Repository: scikit-hep/pylhe

Issue:
use scikit-hep.vector objects for momenta
I find myself often using the following syntax
```python
momentum = vector.Array(ak.flatten(array_from_lhe[<some selection>]['vector']))
```
so that I can use [vector](https://github.com/scikit-hep/vector)'s helper functions for calculating common variables.
I get the `array_from_lhe` by using `pylhe.to_awkward(pylhe.read_lhe(<some file>))`.

To me, from the outside, the existence of the `Momentum4D` record within the awkward array returned by `pylhe.to_awkward` seems to be extraneous especially since we have a module with a known awkward interface and designed for 4D vectors. Is replacing `Momentum4D` with vector's `Vector4D` something that is feasible? If this isn't feasible (or desirable) for some reason, this isn't too much boilerplate code to do what I want, I was just curious.

Discussion:
codecov[bot]: ## [Codecov](https://codecov.io/gh/scikit-hep/pylhe/pull/195?src=pr&el=h1&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=scikit-hep) Report
Patch coverage: **`100.00`**% and project coverage change: **`+0.05`** :tada:
> Comparison is base [(`5bb2356`)](https://codecov.io/gh/scikit-hep/pylhe/commit/5bb2356cd26b77b5f5174c40d4f17f68a415f50e?el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=scikit-hep) 88.07% compared to head [(`d8de28b`)](https://codecov.io/gh/scikit-hep/pylhe/pull/195?src=pr&el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=scikit-hep) 88.12%.

> :exclamation: Current head d8de28b differs from pull request most recent head ebc7922. Consider uploading reports for the commit ebc7922 to get more accurate results

<details><summary>Additional details and impacted files</summary>


```diff
@@            Coverage Diff             @@
##             main     #195      +/-   ##
==========================================
+ Coverage   88.07%   88.12%   +0.05%     
==========================================
  Files           2        2              
  Lines         218      219       +1     
  Branches       48       48              
==========================================
+ Hits          192      193       +1     
  Misses         23       23              
  Partials        3        3              
```

| Flag | Coverage Δ | |
|---|---|---|
| unittests-3.10 | `87.67% <100.00%> (+0.05%)` | :arrow_up: |
| unittests-3.7 | `87.38% <100.00%> (+0.05%)` | :arrow_up: |

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|---|---|---|
| [src/pylhe/awkward.py](https://codecov.io/gh/scikit-hep/pylhe/pull/195?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=scikit-hep#diff-c3JjL3B5bGhlL2F3a3dhcmQucHk=) | `100.00% <100.00%> (ø)` | |

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</details>

[:umbrella: View full report in Codecov by Sentry](https://codecov.io/gh/scikit-hep/pylhe/pull/195?src=pr&el=continue&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=scikit-hep).   
:loudspeaker: Do you have feedback about the report comment? [Let us know in this issue](https://about.codecov.io/codecov-pr-comment-feedback/?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=scikit-hep).

eduardo-rodrigues: This sounds very good. @tomeichlersmith, would it make sense to enhance the notebook example a little bit to show how easier it is now to perform calculations such as invariant masser or cone distances or ...?

Otherwise I'm ready to approve.

Many thanks.
tomeichlersmith: The `awkward_example` jupyter notebook already uses the vector method `mass` to calculate the invariant mass of two particles, but I could look at updating `zpeak`. Where is the `unweighted_events.lhe` file that is referenced in that example?
eduardo-rodrigues: Indeed - I should have checked.

The zpeak notebook needs to be updated since the LHE file it mentions is not in the package nor in scikit-hep-testdata, I recall. It would deserve a new test file ... In that respect, BTW, since you say you regularly use pylhe, would you happen to have interesting (small) files to enhance the test suite in scikit-hep-testdata? We are always keen on having better coverage.
tomeichlersmith: I just used the drell-yan test example for the zpeak and it appears to be the same file (the first two events are the same graphs as before).

I've already included my small and interesting file :smile: It was needed for PR #180 to show that a unregistered PDG ID can exist in the LHE file and the graph can still be rendered.
eduardo-rodrigues: Ah ha, thanks, I knew I had exchanged with you already in the past - too many repos and pull requests ;-).

Patch:
diff --git a/examples/awkward_example.ipynb b/examples/awkward_example.ipynb
index 116c90c..c8a4497 100644
--- a/examples/awkward_example.ipynb
+++ b/examples/awkward_example.ipynb
@@ -22,8 +22,58 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<pre>[{eventinfo: {nparticles: 4, pid: 1, ...}, particles: [...]},\n",
+       " {eventinfo: {nparticles: 5, pid: 1, ...}, particles: [...]},\n",
+       " {eventinfo: {nparticles: 5, pid: 1, ...}, particles: [...]},\n",
+       " {eventinfo: {nparticles: 4, pid: 1, ...}, particles: [...]},\n",
+       " {eventinfo: {nparticles: 4, pid: 1, ...}, particles: [...]},\n",
+       " {eventinfo: {nparticles: 4, pid: 1, ...}, particles: [...]},\n",
+       " {eventinfo: {nparticles: 5, pid: 1, ...}, particles: [...]},\n",
+       " {eventinfo: {nparticles: 4, pid: 1, ...}, particles: [...]},\n",
+       " {eventinfo: {nparticles: 5, pid: 1, ...}, particles: [...]},\n",
+       " {eventinfo: {nparticles: 4, pid: 1, ...}, particles: [...]},\n",
+       " ...,\n",
+       " {eventinfo: {nparticles: 4, pid: 1, ...}, particles: [...]},\n",
+       " {eventinfo: {nparticles: 4, pid: 1, ...}, particles: [...]},\n",
+       " {eventinfo: {nparticles: 4, pid: 1, ...}, particles: [...]},\n",
+       " {eventinfo: {nparticles: 4, pid: 1, ...}, particles: [...]},\n",
+       " {eventinfo: {nparticles: 5, pid: 1, ...}, particles: [...]},\n",
+       " {eventinfo: {nparticles: 4, pid: 1, ...}, particles: [...]},\n",
+       " {eventinfo: {nparticles: 4, pid: 1, ...}, particles: [...]},\n",
+       " {eventinfo: {nparticles: 4, pid: 1, ...}, particles: [...]},\n",
+       " {eventinfo: {nparticles: 5, pid: 1, ...}, particles: [...]}]\n",
+       "-------------------------------------------------------------\n",
+       "type: 10000 * Event[\n",
+       "    eventinfo: EventInfo[\n",
+       "        nparticles: float64,\n",
+       "        pid: float64,\n",
+       "        weight: float64,\n",
+       "        scale: float64,\n",
+       "        aqed: float64,\n",
+       "        aqcd: float64\n",
+       "    ],\n",
+       "    particles: var * Particle[\n",
+       "        vector: Momentum4D[\n",
+       "            x: float64,\n",
+       "            y: float64,\n",
+       "            z: float64,\n",
+       "            t: float64\n",
+       "        ],\n",
+       "        id: float64,\n",
+       "        status: float64,\n",
+       "        mother1: float64,\n",
+       "        mother2: float64,\n",
+       "        color1: float64,\n",
+       "        color2: float64,\n",
+       "        m: float64,\n",
+       "        lifetime: float64,\n",
+       "        spin: float64\n",
+       "    ]\n",
+       "]</pre>"
+      ],
       "text/plain": [
-       "<EventArray [{eventinfo: {nparticles: 4, ... ] type='10000 * Event[\"eventinfo\": ...'>"
+       "<EventArray [{eventinfo: {...}, ...}, ..., {...}] type='10000 * Event[event...'>"
       ]
      },
      "execution_count": 2,
@@ -37,7 +87,6 @@
     "\n",
     "lhe_file = data_path(\"pylhe-drell-yan-ll-lhe.gz\")\n",
     "\n",
-    "pylhe.register_awkward()\n",
     "arr = pylhe.to_awkward(pylhe.read_lhe_with_attributes(lhe_file))\n",
     "\n",
     "arr"
@@ -115,7 +164,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/examples/zpeak.ipynb b/examples/zpeak.ipynb
index bd1eb01..8c080da 100644
--- a/examples/zpeak.ipynb
+++ b/examples/zpeak.ipynb
@@ -27,16 +27,9 @@
     "import pylhe"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Prepare a histogram and a function to calculate the invariant mass of two particles."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -45,12 +38,22 @@
    },
    "outputs": [],
    "source": [
-    "h = hist.Hist.new.Reg(30, 50, 150).Int64()"
+    "# Use an example LHE file from package scikit-hep-testdata\n",
+    "from skhep_testdata import data_path\n",
+    "\n",
+    "lhe_file = data_path(\"pylhe-drell-yan-ll-lhe.gz\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Prepare a histogram to calculate the invariant mass of two particles."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -59,13 +62,7 @@
    },
    "outputs": [],
    "source": [
-    "def invariant_mass(p1, p2):\n",
-    "    return math.sqrt(\n",
-    "        sum(\n",
-    "            (1 if mu == \"e\" else -1) * (getattr(p1, mu) + getattr(p2, mu)) ** 2\n",
-    "            for mu in [\"e\", \"px\", \"py\", \"pz\"]\n",
-    "        )\n",
-    "    )"
+    "mass_hist = hist.Hist.new.Reg(30, 50, 150).Int64()"
    ]
   },
   {
@@ -77,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -86,26 +83,54 @@
    },
    "outputs": [
     {
-     "output_type": "execute_result",
      "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",
+       "50\n",
+       "</text>\n",
+       "<text text-anchor=\"middle\" x=\"250\" y=\"15\" style=\"fill:currentColor;\">\n",
+       "150\n",
+       "</text>\n",
+       "<text text-anchor=\"middle\" x=\"125.0\" y=\"15\" style=\"fill:currentColor;\">\n",
+       "Axis 0\n",
+       "</text>\n",
+       "<polyline points=\"  0,0   0,-1.61 8.33333,-1.61 8.33333,-1.22 16.6667,-1.22 16.6667,-1.27  25,-1.27  25,-1.13 33.3333,-1.13 33.3333,-0.851 41.6667,-0.851 41.6667,-1.04  50,-1.04  50,-1.06 58.3333,-1.06 58.3333,-1.43 66.6667,-1.43 66.6667,-1.96  75,-1.96  75,-3.22 83.3333,-3.22 83.3333,-6.6 91.6667,-6.6 91.6667,-30.7 100,-30.7 100,-100 108.333,-100 108.333,-17.3 116.667,-17.3 116.667,-4.16 125,-4.16 125,-2.53 133.333,-2.53 133.333,-1.1 141.667,-1.1 141.667,-0.989 150,-0.989 150,-0.46 158.333,-0.46 158.333,-0.529 166.667,-0.529 166.667,-0.345 175,-0.345 175,-0.299 183.333,-0.299 183.333,-0.184 191.667,-0.184 191.667,-0.276 200,-0.276 200,-0.115 208.333,-0.115 208.333,-0.115 216.667,-0.115 216.667,-0.184 225,-0.184 225,-0.092 233.333,-0.092 233.333,-0.069 241.667,-0.069 241.667,-0.046 250,-0.046 250,0\" style=\"fill:none; stroke:currentColor;\"/>\n",
+       "</svg>\n",
+       "</div>\n",
+       "<div style=\"flex=grow:1;\">\n",
+       "Regular(30, 50, 150, label='Axis 0')<br/>\n",
+       "<hr style=\"margin-top:.2em; margin-bottom:.2em;\"/>\n",
+       "Int64() Σ=13189080.0 <em>(16780000.0 with flow)</em>\n",
+       "\n",
+       "</div>\n",
+       "</div>\n",
+       "</html>"
+      ],
       "text/plain": [
        "Hist(Regular(30, 50, 150, label='Axis 0'), storage=Int64()) # Sum: 13189080.0 (16780000.0 with flow)"
-      ],
-      "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;\">\n50\n</text>\n<text text-anchor=\"middle\" x=\"250\" y=\"15\" style=\"fill:currentColor;\">\n150\n</text>\n<text text-anchor=\"middle\" x=\"125.0\" y=\"15\" style=\"fill:currentColor;\">\nAxis 0\n</text>\n<polyline points=\"  0,0   0,-1.61 8.33333,-1.61 8.33333,-1.22 16.6667,-1.22 16.6667,-1.27  25,-1.27  25,-1.13 33.3333,-1.13 33.3333,-0.851 41.6667,-0.851 41.6667,-1.04  50,-1.04  50,-1.06 58.3333,-1.06 58.3333,-1.43 66.6667,-1.43 66.6667,-1.96  75,-1.96  75,-3.22 83.3333,-3.22 83.3333,-6.6 91.6667,-6.6 91.6667,-30.7 100,-30.7 100,-100 108.333,-100 108.333,-17.3 116.667,-17.3 116.667,-4.16 125,-4.16 125,-2.53 133.333,-2.53 133.333,-1.1 141.667,-1.1 141.667,-0.989 150,-0.989 150,-0.46 158.333,-0.46 158.333,-0.529 166.667,-0.529 166.667,-0.345 175,-0.345 175,-0.299 183.333,-0.299 183.333,-0.184 191.667,-0.184 191.667,-0.276 200,-0.276 200,-0.115 208.333,-0.115 208.333,-0.115 216.667,-0.115 216.667,-0.184 225,-0.184 225,-0.092 233.333,-0.092 233.333,-0.069 241.667,-0.069 241.667,-0.046 250,-0.046 250,0\" style=\"fill:none; stroke:currentColor;\"/>\n</svg>\n</div>\n<div style=\"flex=grow:1;\">\nRegular(30, 50, 150, label='Axis 0')<br/>\n<hr style=\"margin-top:.2em; margin-bottom:.2em;\"/>\nInt64() Σ=13189080.0 <em>(16780000.0 with flow)</em>\n\n</div>\n</div>\n</html>"
+      ]
      },
+     "execution_count": 4,
      "metadata": {},
-     "execution_count": 20
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "for e in pylhe.read_lhe(\"./unweighted_events.lhe\"):\n",
-    "    h.fill(invariant_mass(e.particles[-1], e.particles[-2]), weight=e.eventinfo.weight)\n",
-    "h"
+    "events = pylhe.to_awkward(pylhe.read_lhe_with_attributes(lhe_file))\n",
+    "mass_hist.fill(\n",
+    "    (events.particles.vector[:, -1] + events.particles.vector[:, -2]).mass,\n",
+    "    weight=events.eventinfo.weight,\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -114,44 +139,34 @@
    },
    "outputs": [
     {
-     "output_type": "display_data",
      "data": {
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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
      },
-     "metadata": {
-      "needs_background": "light"
-     }
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "a = h.plot1d()\n",
-    "a[0].stairs.axes.set_yscale(\"log\")"
+    "artists = mass_hist.plot1d()\n",
+    "ax = artists[0].stairs.axes\n",
+    "ax.set_yscale(\"log\")\n",
+    "ax.set_xlabel(\"Mass [GeV]\")\n",
+    "ax.set_ylabel(\"Count\");"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "import itertools\n",
-    "\n",
-    "events = pylhe.read_lhe(\"./unweighted_events.lhe\")\n",
-    "for i, e in enumerate(itertools.islice(events, 0, 2)):\n",
-    "    filename = f\"event{i}.pdf\"\n",
-    "    pylhe.visualize(e, filename)"
+    "`pylhe` also has helpful graph representation of events so you can view what the LHE events you are studying look like."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -160,26 +175,90 @@
    },
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "<wand.image.Image: 9781212 'PDF' (267x229)>"
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
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\n"
+      "text/plain": [
+       "<pylhe.LHEEvent at 0x7ffb5203b790>"
+      ]
      },
+     "execution_count": 6,
      "metadata": {},
-     "execution_count": 30
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "import wand.image\n",
-    "\n",
-    "wand.image.Image(filename=\"event0.pdf\", resolution=150)"
+    "events = pylhe.read_lhe(lhe_file)\n",
+    "this_event = next(events)\n",
+    "this_event"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -188,27 +267,121 @@
    },
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "<wand.image.Image: 7457927 'PDF' (267x379)>"
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<!-- Generated by graphviz version 2.43.0 (0)\n",
+       " -->\n",
+       "<!-- Title: %3 Pages: 1 -->\n",
+       "<svg width=\"134pt\" height=\"195pt\"\n",
+       " viewBox=\"0.00 0.00 134.00 194.55\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 190.55)\">\n",
+       "<title>%3</title>\n",
+       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-190.55 130,-190.55 130,4 -4,4\"/>\n",
+       "<!-- 0 -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"27\" cy=\"-167.46\" rx=\"27\" ry=\"19.18\"/>\n",
+       "<text text-anchor=\"start\" x=\"24\" y=\"-163.26\" font-family=\"Times,serif\" font-size=\"14.00\">u</text>\n",
+       "</g>\n",
+       "<!-- 2 -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
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qyBNVs7NrU0RpMcVZ6lvoC303I9L5qpEstkAT7cx+egQtvnopH1u6W6j7ggOsinTKnevUmLKXxRizGDWw83SeDigjYZXiFuqx2quFv5H9ZXumMY2ZMOEXSjON0mJW+JtpiIlViluox+or2NYA1DFaL9vyzdROHM2aIkqWXo+0iIkVzyxMS0HJL9SRbyFLv4JA8Iahiw+LSyweV2jHxYrapRugjLHx4ICVG4m4MbJO8z1d3eL3Qf1Fs2vXcNguiq4OWDlarIHCTB44YL42BdYeW+U1UIyy5WqyA1YjadS7EM3rusvPgNX+rUBrpFP/hbiEnwGrw5sbgm/mjR5ooDXa+rE6vB1gjXTqP1ZYtgGOqx6r8QfaNtC5RANB1mhrx2r8kdvQrqTNDLw1eaobq/MPrfyMDZYhfVGgPeXasTr+uOpTeWSbhfbqQN3dHDLtiWqsTr/iBl/SE/9EiUIzVudfC55eKHig0e5jvVi99dWgrXLwQKO2+lqxuvRlgcEOvkcaI2jF6u0flHmUYrg50H3phoiyrhOr94PdpzcXE2yq0g05lK3wjdX7Pyz6MkZCMk+mLN0wKsWgBKvLP9/X3cp8No+izyy5SXdZdGH10UEyKfh00ce6L3ORuHSDJqw+PtC+vMA2x8RAo4p9GXinB6trDrdvOTOp6/mGX9tc/NKC1bWHzaQK8CjMfTXgbW7FJTVgdcvhDzXDV2mHYkwGTzKtsy4dq9tPsq/YvWCSoJx8yWwEeNbDQclY3X6ycTH3LfNY8wQUTIeaxJbXHORZL1b3nbZkOvDQsmEW9r5lMvShLZaFpCoeMrF6xIFqdC9YXplR+5VKKibMj86RQIw9RSIKK2Mf3IhkGmrG+9E6WjrW9u7Nu6akoqI62v7Y8cIyeupGz2Ya6qN0BNli9TAtAExBQ019kn+hpaWjBbqPnDQ1UPQhnh5lzTLKDswoJgMrL7QYDlZSU55wd+z0jvEm4YISzkJz9h9+xf/yP7RKU05jSv7In979+Bases3xiZVXWowOW1FR3pw9d0dLy5qWhjnw3b4EcS8LMytqdnyjfAyrEDdOIFocncJx3CnIceg7OqCjg9PwzIaSzn4L7l4iMzUr4Bd7VGvgPqwCWOo++rs9a4X1wI7iy3rfewS1uNc064VpWJBxIxk7L+ct9gyUSpOxzIAuV1JkTQv7QofL0KLMzIQScq5glHEjMnTLspXau+PKvnnMWC2GQivK9CJ/rchcLd4bxsm2/LUic7U46IWehPEStCJ7WuAALBQ1JC7JYea9pexp0evFRIdeyNAKAbRAkV6Yghk88iA9lgmghSK9mFIgQCtE0AIlemEKJojQCiG0sGtekK8XYrRCCC1QoBeStEIMLWzX60X8OsK+TJBWZD/LOXK0ZAO82O+pPbnL+4INBWv7lNqT60yIWojXC6cVYigthhbsBVhg/6LvV7zI2dAgiBaC9WImqV8BomiBUL0wpTStEEYL2/GMPL1wNBakFcJoATyzQ5ReSNQKcbSwO3H9C4FaIWjeYnBY1AyA1NkWYWox6EVtZJTznQI7aVohUC0k6UWvFc/2l9Se3Gri1EKUXswQqRUiaYGQ8YipaYAXiakYRNJCiF4I7VeAUFoAz7lvRDQ1NUK1QiwtrLsLc9YLwVohlha5b1yWrRWCaQG9XuSZ6EC0Voictxg5n+luTvk77SWrRb6JUaZkndTkYxOtFnnqRe4JkK4x2WqRp15kngDpGhNOi/zGI1I2H38QhexGJL/uXY7N2u0mXC1yS6SkQysUqMWgF1k8vtahFQrUIqdEB71WZEDPR00BLchn47JLgLRM7cbjpoIWeeiFln4FKKEFGeiFnARI15gSWmSgF6ISFXxkSmhBYr2QldTkY1NDi8SJlFRphSJakFAvtGmFKlr0iQ6mCTYiKtMKVbQgUaKDXiuWerRCGS0SJUZxWqFgbvNgqmhBAr2QlwDpGlNGiwR6IS4B0jWmjBZE1guZSU0+NnW0iJxISWRSk49NHS2IuHFZq1aopMWQSOm2muX3mFKtULE6642goiRGkZoA6RpTqBbREh24DYWq5iv2ppIWROhfDP0KoZuPL5tSWhzrhc9exqBAYhMgXWNKacGxXvjcdVabWnYCpKvMqv1jhsVSU2M9HnXKysIKy5YidYzBsEvtQNDgNlj+ydYrLWosf8dimaWOL9yfykbEFGZialPwN+D3+F2vVQF/AP7NX01latOkjjaEfU3tQAizO7NmTpjh6Z5kv+ZfZLKXzb+pVAuwnX0azygEmcFo+UknKdTSAsA+8zP+Rwr7wW7Lk21TxxjKFNMC7JKnnhj+1MLR4sX+pHZwinJagG17Ynh6zG5KCpQ+Bxmbyi7n2GxrnljxxjynqSh4T0c6OqA9U4SKT0AKpU9Qz4KsWfHNdqagpqKivEk9Wlo6WrsGM6PmSXPz4ewT0MIUVPyZ31C8Q4aWc014e65jze/4C//Q29Xcm2pamIaa+oQMO1padrRAd2ldlamBggqozxqhNWuW+lZlDbHrpIWpaJgc3fNrWlraey+lqakoqY4I0vLCUmOToo4WpqRhMihEx5KO1qfsG6dAh0nvJUsNGXCOLPVDGY8PsQomrIZ/t8xpgp5tyuvR2arUCHiMLrUD3gKZsh3+WTCJ89Cbkhmb4d+VFmokd8BLEM1waV6Zxl8FQcV8IOWcMjUeP2gB9dBwrKgT+lEwG6gxk75AJ7kDDzlfsuhfbpik9qanhnu5ZZram09Ji1wvwRFVE6rXp6QFxTAKyFCwRw1bRoRVTwuqvhVf5Nu9o+l9nOdHW5W0YNK/yHyJLWWvaK/5klcNLZhjsWxDTlV587UYvBXWy0juwI0w7+8/MdNGTPsXGYyUVNJiIMVKVms99DIyb/Sk0uJVGriD51XvuxjFSO7A1Y66VlogKSwWCrZYtlIav+QOXOmmG32sUvvxQARuUL2R0QAmd+BKSC2WVxmQvhuFo/Zraj+u8jW1A1e4WMoS4AuRuOn6eWo/dNDCddcEzFNcEYubFM++65ncgQ8ddNIr9NnCWTRukJ19ZozkDnzo4AbRXc2zeGos2etF5psN+2qBihbQ5lXl+T3LnBZMgC6X+ume7AUojc98Xt4ta1oYt21HWTY7u6Qjc73ImhZM0acV4IheBk4m+5BlvH3I1KxIlIbI1NTAzgZSKrOhDJ18+iFL3ee90GefkWgoR8PCYmESauqpf9ye7TA1Z7VYUbO0Pwc8Q3G25bizHZgN311BOrPlpxAbkPs08j/nukkx57QnFRB2T/gWaNlXJCxpaPnJFJTDeVuaEF1e25kdBVWuQ+9sadGnIwpYRNI07A5p0UzJKy1PQAUjhQhVhbl9Iz1CNpbvSMT100MmGKlHpChYsIua56aFH7S43UqgC3qZdiNSrCj5OWqmihYoElR4vsrypUX4nsWhXZ9THpTDrkfpXYtgPjgKZkqLbPsW4W3QijkN348Soyzd5TIFZa6dwrD2iWnhzMyY8P1kJvU7C7OjZcovGlMgfWyfnBZmwpTn0+l1u+PJ1JSflRQ59y1agre8ZsKcl/3k+vEzCru26wikyDRXX7606IDShJo1AEzFjOUoI2/cR1fumcsPWtxorgsYbGRvKlZ0DKQwdeQ7tyLsrMxDli0t+pFBoDvYFCzoDtNXpmAWeczxgxZ32ppAvQtTsKI4TF+ZkkXgqbNTD9zUfra0yHkk0lJTmyLA5ZpT0TE37j+XGD5u1v/wU/uPWeon+xfWJIhYI31nbJmvZ8+4EZGxRvoey389e8a0QMQa6busIfM1qlnTol8jrYwW/TrRrNezZ00LHHhVzmuk77ApsMtZK7KnhX3Jf0/FbWYqalzzmLFlTgscgLWW/oUpmAO7vJsQch6g9kO5fSI18dktLEOqp+z33yd34AoXqz7tibikp2eRuN0hP9KeeHKywSI/SZKgKJI7cKWbYtILvRuBKM1L7sDVjgppld/xXlgPKbkDNwMrkBiD72Ke7yR34AZXS5mlGIYyF4KawOQO3EgMcaUYpJS5EEyLo1IMIlppSWUuBNPCMoxKsm+pKfosnBsZFBZOCxkFn6j72qzCylwIpsWoFMM2xzZ7VN0wQ+8U02LUy8ikBuobfm1z8uuT0MKST8XkESX2lZO3ORbi/CS0sBzVV1+lHbYyGTwRX2c9uQOeLsi+uvmCSfy7lJLZiJzixh1KaXFS+H7LPNY8AQXToX6z5TWHpuwHLY5DKZkO96xlwyzsfctkGG9YLAtpU1aX/jLOy3mfmYqGyZAfr2XNmtbnzjRTUVHRjM7xwlJXJgx1tOjDaqhHFw46WjrW9u6EjqbE0WG8Cr3jhWWuyQgeMaW0ADAFDTX1SWbNlpaOlj5j78Uj1EDR0+H0KGuWNt89pA+aYloMIZb8kT+9+3HLjvEm4YISzmjg7D/8iv/jv/02SjlazjvWPZntzH8BO75RUlGeZM91ry9tUNrR0rKmpWHObynvb4qk2GdQC1eA4iSbnnF64OhwnHNnRwd0dHCqC2ZDSWe/pY4puKUeCoX/Y4Vl421Qasn+kb6HOFM7EDxAzxeSDd5Ilu9f/psNHzXfha1cQSklmx/fM+W06BOMeNzxqW+z9FumvMsZoov4dhdWl6lWC/9aAZqTNx1MtVqEGk6mrLkYxxSrRa8VAS5drxeTkKmn05piWjAF1oFqBz7j1looNbW0MNMQ/Yq9adcLpbQwRa8V4Z5eqNYLpbRgSkHQJIe69UIlLUzBhLBaAar1QiUtwmsFgF3zglK9UEiLSFoBivVCIS3iaAWA7XgBprkWub3f1NGi14plpBVUjnzq9EIdLXqtiDQt3evFRJteKKOFKZkALxEX6avUC2W0iNev2JtOvVBFiwRaASr1QhUt+ksTOfu+7XhGmV4ookUirQB4ZocqvVBEizRaAWB3ff8iWI3n2KZmdZYp2QAvNm490/3ZCzYUrO1Tahz8mB61mAK7WPMVp9brRa2lqpoSWgz9inRbhlX1L5TQghmJK4Dp0gsVtDA1DWm1AlTphQpa9P2KxNUCNemFAlr0VYhTawXAs5aNiApokYdWAFjnhQK9EE+LjLSCYePyLLUfj5p4WuSjFb25qvDCEx0In+XMcfe4hkRK0tXCd1ITH6YhMUrqdDw5JUDy5pf4REqy1SJHrQAFeiGYFmGSmvgw+YmUBHc5c+7a5dgVvsXEqkW+WgHyEymJVYuctQKkJ1ISqhbhEiD5MtmJDoTSImgCJF8meOOySFqETYDkyyTrhUBaREiA5MvE6oVAWsTfUHivydULcbSImNTEhwnVC3G0kKMVIDeRkjBaCNMKEKoXwmghSytAaiIlUbSInADJlwlMdCCKFnETIPkyiYlRBNEiYaKCR02cXgiihbx+xd7k6YUYWgjWChCnF2JokS6piQ+TlkhJCC2EawUI27gshBaytQKkJVISsTorbQIkb1EISqQkQy2SJkDyZZISHQigRQYJkHyZmP5FxrQYWuE5eW0+vtuO9SLnUUnGtKAxdW6JCh62sV7krBqpdzte2Mk5ZWVhhWVLkdobb1HNsFhqamxqXy54mdqBC67VWP6OxTJL7YvHqAo2WP7JNmda5NyIVMAfgH/zV1MaET34S2YKMzE18Dfg92S9Xutragcu2B64X/MvxO7POpjdmTVzRJA7Z7U43E8tP0knBYDt7NN4RJWv/uVMi/0AruXJtqmd8WX2F34m+1FVzrRw8xYv9iclg9Pe7JKnnhg/1OJWMyUF4p+DvG227YmR7YRWlC6nqSh4797o6ID2TBEqlJICwLbmiRVvPE29CyvvFugJqimoqKkob7oj1nR0tHYNZkbNk67m49hMzYpvtnscqwC++aRFH2BJ9U6ALeea8Pb4fc3v+Av/0NPVPDVTUPFnfkPhAauWjtYnVp5oYRpqqhNR3NHSsqMFukvrqkwNFFQUZ8eANWuWgldlicTqYVqYiobJEY/XtLS097pnnKAeB71myVJ6kyIHqwdoYUoaJoMEdiz9StnQJDXDW0uWmWfAeS8WaVjd+bhnwmr4d8ucJujDpSmvR2erUj9K0o7VPT+Zsh3+WTCJ89Cbkhmb4d+VDGpIxerWrzfD6V6Zxl8FQc18AHpOmfqya8XqttPsxXBFnRDuglkf7pZZngt0pGN17ddKFv3LTQ4Z+SmY9y+3TFN7ow+r6w47y/ESHMGf8I488UoFVteQYt+zzVCwR2KdwUXQg9VHH1d9y7TIt3vHpPdxnvZSaMLqo4O4F5kvsaXs79LXdBdEF1aXDuA6KtuQ0y/egi0Gb5P0MrRh9f5P95wSMW1kGXX2Ivf+NWJ1OdBVfh2ni8E2fcsZUch1YvX2T15jg+st2Kr3PZpi6MTqra/PpQZqsVCwxbKNI+hasTr/qutRr1K7/ECwbqC4CS/qerE6/5rF8iqrnXzncr0Gh1QtVsf/lDEFOGiwrqc9D3gG1Vgdf8F1QQSMva8I1k30But66sZq/KGTkwyeLXgJ1Q0cA2XG0I7V+MMNortPZ8HWWALphXasDh9MwoGYLNgVlk2A46rH6vD2axgIk4bahLl8+rHavxlQcJMGu/F/AT8DVvu3AsntFc4EbaF7uff6VDUdVoGvxAgr94bjf5Ip3NAdN9+dw5RYBb8WA1Yuv0WNkoSob5hLkOovgdmnwMrRogLW0nd4vmNuw52/vDOfAquDWijaEz4227ED/JVrqPgEWH0dcmtHLSJpFsO+7cqs+ldtoGx6LbUvWvSpm+JiVb+RBvol0BbtPVZDDzTgc8BLxw4/V8gMy9bTsZqUWEXodPZYfcEl9urCtZamZju8nppFqPNcuAOg8JSPv+JTYPWF8K1lzSGPQ5OgXXYX0SMtAvqaCVYxEjDW+7bYFFSozYblxTLBKjgtjsKrOe+u/aDJYB9iFc3Cq8U4vOo8p6SGXN7e7AOs4tkX+m5GsDOMw6tZg8cZx1vMTzvdEjL3bjZYfcEBVgVzoN7nlxwkMnYxphrYeUrh2AGlfqz2auFzHnBkphgddwa0poyeH7/CXw/mk2D1FWxrYNQH9mo1UJoJHTUvVDQU0R8zeaTFp8HKzTRiWQSaNXu1UO2XzVPHnsWjxOJxhfbnwMqNRFpC1baoWYMdEpPa+M8e6yFCP9bi90H92NN8sOp5aQmx5rHweZ/e6UOYZTjKsdq/EWQhGjVBHytd4UGANdqfAauA8FkokvM/wCUMs548L6wOb24Ivpk3eqChBF89Voe3A6yRTv3nCnEHOK56rMYfaNtA5xINBFmjrR2r8UduQ7uSbTGBtyYrx+r8Qys/Y4MldPoi7Vgdf1z1qTyyzUJ7daBTLGHTnqjG6vQrbvAlPfFPlCg0Y3X+teDphYIHGu0+1ovVW191LY3QTC9xW32tWF36ssBgB98jjRG0YvX2D8o8SjHcHOi+dENEWdeJ1fvB7tObiwk2VekGjVi9/8OiL2MkJPNkytIN+rC6/PNEpRjuuCwus+Qm3WXRhdVHB2nybzmp+3qjiUs3aMLq4wPtywtkValv8O5QsS+DtEV6sLrmcEVedT1Hfh3qe2bi18gn0Vhde9hMqgCPwsy2crIGrG45/KFmeOKSjkwGTzKtsy4dq1tPMh2KuS9SiCQlsyHMVd7DwaHmqECsbj/ZQZIsW+ax5gkomA41iS2vOcizXqzuO23JdOChZcMs7H3LZOj0WiyL1Guk9WP1iAPV6F6wvDLzvT2Oignzo3NM8upeasXK2Af3p5mGmsnojY6WltbevXnXlFRUVEfbHzuWvHhKRpDM5GD1MC0ATEFDTX2SPKWlpaMFuo+cNDVQ9CGeHmXN0qpJpSQDKy+0GA5WUlOecHfs9I7xJuGCEs5Cc9bR0rFOmSgorOWNlVdajA5bUVHenD1350RVbU7tNy1HrALR4iTsgv0m+ePQd3RARwd6deEWywWr/wclRWd17t5BvQAAACV0RVh0ZGF0ZTpjcmVhdGUAMjAyMS0wNC0xN1QxODoyMzozNiswMjowMLuC9m0AAAAldEVYdGRhdGU6bW9kaWZ5ADIwMjEtMDQtMTdUMTg6MjM6MzYrMDI6MDDK307RAAAAFHRFWHRwZGY6VmVyc2lvbgBQREYtMS41IAVcCzkAAABKdEVYdHNpZ25hdHVyZQA3NDU3OTI3NzVmODU0OWIxOWU2YzNkYWY2ZDY0MGYyNzI2MDc4MTk2NzRjYzY4YzNkNDU0MDAwM2U1NmU3ZGQw5HBbGAAAAABJRU5ErkJggg==\n"
+      "text/plain": [
+       "<pylhe.LHEEvent at 0x7ffb52600df0>"
+      ]
      },
+     "execution_count": 7,
      "metadata": {},
-     "execution_count": 31
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "wand.image.Image(filename=\"event1.pdf\", resolution=150)"
+    "this_event = next(events)\n",
+    "this_event"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "You can also render these graphs into a PDF file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'z0-event.pdf'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "this_event.graph.render(filename=\"z0-event\", format=\"pdf\", cleanup=True)"
+   ]
   }
  ],
  "metadata": {
diff --git a/src/pylhe/awkward.py b/src/pylhe/awkward.py
index 4d522e4..c42d160 100644
--- a/src/pylhe/awkward.py
+++ b/src/pylhe/awkward.py
@@ -10,12 +10,21 @@ def __dir__():
 
 
 def register_awkward():
-    """Register Awkward behaviors for pylhe."""
+    """
+    .. deprecated:: 0.6.0
+       Remove use of :func:`~pylhe.awkward.register_awkward` as registration
+       is automatic.
+    .. warning:: :func:`~pylhe.awkward.register_awkward` will be removed in
+     ``pylhe`` ``v0.8.0``.
+    """
+    import warnings
 
-    vector.register_awkward()
-    ak.mixin_class(ak.behavior)(Particle)
-    ak.mixin_class(ak.behavior)(Event)
-    ak.mixin_class(ak.behavior)(EventInfo)
+    warnings.warn(
+        "pylhe.awkward.register_awkward is deprecated as of pylhe v0.6.0 and will be removed in pylhe v0.8.0."
+        + " Please remove use of pylhe.awkward.register_awkward in favor of automatic registration.",
+        category=DeprecationWarning,
+        stacklevel=2,  # Raise to user level
+    )
 
 
 def to_awkward(event_iterable):
@@ -58,3 +67,10 @@ class Event:
 
 class EventInfo:
     pass
+
+
+# Register Awkward behaviors
+vector.register_awkward()
+ak.mixin_class(ak.behavior)(Particle)
+ak.mixin_class(ak.behavior)(Event)
+ak.mixin_class(ak.behavior)(EventInfo)


Test patch:
diff --git a/tests/test_api.py b/tests/test_api.py
index 39d1f10..d390635 100644
--- a/tests/test_api.py
+++ b/tests/test_api.py
@@ -33,5 +33,10 @@ def test_awkward_api():
     assert dir(pylhe.awkward) == ["register_awkward", "to_awkward"]
 
 
+def test_deprecated_api_warning():
+    with pytest.warns(DeprecationWarning):
+        pylhe.register_awkward()
+
+
 def test_load_version():
     assert pylhe.__version__
diff --git a/tests/test_awkward.py b/tests/test_awkward.py
index a9c7c49..0c9bfd9 100644
--- a/tests/test_awkward.py
+++ b/tests/test_awkward.py
@@ -13,3 +13,8 @@ def test_to_awkward():
     arr = pylhe.to_awkward(pylhe.read_lhe_with_attributes(TEST_FILE))
     assert len(arr) == 791
     assert len(arr.eventinfo.nparticles) == len(arr.particles)
+
+
+def test_awkward_registration():
+    arr = pylhe.to_awkward(pylhe.read_lhe_with_attributes(TEST_FILE))
+    assert len(arr.particles.vector.mass) == len(arr.particles)