//nnVrVtXjmMxbQcAAOxiEXloNmzYoJycHDVv3rwyxgMAABD2XJfwzjvvPOXn51fGWAAAQIRx9J91UFYOS9/bdQbq2Wef1ZAhQ/TVV18pPT1dVapUKfN5mzZtKmxwAAAA4ch1APXtt99q27ZtGjRoUPCc4zgyxshxHPn9/god4G9tdzufYn0+28MIWf2HP7I9hFPaFfXPsT0EV+qLvw+VKXZJju0hIMz4L2tvewgh85f8JC17s+IbZhuD0AwePFjt2rXTyy+/zCJyAACiHYvIQ/Pll19q/vz5atq0aWWMBwAAIOy5XkTetWtXffzxx5UxFgAAEGlsv0g4UjJQvXr10ujRo5Wbm6uzzz77mEXk11xzTYUNDgAAhLdo3YncdQA1ZMgQSdKDDz54zGenwiJyAACAX+M6gPrlu+8AAEAUYxE5AACASwRQJzZt2jTddtttio+P17Rp00567R133FEhAwMAAAhXIQVQU6dOVb9+/RQfH6+pU6ee8DrHcQigAACIIiwiP4m8vLzj/hkAAEQ5diIHAABwiTVQofH7/Zo1a5aWLFmi3bt3H/NU3tKlSytscAAAAOHIdQA1cuRIzZo1Sz179lR6ejrvwgMAIIqxBipE2dnZmjNnjnr06FEZ4wEAAJEkSkt4rt+FV7VqVV4kDAAAoprrAGrs2LF6/PHHZYzNcBMAAIQF858yno0jYhaRf/jhh3r//ff17rvvqnXr1se8THjevHkVNjgAABDmorSE5zqAqlGjhn7/+99XxlgAAAAigusAaubMmZUxDgAAEImiNAPleg3U8ezdu1fTp09X27ZtK6I5AAAQIWyuf7K5hUK5diJfvHixnnvuOb3xxhs6/fTTdd1111XUuAAAAMKW6wBqx44dmjlzpmbOnKmioiLt3btXc+bM0fXXX18Z4wMAAAg7IZfw5syZo+7du6tly5bauHGjHn/8cX399deKiYlRy5YtK3OMAAAgXJkwOCwIOQPVt29f3X333Zo7d64SExMrc0wAACBCROurXELOQA0ePFgzZszQlVdeqaeeekp79+6tzHEBAACErZADqKeffloFBQW67bbb9PLLL6tevXrq3bu3jDEKBAKVOUYAABDOoqx8J7ncxqBatWrKyMjQ8uXLlZubq1atWqlu3bq66KKL1LdvX3YhBwAg2the/xTuJbxfOuuss5SZman8/HzNnj1bBw8eVJ8+fSpybAAAAGGpXPtASVJMTIx69eqlXr16affu3RUxJgAAECGidRF5uQOon6tTp05FNgcAAMKd5bVIEVfCAwAAiFYVmoECAADRJVpLeGSgAACAd7afwHMZQGVmZuq8885TYmKi6tSpo2uvvVZbtmxx/bVdB1ADBw7UBx984LojAAAA25YvX65hw4ZpxYoVWrRokUpKStS9e3cdOHDAVTuuS3j79+9X9+7dlZqaqkGDBikjI0MNGjRw2wwAADgVRNgi8gULFpT5eebMmapTp45ycnJ0ySWXhNyO6wzU3Llz9dVXX2n48OF69dVXdeaZZ+qqq67Sa6+9psOHD7ttDgAARLDSNVA2D0kqLCwscxQXF4c0/n379kmSatWq5ep7e1oDVbt2bY0cOVLr1q3TqlWr1LRpU/Xv31/169fX6NGj9fnnn3tpFgAARBrb65+OBlCpqalKTk4OHpmZmb8+dGM0ZswYderUSenp6a6+drmewisoKNDChQu1cOFCxcbGqkePHvrkk0/UqlUrPfLIIxo9enR5mgcAAAhJfn6+kpKSgj/7fL5fvWf48OHasGGDPvzwQ9f9uQ6gDh8+rPnz52vmzJlauHCh2rRpo9GjR6tfv35KTEyUJGVnZ+v2228ngAIA4FQXJmugkpKSygRQv2bEiBGaP3++PvjgAzVs2NB1t64DqHr16ikQCKhPnz5atWqV2rZte8w1V1xxhWrUqOF6MAAAILJE2j5QxhiNGDFCr7/+upYtW6a0tDRP/boOoKZOnaobbrhB8fHxJ7ymZs2aysvL8zQgAACAyjJs2DC99NJLevPNN5WYmKhdu3ZJkpKTk1WtWrWQ23EdQPXv39/tLQAA4FQVJiW8UGVlZUmSOnfuXOb8zJkzNXDgwJDbCTmAGjx4cEjXPf/88yF3DgAAIlsklvAqQsgB1KxZs9S4cWO1a9euwjoHAACIRCEHUEOGDFF2dra2b9+uwYMH6+abb3a96RQAADjFRFgJr6KEvJHmjBkzVFBQoHvuuUdvvfWWUlNTdeONN+q9994jIwUAQLSyvYlmuAdQ0pFNqfr06aNFixbp008/VevWrTV06FA1btxYRUVFlTVGAACAsOJ5J3LHceQ4jowxCgQCFTkmAAAQIZyjh83+bXCVgSouLtbLL7+sbt26qXnz5srNzdUTTzyhHTt2KCEhobLGCAAAwpXt8p2lEl7IGaihQ4cqOztbjRo10qBBg5Sdna3atWtX5tgAAECYi7RtDCpKyAHUU089pUaNGiktLU3Lly/X8uXLj3vdvHnzKmxwAAAA4SjkAGrAgAFyHJtVTgAAEHaidBsDVxtpAgAAHCMKdzNytYgcAAAA5djGAAAAgEXkAAAAbkXpGihKeAAAAC6RgQIAAJ5RwgMAAHCLEh4AAABCQQYKAAB4RgkPAADArSgt4RFAAQAA76I0gGINFAAAgEtkoAAAgGesgQIAAHCLEh4AAABCQQYKAAB45hgjx9hLQdnqmwAKAAB4RwkPAAAAoSADBQAAPOMpPAAAALco4QEAACAUZKAAAIBnlPAAAADcitISHgEUAADwLFozUKyBAgAAcIkMFAAA8I4SHgAAgHs2S3i2UMIDAABwiQwUAADwzpgjh83+LSCAAgAAnvEUHgAAAEJCBgoAAHjHU3gAAADuOIEjh83+baCEBwAA4BIZKAAA4F2UlvDCMgO1a9cujRgxQk2aNJHP51Nqaqp69eqlJUuWhHT/rFmzVKNGjcodJAAACD6FZ/OwIewyUF988YUuuugi1ahRQ4888ojatGmjw4cP67333tOwYcO0efNm20MEAAClonQfqLDLQA0dOlSO42jVqlX6wx/+oGbNmql169YaM2aMVqxYIUmaMmWKzj77bFWvXl2pqakaOnSoioqKJEnLli3ToEGDtG/fPjmOI8dxNGHCBIvfCAAAnGrCKoD6/vvvtWDBAg0bNkzVq1c/5vPSslxMTIymTZumjRs36u9//7uWLl2qu+++W5LUsWNHPfbYY0pKSlJBQYEKCgp05513HtNWcXGxCgsLyxwAAMAd2+U7NtKUtHXrVhlj1KJFi5NeN2rUKHXp0kVpaWnq2rWrHnroIc2ZM0eSVLVqVSUnJ8txHKWkpCglJUUJCQnHtJGZmank5OTgkZqaWinfCQCAU5oJg8OCsAqgzNE6puM4J73u/fffV7du3dSgQQMlJiZqwIAB+u6773TgwIGQ+7r33nu1b9++4JGfn1+usQMAgOgRVgHUWWedJcdxtGnTphNe8+WXX6pHjx5KT0/X3LlzlZOToyeffFKSdPjw4ZD78vl8SkpKKnMAAAB3bJfvKOFJqlWrlq644go9+eSTx80m/fDDD1qzZo1KSkr0t7/9TRdccIGaNWumr7/+usx1VatWld/v/62GDQBA9Cp9Cs/mYUFYBVCSNGPGDPn9fp1//vmaO3euPv/8c23atEnTpk3ThRdeqN/97ncqKSnR9OnTtX37dr3wwgt66qmnyrRx5plnqqioSEuWLNGePXt08OBBS98GAACcisIugEpLS9PatWvVpUsXjR07Vunp6erWrZuWLFmirKwstW3bVlOmTNHDDz+s9PR0vfjii8rMzCzTRseOHTVkyBDddNNNOuOMM/TII49Y+jYAAJzabJfvbJXwHGMs5b7CTGFhoZKTk9V89F8U64u3PZyQ1X/4I9tDAICo5b+sve0hhKyk5Cf9a9mftW/fvgpZ91v6e/PCKx9UXBV7vzdLDv+k/1vwpwr7XqEKuwwUAABAuAu7V7kAAIDIYbOMVtq/DQRQAADAu4A5ctjs3wICKAAA4J3F3cCD/VvAGigAAACXyEABAADPHFleA2WpXwIoAADgncXdwIP9W0AJDwAAwCUCKAAA4JntXci9lA8/+OAD9erVS/Xr15fjOHrjjTdct0EABQAAvDNhcLh04MABnXPOOXriiSfc33wUa6AAAEBUueqqq3TVVVeVqw0CKAAA4JljjByLi8hL+y4sLCxz3ufzyefzVVq/lPAAAIB3gTA4JKWmpio5OTl4ZGZmVurXJgMFAAAiXn5+vpKSkoI/V2b2SSKAAgAA5RAuJbykpKQyAVRlI4ACAADeRem78AigAACAdxG4E3lRUZG2bt0a/DkvL0/r169XrVq11KhRo5DaIIACAABRZc2aNerSpUvw5zFjxkiSMjIyNGvWrJDaIIACAACeed0NvCL7d6tz584y5cyaEUABAADvIrCEVxHYBwoAAMAlMlAAAMAzJ3DksNm/DQRQAADAO0p4AAAACAUZKAAA4B0baQIAALgTLq9y+a1RwgMAAHCJDBQAAPAuSheRE0ABAADvjCSL2xiwBgoAAEQc1kABAAAgJGSgAACAd0aW10DZ6ZYACgAAeBeli8gp4QEAALhEBgoAAHgXkORY7t8CAigAAOAZT+EBAAAgJGSgAACAd1G6iJwACgAAeBelARQlPAAAAJfIQAEAAO+iNANFAAUAALxjGwMAAAB32MYAAAAAISEDBQAAvGMNFAAAgEsBIzkWA6gAJTwAAICIQAYKAAB4RwkPAADALcsBlCjhAQAARAQyUAAAwDtKeAAAAC4FjGyV0f7T/2+PEh4AAIBLZKAAAIB3JnDksNm/BQRQAADAO9ZAAQAAuMQaKAAAAISCDBQAAPCOEh4AAIBLRpYDKDvdUsIDAABwiQwUAADwjhIeAACAS4GAJIv7QAXs9E0JDwAAwCUyUAAAwDtKeAAAAC5FaQBFCQ8AAMAlMlAAAMC7KH2VCwEUAADwzJiAjLH3FJ6tvgmgAACAd8ZYywIF+7eANVAAAAAukYECAADeGctroNjGAAAARJxAQHIs7kRuaQ0UJTwAAACXCKAAAIhy+fn5ysrKUklJScj37Ny588gfSjfStHlYQAkPAIAo98ADD2jBggXq16+fkpKSQrrnoYcekiSZQEDGYgmPbQwAAMBvbu3atfrHP/6hrKyskIOntWvXKjs7u5JHFt4o4QEAEKWMMRo7dqxatmypW2+91dU9zZo1Kz1h/7CADBQAAFHqn//8p5YtW6a3335bcXGhhQSl98yZM0c33njjkU00nejbxoAMFAAAUaikpER33XWXunbtqh49eri+p3v37pU8wvBGBgoAgCj0zDPPaMuWLXrppZfkOI73e4yRZHMfKEp4AADgN1BYWKjx48erf//+ateunad7CgsLJUkmYGQslvAMARQAAPgtPPzww9q/f78mTZpU/ntMQHYzUOxEDgAAKtnOnTs1ZcoUjR07Vg0bNgzpnvz8fNf3hLsZM2YoLS1N8fHxat++vf71r3+5up8ACgCAKPLQQw8pKSlJ99xzT8j3PPDAAye8xwSM9cOtV155RaNGjdL999+vdevW6eKLL9ZVV12lHTt2hNwGARQAAFEkOztbf/7zn5WYmBjS9aUbbZ7wHhOwf7g0ZcoU3XLLLbr11lvVsmVLPfbYY0pNTVVWVlbIbbAG6qjSRWj+4p8sj8SdEnPY9hAAIGr5SyLnd8bhw0fGetZZZ7neNPNkG22W6LBkcRuoEh35PVi6qL2Uz+eTz+c75vpDhw4pJydH48aNK3O+e/fu+uijj0LulwDqqO+++06StHXGg5ZH4s4W2wMAgGi27E3bI3Dt7rvvdr1p5vE22qxatapSUlL04a53KmOYriQkJCg1NbXMufHjx2vChAnHXLtnzx75/X7VrVu3zPm6detq165dIfdJAHVUrVq1JEk7duxQcnKy5dGcmgoLC5Wamqr8/PyQ37eE0DG/lYv5rVzMb+UqKSnReeedp+3bt+v6668P+Z6TbbQZHx+vvLw8HTp0qKKH65ox5pi9rI6Xffq5X15/vDZOhgDqqJiYI8vBkpOT+cdbyZKSkpjjSsT8Vi7mt3Ixv5UjKytLeXl5kqTY2NiQ7gllo834+HjFx8dX2Dh/C6effrpiY2OPyTbt3r37mKzUybCIHACAU1jpBph9+vRxfc+AAQNC3mgzUlStWlXt27fXokWLypxftGiROnbsGHI7BFAAAJzCSjfAfOCBB1zfM3HixEocmT1jxozRs88+q+eff16bNm3S6NGjtWPHDg0ZMiTkNijhHeXz+TR+/PhfrZnCO+a4cjG/lYv5rVzMb+X4+QaYTZo0CWmOT8VNM3/ppptu0nfffacHH3xQBQUFSk9P1zvvvKPGjRuH3IZjbL1EBgAAVKoBAwbovffe09atW0Pe98nLPdGIDBQAAKegtWvX6oUXXlBWVparTTPd3hOtyEABAHCKMcaoa9eu+uabb7Rhw4aQ9n3yck80Y3YAADjFnGwDzIq8J5qRgQIA4BRy+PBhtWnTRvXr19fixYtD2hzSyz3Rjm0MjpoxY4bS0tIUHx+v9u3b61//+pftIUWkzMxMnXfeeUpMTFSdOnV07bXXasuWsi+cMcZowoQJql+/vqpVq6bOnTvrk08+sTTiyJaZmSnHcTRq1KjgOea3fL766ivdfPPNql27tk477TS1bdtWOTk5wc+Z3/IpKSnRAw88oLS0NFWrVk1NmjTRgw8+qEDgPy+EZY5D98EHH6hXr16qX7++HMfRG2+8oWeffVZbtmzRX//6V0n61bksLi5Wt27dtHnzZn300Ufq3bu3du7caePrRBYDk52dbapUqWKeeeYZ8+mnn5qRI0ea6tWrmy+//NL20CLOFVdcYWbOnGk2btxo1q9fb3r27GkaNWpkioqKgtdMnjzZJCYmmrlz55rc3Fxz0003mXr16pnCwkKLI488q1atMmeeeaZp06aNGTlyZPA88+vd999/bxo3bmwGDhxoVq5cafLy8szixYvN1q1bg9cwv+UzceJEU7t2bfP222+bvLw88+qrr5qEhATz2GOPBa9hjkP3zjvvmPvvv9/MnTvXSDIvvviiOeOMM0xGRoYxJrS5HDx4sImJiTHdunUza9euNV26dDHnnHOOKSkpsfStIgMBlDHm/PPPN0OGDClzrkWLFmbcuHGWRnTq2L17t5Fkli9fbowxJhAImJSUFDN58uTgNT/99JNJTk42Tz31lK1hRpz9+/ebs846yyxatMhceumlwQCK+S2fe+65x3Tq1OmEnzO/5dezZ08zePDgMueuu+46c/PNNxtjmOPykGRee+01k5WVZfLz80Oayx9++MHExcWZW265xeTn5xtjjPnqq69MTEyMWbBggZXvESmivoR36NAh5eTkqHv37mXOd+/eXR999JGlUZ069u3bJ+k/L2vOy8vTrl27ysy3z+fTpZdeyny7MGzYMPXs2VOXX355mfPMb/nMnz9fHTp00A033KA6deqoXbt2euaZZ4KfM7/l16lTJy1ZskSfffaZJOnjjz/Whx9+GHxZLXNcPrGxsRoyZIgaNmwY0lzm5OSopKREjz76aHDTzPr16ys9PZ35/hVRv8x+z5498vv9x7xAsG7duse8aBDuGGM0ZswYderUSenp6ZIUnNPjzfeXX375m48xEmVnZ2vt2rVavXr1MZ8xv+Wzfft2ZWVlacyYMbrvvvu0atUq3XHHHfL5fBowYADzWwHuuece7du3Ty1atFBsbKz8fr8mTZoUfE8bc1xxQpnLXbt2qWrVqqpZs+Yx1/A78OSiPoAq9csnDowxPIVQTsOHD9eGDRv04YcfHvMZ8+1Nfn6+Ro4cqYULF570DejMrzeBQEAdOnTQX/7yF0lSu3bt9MknnygrK0sDBgwIXsf8evfKK69o9uzZeumll9S6dWutX79eo0aNUv369ZWRkRG8jjmuOF7mkvn+dVFfwjv99NMVGxt7TKS9e/fuY6J2hG7EiBGaP3++3n///TLvUkpJSZEk5tujnJwc7d69W+3bt1dcXJzi4uK0fPlyTZs2TXFxccE5ZH69qVevnlq1alXmXMuWLbVjxw5J/P2tCHfddZfGjRun//7v/9bZZ5+t/v37a/To0crMzJTEHFekUOYyJSVFhw4d0t69e094DY4v6gOoqlWrqn379lq0aFGZ84sWLVLHjh0tjSpyGWM0fPhwzZs3T0uXLlVaWlqZz9PS0pSSklJmvg8dOqTly5cz3yG47LLLlJubq/Xr1wePDh06qF+/flq/fr2aNGnC/JbDRRdddMy2G5999lnwBaP8/S2/gwcPKiam7K+e2NjY4DYGzHHFCWUu27dvrypVqpS5pqCgQBs3bmS+f4215ethpHQbg+eee858+umnZtSoUaZ69ermiy++sD20iHP77beb5ORks2zZMlNQUBA8Dh48GLxm8uTJJjk52cybN8/k5uaaPn368IhyOfz8KTxjmN/yWLVqlYmLizOTJk0yn3/+uXnxxRfNaaedZmbPnh28hvktn4yMDNOgQYPgNgbz5s0zp59+urn77ruD1zDHodu/f79Zt26dWbdunZFkpkyZYtatWxfchieUuRwyZIhp2LChWbx4sVm7dq3p2rUr2xiEgADqqCeffNI0btzYVK1a1Zx77rnBx+7hjqTjHjNnzgxeEwgEzPjx401KSorx+XzmkksuMbm5ufYGHeF+GUAxv+Xz1ltvmfT0dOPz+UyLFi3M008/XeZz5rd8CgsLzciRI02jRo1MfHy8adKkibn//vtNcXFx8BrmOHTvv//+cf/PLd0HKpS5/PHHH83w4cNNrVq1TLVq1czVV19tduzYYeHbRBZe5QIAAOBS1K+BAgAAcIsACgAAwCUCKAAAAJcIoAAAAFwigAIAAHCJAAoAAMAlAigAAACXCKAAVIrOnTtr1KhRtocBAJWCAAqAdcuWLZPjOPrhhx9sDwUAQkIABQAA4BIBFIByO3DggAYMGKCEhATVq1dPf/vb38p8Pnv2bHXo0EGJiYlKSUlR3759tXv3bknSF198oS5dukiSatasKcdxNHDgQEnSggUL1KlTJ9WoUUO1a9fW1VdfrW3btv2m3w0AjocACkC53XXXXXr//ff1+uuva+HChVq2bJlycnKCnx86dEgPPfSQPv74Y73xxhvKy8sLBkmpqamaO3euJGnLli0qKCjQ448/LulIYDZmzBitXr1aS5YsUUxMjH7/+98rEAj85t8RAH6OlwkDKJeioiLVrl1b//jHP3TTTTdJkr7//ns1bNhQt912mx577LFj7lm9erXOP/987d+/XwkJCVq2bJm6dOmivXv3qkaNGifs69tvv1WdOnWUm5ur9PT0SvpGAPDryEABKJdt27bp0KFDuvDCC4PnatWqpebNmwd/XrdunXr37q3GjRsrMTFRnTt3liTt2LHjV9vu27evmjRpoqSkJKWlpYV0HwBUNgIoAOXya0nsAwcOqHv37kpISNDs2bO1evVqvf7665KOlPZOplevXvruu+/0zDPPaOXKlVq5cmVI9wFAZSOAAlAuTZs2VZUqVbRixYrgub179+qzzz6TJG3evFl79uzR5MmTdfHFF6tFixbBBeSlqlatKkny+/3Bc9999502bdqkBx54QJdddplatmypvXv3/gbfCAB+HQEUgHJJSEjQLbfcorvuuktLlizRxo0bNXDgQMXEHPnvpVGjRqpataqmT5+u7du3a/78+XrooYfKtNG4cWM5jqO3335b3377rYqKilSzZk3Vrl1bTz/9tLZu3aqlS5dqzJgxNr4iAByDAApAuT366KO65JJLdM011+jyyy9Xp06d1L59e0nSGWecoVmzZunVV19Vq1atNHnyZP31r38tc3+DBg305z//WePGjVPdunU1fPhwxcTEKDs7Wzk5OUpPT9fo0aP16KOP2vh6AHAMnsIDAABwiQwUAACASwRQAAAALhFAAQAAuEQABQAA4BIBFAAAgEsEUAAAAC4RQAEAALhEAAUAAOASARQAAIBLBFAAAAAuEUABAAC4RAAFAADg0v8DAmJ3juuvHjQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h2D.fill(data=ak.ravel(jagged_data),animal='Dog')\n",
    "h2D.fill(data=ak.ravel(flat_data),animal=['Cat','Cat','Dog','Cat'])\n",
    "hep.hist2dplot(h2D)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df0d6752-526c-4fc5-a67a-1285ac0413c8",
   "metadata": {},
   "source": [
    "This extends to 3D histograms in the same way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4439956b-8244-41d6-bb31-059aa17ee691",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hist(\n",
       "  Regular(10, 0, 100, name='data'),\n",
       "  StrCategory(['Cat', 'Dog'], name='animal', label='My Animal Axis'),\n",
       "  StrCategory(['red', 'blue'], growth=True, name='color'),\n",
       "  storage=Double()) # Sum: 19.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other_jagged_data = ak.Array([[52,30,35],[88,89]])\n",
    "h3D.fill(data=flat_data,animal='Cat',color='red')\n",
    "h3D.fill(data=ak.ravel(jagged_data),animal='Dog',color='blue')\n",
    "h3D.fill(data=ak.ravel(other_jagged_data),animal='Cat',color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cc41b98-55f4-4f8e-b501-f6867ab52d8a",
   "metadata": {},
   "source": [
    "We can't plot all axes of the 3D histogram at the same time, but we can project down to 2 axes by summing/integrating over one of the axes. Here, we first sum over the `color` axis, so the resulting plot projects out that axis. Then, we instead sum over the `data` axis, just to see how we can project out a continuous variable's axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a8aafeda-9bd0-420a-9e14-53b69b6b6c79",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ColormeshArtists(pcolormesh=<matplotlib.collections.QuadMesh object at 0x7f7816728550>, cbar=<matplotlib.colorbar.Colorbar object at 0x7f781674b4f0>, text=[])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hep.hist2dplot(h3D[{'color': sum}])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5356994f-48f8-4820-901a-d3dab5ca439d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ColormeshArtists(pcolormesh=<matplotlib.collections.QuadMesh object at 0x7f78167f5f90>, cbar=<matplotlib.colorbar.Colorbar object at 0x7f781661caf0>, text=[])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hep.hist2dplot(h3D[{'data': sum}])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4824d07a-a559-41e4-9964-a8bfafb5ca6d",
   "metadata": {},
   "source": [
    "We can also examine the actual values in the histogram's bins with `my_hist.values()`. This comes out as a nested numpy array, where the level of nesting corresponds to the number of dimensions of the histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "15bf3ed4-c1ec-4494-b3b5-244df39910fb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[3., 0.],\n",
       "        [0., 0.]],\n",
       "\n",
       "       [[0., 0.],\n",
       "        [0., 1.]],\n",
       "\n",
       "       [[0., 0.],\n",
       "        [0., 1.]],\n",
       "\n",
       "       [[2., 0.],\n",
       "        [0., 5.]],\n",
       "\n",
       "       [[0., 0.],\n",
       "        [0., 2.]],\n",
       "\n",
       "       [[1., 0.],\n",
       "        [0., 0.]],\n",
       "\n",
       "       [[0., 0.],\n",
       "        [0., 1.]],\n",
       "\n",
       "       [[0., 0.],\n",
       "        [0., 0.]],\n",
       "\n",
       "       [[2., 0.],\n",
       "        [0., 0.]],\n",
       "\n",
       "       [[1., 0.],\n",
       "        [0., 0.]]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h3D.values()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d8803dc-e697-4fb7-8768-c50a7752d834",
   "metadata": {},
   "source": [
    "Finally, information about the bins is accessible through the `axes` attribute of the histogram object. For example, we could get the centers of the bins for the continuous-valued variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5f4a4b99-513a-468b-bc73-1e69588a871a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5., 15., 25., 35., 45., 55., 65., 75., 85., 95.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h3D.axes[0].centers"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}