{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting started\n",
    "\n",
    "This getting started guide will walk you through\n",
    "\n",
    " - the creation of your first plot in uhepp format,\n",
    " - how to save and load plots locally and,\n",
    " - how to interact with the API of uhepp.org.\n",
    "\n",
    "The guide expects that you have some basic experience in Python. You need to be familiar with functions and objects in Python, however, you don't need to be a library developer. The whole uhepp eco-system does not rely on ROOT, therefore, it's not a disadvantage if you are not familiar with ROOT. You should have a working installation of Python 3 on your machine. The guide will install additional Python packages. The guide expects you to install packages, either in your home directory, system-wide, or in a virtual environment.\n",
    "\n",
    "This guide will not show you how to process HEP data. It is assumed that you have an analysis framework in place that iterates over individual events and whose output is a histograms collection. These might be ROOT TH1 histograms or numpy arrays produced with Numpy, Pandas, Dask, PySpark, or Caffea. For this guide, we will read sample histograms, including statistical uncertainty from a JSON file.\n",
    "\n",
    "## Install requirements\n",
    "\n",
    "In principle, you only need a text editor to write and edit JSON or YAML files and a simple tool like curl to interact with the API. However, this would be extremely cumbersome. It is much more convenient to use the uhepp Python package for both tasks. The package provides an interface to create and manipulate plots in uhepp format stored locally or remotely via the API.\n",
    "\n",
    "For this guide, we will need the `uhepp` and `requests` package to work with the toy data. Make sure the following package is installed. If you use pip, run:\n",
    "\n",
    "```bash\n",
    "pip install uhepp requests\n",
    "```\n",
    "\n",
    "To test that it is installed, open an interactive Python 3 shell and type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import uhepp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keep the shell open, we will use it trough out this guide."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create your first plot\n",
    "\n",
    "Creating a plot with uhepp involved two tightly coupled but still separated tasks. First, we need to add the raw histogram contents. Afterward, we can set up the visual appearance of the plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Raw data\n",
    "\n",
    "The first step is to load the sample histograms from a prepared JSON file.  We will [download the toy data](https://gitlab.cern.ch/fsauerbu/uhepp/-/raw/master/docs/toyhisto.json) from a GitLab repository. This tutorial will use `requests` to download the file and parse the JSON on the fly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_URL = \"https://gitlab.cern.ch/fsauerbu/uhepp/-/raw/master/docs/toyhisto.json\"\n",
    "toydata = requests.get(DATA_URL).json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `toydata` variable is a dictionary, contains binned yields for three processes: Simulated background (bkg), simulated signal (sig), and measured data. (Here, in this example, even the measure data is taken from a random generator.) Besides the yields, the variable also contains the statistical uncertainty for each bin and process. Additionally, the dictionary contains the key bin_edges. This variable stores the boundaries of the bins. Feel free to explore the data in your shell. For example, run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.38476131306473815, 0.6852710350460031, 1.7207156839990556, 0.0, 2.8308405623840565, 4.0549770212878675, 5.57300699977592, 7.591543782837617, 12.13708713548585, 15.913666346814601, 18.5119900031778, 23.88618979127034, 30.019646840475104, 41.61652455284508, 46.602998183358295, 51.42363524234844, 43.92248543679045, 44.891664438449766, 40.488581031599495, 33.42807275917818, 17.59299466369191, 21.105542096584088, 9.273034869562252, 5.021636967998518, 2.694741424994959, 3.603462408442283, 0.3354262885936805, 0.7772487556835586, 0.0, 0.3308584281754179, 0.471742727544779, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n"
     ]
    }
   ],
   "source": [
    "print(toydata[\"bkg\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.19577515884588487, 0.320019978148849, 0.33110021101888526, 0.5208840004818056, 0.6396045713812509, 0.7281262404641017, 0.846256380979379, 0.8907322253814467, 1.0681261828724806, 1.0911717450407639, 0.991076322718083, 1.0591769666174482, 1.0976550831183312, 0.9730515941582837, 0.9395782774358155, 0.8973089778473059, 0.7007885754941169, 0.6196871081239709, 0.5819549508292591, 0.4978869022937864, 0.4515592318838932, 0.30267826071959775, 0.18549896395226498, 0.14203642969452882, 0.13900129897446561, 0.0920435947179317, 0.1192075877406717]\n"
     ]
    }
   ],
   "source": [
    "print(toydata[\"sig_stat\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "in your interactive Python shell.\n",
    "\n",
    "You might have noticed that there are 41 bin edges. This means you would expect 40 bins. However, the binned data lists have 42 entries. The additional two values correspond to the under- and overflow bins. By convention, these two bins are always included in the raw data of a uhepp histogram. It is up to you to tell uhepp to include these events in the visual plot or not.\n",
    "\n",
    "A uhepp histogram stores the raw data separated from the visual specification. Let's start by creating a UHeppHist object and adding the raw data to the histogram. When you create a histogram object, the first argument is the mathematical symbol of the quantity of the x-axis. In our case, the data represents a mass distribution, so we use the letter m. The second argument is the bin boundaries. We take them directly from the `toydata` dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist = uhepp.UHeppHist(\"$m$\", toydata[\"bin_edges\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The raw data is stored in the yields attribute. It is another dictionary mapping arbitrary internal names to the binned data. The binned data is stored as Yield objects. The yield objects couple the value with its uncertainties. A yield object comes close to a ROOT TH1 object (with some key distinctions). Yield objects can be added, scales, etc., while propagating uncertainties. Let's first create the yield objects from the sample data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "signal_yield     = uhepp.Yield(toydata[\"sig\"],  toydata[\"sig_stat\"])\n",
    "background_yield = uhepp.Yield(toydata[\"bkg\"],  toydata[\"bkg_stat\"])\n",
    "data_yield       = uhepp.Yield(toydata[\"data\"], toydata[\"data_stat\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's add the yields to our histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist.yields = {\n",
    "    \"signal\":     signal_yield,\n",
    "    \"background\": background_yield,\n",
    "    \"data\":       data_yield\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The names that we use here as dictionary keys, can be arbitrary strings. You are encouraged to use descriptive names, which makes editing the histogram much easier. We will use these names later to refer to the yields when we specify the main plot's content or the ratio plot. In this example, we've created a single background entry. In a real-world histogram, you would have several different physics processes with one entry per process. You are encouraged to use a fine-grained process list here. Merging two or more yield entries in the visual specification is easy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visual settings\n",
    "\n",
    "From the visual point of view, uhepp histograms are made up of a list of stacks. A stack consists of stack items. The bin contents of stack items within a stack are added (*stacked*). Separate stacks are down on top of each other. A stack in this context does not only refer to a bar histogram (`stepfilled`). It could be only the outline of bar histogram (`step`) or `points` usually used for measured data.\n",
    "\n",
    "Let's start by setting up a stack for the signal and background expectation (here `mc` for Monte Carlo). This stack consists of one stack item for the `signal` and one for the `background`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "background_si = uhepp.StackItem([\"background\"], \"Background\")\n",
    "signal_si = uhepp.StackItem([\"signal\"], \"Signal\", color=\"red\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The creation of a `StackItem` takes at least two arguments. The first argument is a list of yield names we've defined in the previous section. Here you see the real power of separating the yield data from their stack definition. By passing multiple processes to the first argument, we can merge histograms with a single line of Python code. The second argument is used as the label in the legend. This is also the time to specify the color, line style, and other style settings. We choose to have the signal in red and leave the background color up to the system.\n",
    "\n",
    "The stack items need to be combined into a `Stack`. This tells the renderer to *stack* the bars of each stack item vertically. Finally, we can add the `mc_stack` to the stack list of our histogram object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "mc_stack = uhepp.Stack([background_si, signal_si])\n",
    "hist.stacks.append(mc_stack)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We proceed similarly with data. Here we have a single item in the stack. The type of the stack should be `points`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_si = uhepp.StackItem([\"data\"], \"Data\")\n",
    "\n",
    "data_stack = uhepp.Stack([data_si], bartype=\"points\")\n",
    "hist.stacks.append(data_stack)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To improve the output, we can also make the label of the x-axis more verbose. In the beginning, we've set the symbol already to `m`. Here we add a verbose label `Mass` and the unit `GeV`. On top, we can add some metadata. Some tools use the filename attribute to have a default filename, for example, when you render the plot to graphics file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist.variable = \"Mass\"\n",
    "hist.unit = \"GeV\"\n",
    "hist.filename = \"higgs_mass_dist\"\n",
    "hist.author = \"Your name\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it is time to see how far we've got."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 360x324 with 1 Axes>,\n",
       " <AxesSubplot:xlabel='Mass $m$ / GeV', ylabel='Events / 5 GeV'>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist.render()\n",
    "# hist.show()   if you're not in a Jupyter notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far so good. However, we realize that a ratio plot between data and the `background` plus `signal` model would have been nice. Adding a ratio plot is a matter of adding a `RatioItem` to the `ratio` list property. The two mandatory arguments of `RatioItem` are two lists of yield names: First, the one of the numerator. Second, the one for the denominator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 360x360 with 2 Axes>,\n",
       " (<AxesSubplot:ylabel='Events / 5 GeV'>,\n",
       "  <AxesSubplot:xlabel='Mass $m$ / GeV'>))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratio_si = uhepp.RatioItem([\"data\"], [\"signal\", \"background\"], bartype=\"points\")\n",
    "hist.ratio = [ratio_si]\n",
    "\n",
    "hist.render()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can you update the above example, to have the comparison of data against the background-only model?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You might interject that the binning is a bit too fine, and the statistical power of each bin is relatively low. No problem. We can rebin the histogram on the fly during rendering. Simply set an alternative binning. The original histograms are not modified, so you can always undo this step without rerunning your analysis framework."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 360x360 with 2 Axes>,\n",
       " (<AxesSubplot:ylabel='Events / 20 GeV'>,\n",
       "  <AxesSubplot:xlabel='Mass $m$ / GeV'>))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "coarser_bins = hist.bin_edges[::4]  # Keep only every fourth bin edge\n",
    "hist.rebin_edges = coarser_bins\n",
    "hist.render()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To save the result as a graphics file, call the `render` method with a filename."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 360x360 with 2 Axes>,\n",
       " (<AxesSubplot:ylabel='Events / 20 GeV'>,\n",
       "  <AxesSubplot:xlabel='Mass $m$ / GeV'>))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist.render(\"mass_dist.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Local storage\n",
    "\n",
    "At this point, we might want to save the result not only in a graphics file but also as a uhepp plot. The whole point of uhepp is that you can save the intermediate result between raw data and graphics files such that you can make modifications to the visual appearance without reprocessing your data set.\n",
    "\n",
    "The uhepp format does not define the syntax of the file format, only its semantics. You can use any semi-structured format that supports lists and maps. Popular choices are JSON or YAML. We will use JSON in this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist.to_json(\"mass_hist.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uhep plots stored in JSON or YAML can be modifed with many popular tools. You could even change labels or style settings manually with a text editor. Loading previously saved histograms is also easy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 360x360 with 2 Axes>,\n",
       " (<AxesSubplot:ylabel='Events / 20 GeV'>,\n",
       "  <AxesSubplot:xlabel='Mass $m$ / GeV'>))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Could be in a new Python session\n",
    "import uhepp\n",
    "loaded_hist = uhepp.from_json(\"mass_hist.json\")\n",
    "loaded_hist.render()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Think about your recent work. Was there a moment when you wanted to modify the labels, axis ranges, collaboration badges, color scheme, binning, histogram content etc., after the plots have been produced? Had you stored the intermediate data in uhepp format, these tasks are trivial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Online storage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In high-energy physics, a common task is to processes enormous data sets. Often this is only possible with a computing cluster. The resulting histograms and plots are stored on a remote file system. To see what the analysis code did, one has to copy the files to the local computer.\n",
    "\n",
    "Today, high-energy experiments can only be built and maintained by large collaborations. The actual analysis of the data takes place in a team. Showing histograms to colleagues is a daily recurring task. To me, it was both tedious and unsatisfactory to prepare a PDF plot books.\n",
    "\n",
    "Wouldn't it be nice to have a central hub for all plots? Computing nodes can push histograms in uhepp format to the hub. The analyzer can review plots in realtime; colleagues can browse through the plots online, extract yields, download them, or even upload modified (e.g., rebinned) versions themself. Students could also benefit from a central hub. On the hub, a student can collect plots and histograms throughout the whole time as a student. Naturally, things like color schemes, process compositions, etc., evolve during the time as a student, however, in the thesis, it would be nice to present the material uniformly. If all the material is stored on the hub in uhepp format, it's easy to rebrand, recolor the histogram.\n",
    "\n",
    "Sounds cool? Then sign up.\n",
    "\n",
    " - Go to [https://uhepp.org/login](https://uhepp.org/login) and login using your CERN account and CERN's Single Sign-On.\n",
    " - To use the API, you need an API token that is linked to your account. Go to [https://uhepp.org/tokens/new](https://uhepp.org/tokens/new) and create a token. Follow the instructions given on the token creation page. You need to add the token and the API endpoint to your environment variables.\n",
    " - Plots on the uhepp hub are organized in collections. We will push the toy plot from the previous section to a new collection. Navigate to [https://uhepp.org/c/new](https://uhepp.org/c/new) and create a new collection. On the collection page, you see the ID of the collection. We need to specify this ID when we want to push or pull plots. For this tutorial, we assume the ID is `1`.\n",
    " \n",
    "Now, you are ready to push your first plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "https://uhepp.org/p/a378d2b0-cde2-4266-be9b-85945d94880d"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist.push(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The return value of `push()` is a push receipt. By default, it renders to the URL where you can view the plot online. Every plot has a unique identifier (uuid). You can see the uuid in the URL. The push receipt gives you programmatic access to the generated uuid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a378d2b0-cde2-4266-be9b-85945d94880d'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "receipt = _18  # Take the return value of the previous cell\n",
    "receipt.uuid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have the uuid of a plot, you can also download it. The uhepp module provides the `pull()` method for this purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This could be a new shell again or a different compute\n",
    "import uhepp\n",
    "uuid = 'a378d2b0-cde2-4266-be9b-85945d94880d'\n",
    "hist = uhepp.pull(uuid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 360x360 with 2 Axes>,\n",
       " (<AxesSubplot:ylabel='Events / 20 GeV'>,\n",
       "  <AxesSubplot:xlabel='Mass $m$ / GeV'>))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist.render()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, we might decide that the binning should be a bit finer again, and signal should be `C1` orange instead of red. So let's change this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 360x360 with 2 Axes>,\n",
       " (<AxesSubplot:ylabel='Events / 10 GeV'>,\n",
       "  <AxesSubplot:xlabel='Mass $m$ / GeV'>))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist.rebin_edges = hist.bin_edges[::2]  # Drop only every second bin edges\n",
    "hist.stacks[0].content[1].color = 'C1'  # 0-th stack, 1-st stack-item\n",
    "hist.render()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "python",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "python3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}