{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Reading data with coffea NanoEvents\n", "\n", "This is a rendered copy of [nanoevents.ipynb](https://github.com/scikit-hep/coffea/blob/master/binder/nanoevents.ipynb). You can optionally run it interactively on [binder at this link](https://mybinder.org/v2/gh/coffeateam/coffea/master?filepath=binder%2Fnanoevents.ipynb)\n", "\n", "NanoEvents is a Coffea utility to wrap flat nTuple structures (such as the CMS [NanoAOD](https://www.epj-conferences.org/articles/epjconf/pdf/2019/19/epjconf_chep2018_06021.pdf) format) into a single awkward array with appropriate object methods (such as Lorentz vector methods$^*$), cross references, and nested objects, all accessed in delayed$^\\dagger$ mode from the source ROOT TTree via uproot. The interpretation of the TTree data is configurable via [schema objects](https://coffea-hep.readthedocs.io/en/latest/modules/coffea.nanoevents.html#classes), which are community-supplied for various source file types. These schema objects allow a richer interpretation of the file contents than the `uproot` methods. Currently available schemas include:\n", "\n", " - `BaseSchema`, which provides a simple representation of the input TTree, where each branch is available verbatim as `events.branch_name`. Any branches that uproot supports at \"full speed\" (i.e. that are fully split and either flat or single-jagged) can be read by this schema;\n", " - `NanoAODSchema`, which is optimized to provide all methods and cross-references in CMS NanoAOD format;\n", " - `PFNanoAODSchema`, which builds a double-jagged particle flow candidate colllection `events.jet.constituents` from compatible PFNanoAOD input files;\n", " - `TreeMakerSchema` which is designed to read TTrees made by [TreeMaker](https://github.com/TreeMaker/TreeMaker), an alternative CMS nTuplization format;\n", " - `PHYSLITESchema`, for the ATLAS DAOD_PHYSLITE derivation, a compact centrally-produced data format similar to CMS NanoAOD; and\n", " - `DelphesSchema`, for reading Delphes fast simulation [nTuples](https://cp3.irmp.ucl.ac.be/projects/delphes/wiki/WorkBook/RootTreeDescription).\n", "\n", "We welcome contributions for new schemas, and can assist with the design of them.\n", "\n", "$^*$ Vector methods are currently made possible via the [coffea vector](https://coffea-hep.readthedocs.io/en/latest/modules/coffea.nanoevents.methods.vector.html) methods mixin class structure. In a future version of coffea, they will instead be provided by the dedicated scikit-hep [vector](https://vector.readthedocs.io/en/latest/) library, which provides a more rich feature set. The coffea vector methods predate the release of the vector library.\n", "\n", "$^\\dagger$ _delayed_ access refers to only fetching the needed data from the (possibly remote) file when you're ready to compute the entire set of outputs. Until then, a minimal amount of metadata is used to trace the operations to eventually be performed (frequently called \"lazily\" \"booking\" operations in RDataFrame's approach to this)\n", "\n", "\n", "In this demo, we will use NanoEvents to read a small CMS NanoAOD sample. The events object can be instantiated as follows:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [] }, "outputs": [], "source": [ "import awkward as ak\n", "from coffea.nanoevents import NanoEventsFactory, NanoAODSchema\n", "\n", "NanoAODSchema.warn_missing_crossrefs = False\n", "\n", "fname = \"coffea/tests/samples/nano_dy.root\"\n", "access_log = []\n", "events = NanoEventsFactory.from_root(\n", " {fname: \"Events\"},\n", " schemaclass=NanoAODSchema,\n", " metadata={\"dataset\": \"DYJets\"},\n", " mode=\"eager\",\n", " access_log=access_log,\n", ").events()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the factory constructor, we also pass the desired schema version (the latest version of NanoAOD can be built with `schemaclass=NanoAODSchema`) for this file and some extra metadata that we can later access with `events.metadata`. In a later example, we will show how to set up this metadata in coffea processors where the `events` object is pre-created for you. Consider looking at the [from_root](https://coffea-hep.readthedocs.io/en/latest/api/coffea.nanoevents.NanoEventsFactory.html#coffea.nanoevents.NanoEventsFactory.from_root) class method to see all optional arguments.\n", "\n", "The `events` object is an awkward array, which at its top level is a record array with one record for each \"collection\", where a collection is a grouping of fields (TBranches) based on the naming conventions of [NanoAODSchema](https://coffea-hep.readthedocs.io/en/latest/api/coffea.nanoevents.NanoAODSchema.html). For example, in the file we opened, the branches:\n", "```\n", "Generator_binvar\n", "Generator_scalePDF\n", "Generator_weight\n", "Generator_x1\n", "Generator_x2\n", "Generator_xpdf1\n", "Generator_xpdf2\n", "Generator_id1\n", "Generator_id2\n", "```\n", "are grouped into one sub-record named `Generator` which can be accessed using either getitem or getattr syntax, i.e. `events[\"Generator\"]` or `events.Generator`. e.g." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
[1,\n",
       " -1,\n",
       " -1,\n",
       " 21,\n",
       " 21,\n",
       " 4,\n",
       " 2,\n",
       " -2,\n",
       " 2,\n",
       " 1,\n",
       " ...,\n",
       " 1,\n",
       " -2,\n",
       " 2,\n",
       " 1,\n",
       " 2,\n",
       " -2,\n",
       " -1,\n",
       " 2,\n",
       " 1]\n",
       "-----\n",
       "backend: cpu\n",
       "nbytes: 160 B\n",
       "type: 40 * int32[parameters={"__doc__": "id of first parton", "typename": "int32_t"}]
" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "events.Generator.id1" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "['binvar', 'scalePDF', 'weight', 'x1', 'x2', 'xpdf1', 'xpdf2', 'id1', 'id2']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# all names can be listed with:\n", "events.Generator.fields" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In CMS NanoAOD, each TBranch has a self-documenting help string embedded in the title field, which is carried into the NanoEvents, e.g. executing the following cell should produce a help pop-up:\n", "```\n", "Type: Array\n", "String form: [1, -1, -1, 21, 21, 4, 2, -2, 2, 1, 3, 1, ... -1, -1, 1, -2, 2, 1, 2, -2, -1, 2, 1]\n", "Length: 40\n", "File: ~/src/awkward-1.0/awkward1/highlevel.py\n", "Docstring: id of first parton\n", "Class docstring: ...\n", "```\n", "where the `Docstring` shows information about the content of this array." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "\u001b[31mType:\u001b[39m Array\n", "\u001b[31mString form:\u001b[39m [1, -1, -1, 21, 21, 4, 2, -2, 2, 1, 3, ..., -1, 1, -2, 2, 1, 2, -2, -1, 2, 1]\n", "\u001b[31mLength:\u001b[39m 40\n", "\u001b[31mFile:\u001b[39m ~/Dropbox/work/pyhep_dev/awkward/src/awkward/highlevel.py\n", "\u001b[31mDocstring:\u001b[39m id of first parton\n", "\u001b[31mClass docstring:\u001b[39m\n", "Args:\n", " data (#ak.contents.Content, #ak.Array, `np.ndarray`, `cp.ndarray`, `pyarrow.*`, str, dict, or iterable):\n", " Data to wrap or convert into an array.\n", " - If a NumPy array, the regularity of its dimensions is preserved\n", " and the data are viewed, not copied.\n", " - CuPy arrays are treated the same way as NumPy arrays except that\n", " they default to `backend=\"cuda\"`, rather than `backend=\"cpu\"`.\n", " - If a pyarrow object, calls #ak.from_arrow, preserving as much\n", " metadata as possible, usually zero-copy.\n", " - If a dict of str → columns, combines the columns into an\n", " array of records (like Pandas's DataFrame constructor).\n", " - If a string, the data are assumed to be JSON.\n", " - If an iterable, calls #ak.from_iter, which assumes all dimensions\n", " have irregular lengths.\n", " behavior (None or dict): Custom #ak.behavior for this Array only.\n", " with_name (None or str): Gives tuples and records a name that can be\n", " used to override their behavior (see below).\n", " check_valid (bool): If True, verify that the #layout is valid.\n", " backend (None, `\"cpu\"`, `\"jax\"`, `\"cuda\"`): If `\"cpu\"`, the Array will be placed in\n", " main memory for use with other `\"cpu\"` Arrays and Records; if `\"cuda\"`,\n", " the Array will be placed in GPU global memory using CUDA; if `\"jax\"`, the structure\n", " is copied to the CPU for use with JAX. if None, the `data` are left untouched.\n", "\n", "High-level array that can contain data of any type.\n", "\n", "For most users, this is the only class in Awkward Array that matters: it\n", "is the entry point for data analysis with an emphasis on usability. It\n", "intentionally has a minimum of methods, preferring standalone functions\n", "like\n", "\n", " ak.num(array1)\n", " ak.combinations(array1)\n", " ak.cartesian([array1, array2])\n", " ak.zip({\"x\": array1, \"y\": array2, \"z\": array3})\n", "\n", "instead of bound methods like\n", "\n", " array1.num()\n", " array1.combinations()\n", " array1.cartesian([array2, array3])\n", " array1.zip(...) # ?\n", "\n", "because its namespace is valuable for domain-specific parameters and\n", "functionality. For example, if records contain a field named `\"num\"`,\n", "they can be accessed as\n", "\n", " array1.num\n", "\n", "instead of\n", "\n", " array1[\"num\"]\n", "\n", "without any confusion or interference from #ak.num. The same is true\n", "for domain-specific methods that have been attached to the data. For\n", "instance, an analysis of mailing addresses might have a function that\n", "computes zip codes, which can be attached to the data with a method\n", "like\n", "\n", " latlon.zip()\n", "\n", "without any confusion or interference from #ak.zip. Custom methods like\n", "this can be added with #ak.behavior, and so the namespace of Array\n", "attributes must be kept clear for such applications.\n", "\n", "See also #ak.Record.\n", "\n", "Interfaces to other libraries\n", "=============================\n", "\n", "NumPy\n", "*****\n", "\n", "When NumPy\n", "[universal functions](https://docs.scipy.org/doc/numpy/reference/ufuncs.html)\n", "(ufuncs) are applied to an ak.Array, they are passed through the Awkward\n", "data structure, applied to the numerical data at its leaves, and the output\n", "maintains the original structure.\n", "\n", "For example,\n", "\n", " >>> array = ak.Array([[1, 4, 9], [], [16, 25]])\n", " >>> np.sqrt(array)\n", " \n", "\n", "See also #ak.Array.__array_ufunc__.\n", "\n", "Some NumPy functions other than ufuncs are also handled properly in\n", "NumPy >= 1.17 (see\n", "[NEP 18](https://numpy.org/neps/nep-0018-array-function-protocol.html))\n", "and if an Awkward override exists. That is,\n", "\n", " np.concatenate\n", "\n", "can be used on an Awkward Array because\n", "\n", " ak.concatenate\n", "\n", "exists.\n", "\n", "Pandas\n", "******\n", "\n", "Ragged arrays (list type) can be converted into Pandas\n", "[MultiIndex](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html)\n", "rows and nested records can be converted into MultiIndex columns. If the\n", "Awkward Array has only one \"branch\" of nested lists (i.e. different record\n", "fields do not have different-length lists, but a single chain of lists-of-lists\n", "is okay), then it can be losslessly converted into a single DataFrame.\n", "Otherwise, multiple DataFrames are needed, though they can be merged (with a\n", "loss of information).\n", "\n", "The #ak.to_dataframe function performs this conversion; if `how=None`, it\n", "returns a list of DataFrames; otherwise, `how` is passed to `pd.merge` when\n", "merging the resultant DataFrames.\n", "\n", "Numba\n", "*****\n", "\n", "Arrays can be used in [Numba](http://numba.pydata.org/): they can be\n", "passed as arguments to a Numba-compiled function or returned as return\n", "values. The only limitation is that Awkward Arrays cannot be *created*\n", "inside the Numba-compiled function; to make outputs, consider\n", "#ak.ArrayBuilder.\n", "\n", "Arrow\n", "*****\n", "\n", "Arrays are convertible to and from [Apache Arrow](https://arrow.apache.org/),\n", "a standard for representing nested data structures in columnar arrays.\n", "See #ak.to_arrow and #ak.from_arrow.\n", "\n", "JAX\n", "********\n", "\n", "Derivatives of a calculation on an #ak.Array (s) can be calculated with\n", "[JAX](https://github.com/google/jax#readme), but only if the array\n", "functions in `ak` / `numpy` are used, not the functions in the `jax`\n", "library directly (apart from e.g. `jax.grad`).\n", "\n", "Like NumPy ufuncs, the function and its derivatives are evaluated on the\n", "numeric leaves of the data structure, maintaining structure in the output." ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "events.Generator.id1?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Based on a collection's name or contents, some collections acquire additional _methods_, which are extra features exposed by the code in the mixin classes of the `coffea.nanoevents.methods` modules. For example, although `events.GenJet` has the fields:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "['eta', 'mass', 'phi', 'pt', 'partonFlavour', 'hadronFlavour']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "events.GenJet.fields" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we can access additional attributes associated to each generated jet by virtue of the fact that they can be interpreted as [Lorentz vectors](https://coffea-hep.readthedocs.io/en/latest/api/coffea.nanoevents.methods.vector.LorentzVector.html#coffea.nanoevents.methods.vector.LorentzVector):" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
[[217, 670, 258],\n",
       " [34.5, 98.3, 1.16e+03, 38.1, 20.4, 29.7],\n",
       " [306, 62.8, 74.1, 769, 11.2],\n",
       " [170, 117, 29.3, 45.9],\n",
       " [101, 117, 129, 15.6],\n",
       " [63.1, 37.2, 33.7, 36.2],\n",
       " [303, 50.5, 1.29e+03, 278],\n",
       " [615, 282, 2.11e+03],\n",
       " [195, 47.6],\n",
       " [95, 44.6, 223, 318, 30, 108, 62.9],\n",
       " ...,\n",
       " [41.6, 36.7, 78.9, 13],\n",
       " [1.51e+03, 1.23e+03],\n",
       " [152, 160, 777, 27.1, 346, 65.1, 37.9, 27.2, 16.3],\n",
       " [35.4, 20.4],\n",
       " [20.1, 16.2],\n",
       " [34],\n",
       " [553, 283],\n",
       " [771, 452, 16],\n",
       " [76.9]]\n",
       "----------------------------------------------------\n",
       "backend: cpu\n",
       "nbytes: 836 B\n",
       "type: 40 * var * float32[parameters={"typename": "float[]"}]
" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "events.GenJet.energy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can call more complex methods, like computing the distance $\\Delta R = \\sqrt{\\Delta \\eta^2 + \\Delta \\phi ^2}$ between two LorentzVector objects:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
[[],\n",
       " [3.13],\n",
       " [3.45, 2.18],\n",
       " [1.58, 3.76],\n",
       " [],\n",
       " [0.053],\n",
       " [0.0748],\n",
       " [],\n",
       " [],\n",
       " [1.82],\n",
       " ...,\n",
       " [0.00115],\n",
       " [],\n",
       " [0.0149],\n",
       " [],\n",
       " [0.0308],\n",
       " [],\n",
       " [0.0858],\n",
       " [],\n",
       " []]\n",
       "--------------\n",
       "backend: cpu\n",
       "nbytes: 428 B\n",
       "type: 40 * var * float32[parameters={"typename": "float[]"}]
" ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# find distance between leading jet and all electrons in each event\n", "dr = events.Jet[:, 0].delta_r(events.Electron)\n", "dr" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
[None,\n",
       " 3.13,\n",
       " 2.18,\n",
       " 1.58,\n",
       " None,\n",
       " 0.053,\n",
       " 0.0748,\n",
       " None,\n",
       " None,\n",
       " 1.82,\n",
       " ...,\n",
       " 0.00115,\n",
       " None,\n",
       " 0.0149,\n",
       " None,\n",
       " 0.0308,\n",
       " None,\n",
       " 0.0858,\n",
       " None,\n",
       " None]\n",
       "---------\n",
       "backend: cpu\n",
       "nbytes: 200 B\n",
       "type: 40 * ?float32
" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# find minimum distance\n", "drmin = ak.min(dr, axis=1)\n", "drmin" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
[[None, None, None, None, None],\n",
       " [Electron, Electron, ..., Electron, Electron],\n",
       " [Electron, Electron, ..., Electron],\n",
       " [Electron, Electron, Electron],\n",
       " [None, None, None, None, None],\n",
       " [Electron, Electron, ..., Electron, Electron],\n",
       " [Electron, Electron, Electron, Electron],\n",
       " [None, None, None, None],\n",
       " [None],\n",
       " [Electron, Electron, ..., Electron],\n",
       " ...,\n",
       " [Electron, Electron],\n",
       " [None, None, None, None],\n",
       " [Electron, Electron, ..., Electron],\n",
       " [None, None, None],\n",
       " [Electron, Electron],\n",
       " [None, None, None],\n",
       " [{deltaEtaSC: -0.00986, dr03EcalRecHitSumEt: 0, ...}],\n",
       " [None, None, None, None, None, None],\n",
       " [None, None]]\n",
       "-------------------------------------------------------\n",
       "backend: cpu\n",
       "nbytes: 7.1 kB\n",
       "type: 40 * var * ?Electron[\n",
       "    deltaEtaSC: float32[parameters={"__doc__": "delta eta (SC,ele) with sign", "typename": "float[]"}],\n",
       "    dr03EcalRecHitSumEt: float32[parameters={"__doc__": "Non-PF Ecal isolation within a delta R cone of 0.3 with electron pt > 35 GeV", "typename": "float[]"}],\n",
       "    dr03HcalDepth1TowerSumEt: float32[parameters={"__doc__": "Non-PF Hcal isolation within a delta R cone of 0.3 with electron pt > 35 GeV", "typename": "float[]"}],\n",
       "    dr03TkSumPt: float32[parameters={"__doc__": "Non-PF track isolation within a delta R cone of 0.3 with electron pt > 35 GeV", "typename": "float[]"}],\n",
       "    dr03TkSumPtHEEP: float32[parameters={"__doc__": "Non-PF track isolation within a delta R cone of 0.3 with electron pt > 35 GeV used in HEEP ID", "typename": "float[]"}],\n",
       "    dxy: float32[parameters={"__doc__": "dxy (with sign) wrt first PV, in cm", "typename": "float[]"}],\n",
       "    dxyErr: float32[parameters={"__doc__": "dxy uncertainty, in cm", "typename": "float[]"}],\n",
       "    dz: float32[parameters={"__doc__": "dz (with sign) wrt first PV, in cm", "typename": "float[]"}],\n",
       "    dzErr: float32[parameters={"__doc__": "dz uncertainty, in cm", "typename": "float[]"}],\n",
       "    eCorr: float32[parameters={"__doc__": "ratio of the calibrated energy/miniaod energy", "typename": "float[]"}],\n",
       "    ...\n",
       "parameters={"__doc__": "slimmedElectrons after basic selection (pt > 5 )", "collection_name": "Electron"}]
" ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# a convenience method for this operation on all jets is available\n", "events.Jet.nearest(events.Electron)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The assignment of methods classes to collections is done inside the schema object during the initial creation of the array, governed by the awkward array's `__record__` parameter and the associated behavior. See [ak.behavior](https://awkward-array.readthedocs.io/en/latest/ak.behavior.html) for a more detailed explanation of array behaviors.\n", "\n", "Additional methods provide convenience functions for interpreting some branches, e.g. CMS NanoAOD packs several jet identification flag bits into a single integer, `jetId`. By implementing the bit-twiddling in the [Jet mixin](https://github.com/CoffeaTeam/coffea/blob/7045c06b9448d2be4315e65d432e6d8bd117d6d7/coffea/nanoevents/methods/nanoaod.py#L279-L282), the analsyis code becomes more clear:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[6, 6, 6, 6, 6], [6, 2, 6, 6, 6, 6, 6, 0], ..., [6, 6, 0, ..., 6, 6], [6, 6]]\n", "[[True, True, True, True, True], [True, True, ..., False], ..., [True, True]]\n" ] } ], "source": [ "print(events.Jet.jetId)\n", "print(events.Jet.isTight)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also define convenience functions to unpack and apply some mask to a set of flags, e.g. for generated particles:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Raw status flags: [[10625, 27009, 4481, 22913, 257, ..., 13884, 13884, 13884, 12876, 12876], ...]\n" ] }, { "data": { "text/html": [ "
[[True, True, False, False, False, ..., False, False, False, False, False],\n",
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       " [True, False, False, False, False, ..., False, False, False, False, False],\n",
       " [True, False, False, False, False, False, ..., True, True, True, False, False],\n",
       " [True, True, False, False, False, ..., False, False, False, False, False],\n",
       " [False, False, False, False, False, ..., True, False, False, False, False],\n",
       " [True, True, False, False, False, True, ..., True, True, False, False, False],\n",
       " [True, True, False, False, False, ..., False, False, False, False, False],\n",
       " [True, True, False, False, False, ..., False, False, False, False, False],\n",
       " ...,\n",
       " [True, True, False, False, False, ..., False, False, False, False, False],\n",
       " [True, True, False, False, False, True, ..., True, False, True, True, True],\n",
       " [False, False, False, False, False, ..., False, False, False, False, False],\n",
       " [True, True, False, False, False, ..., False, False, False, False, False],\n",
       " [True, True, False, False, False, ..., False, False, False, False, False],\n",
       " [True, True, False, False, False, ..., False, False, False, False, False],\n",
       " [True, True, False, True, True, True],\n",
       " [True, True, False, False, True, False, False, True, True, True],\n",
       " [True, True, False, False, False, ..., False, False, False, False, False]]\n",
       "--------------------------------------------------------------------------------\n",
       "backend: cpu\n",
       "nbytes: 1.4 kB\n",
       "type: 40 * var * bool[parameters={"__doc__": "gen status flags stored bitwise, bits are: 0 : isPrompt, 1 : isDecayedLeptonHadron, 2 : isTauDecayProduct, 3 : isPromptTauDecayProduct, 4 : isDirectTauDecayProduct, 5 : isDirectPromptTauDecayProduct, 6 : isDirectHadronDecayProduct, 7 : isHardProcess, 8 : fromHardProcess, 9 : isHardProcessTauDecayProduct, 10 : isDirectHardProcessTauDecayProduct, 11 : fromHardProcessBeforeFSR, 12 : isFirstCopy, 13 : isLastCopy, 14 : isLastCopyBeforeFSR, ", "typename": "int32_t[]"}]
" ], "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(f\"Raw status flags: {events.GenPart.statusFlags}\")\n", "events.GenPart.hasFlags([\"isPrompt\", \"isLastCopy\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "CMS NanoAOD also contains pre-computed cross-references for some types of collections. For example, there is a TBranch `Electron_genPartIdx` which indexes the `GenPart` collection per event to give the matched generated particle, and `-1` if no match is found. NanoEvents transforms these indices into an awkward _indexed array_ pointing to the collection, so that one can directly access the matched particle using getattr syntax:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
[[],\n",
       " [-11],\n",
       " [-11, 11],\n",
       " [22, None],\n",
       " [],\n",
       " [None],\n",
       " [None],\n",
       " [],\n",
       " [],\n",
       " [11],\n",
       " ...,\n",
       " [11],\n",
       " [],\n",
       " [11],\n",
       " [],\n",
       " [-11],\n",
       " [],\n",
       " [None],\n",
       " [],\n",
       " []]\n",
       "------------\n",
       "backend: cpu\n",
       "nbytes: 4.8 kB\n",
       "type: 40 * var * ?int32[parameters={"__doc__": "PDG id", "typename": "int32_t[]"}]
" ], "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "events.Electron.matched_gen.pdgId" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
[[84.4, 29.4],\n",
       " [31.1],\n",
       " [53.4, 81.9],\n",
       " [29.2],\n",
       " [17.5],\n",
       " [65.9, 47.8],\n",
       " [58.5, 44.7],\n",
       " [50.2, 45.2],\n",
       " [33.3, 25.9],\n",
       " [None],\n",
       " [26.1],\n",
       " [25.8]]\n",
       "--------------\n",
       "backend: cpu\n",
       "nbytes: 1,000 B\n",
       "type: 12 * var * ?float32[parameters={"__doc__": "pt", "typename": "float[]"}]
" ], "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "events.Muon[ak.num(events.Muon) > 0].matched_jet.pt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For generated particles, the parent index is similarly mapped:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
[[None, None, 1, 1, 23, 23, 23, 23, ..., 15, -15, -15, -15, -15, -15, 111, 111],\n",
       " [None, None, -1, 23, 23, 23, 23, ..., -11, None, None, None, None, None, 433],\n",
       " [None, None, -1, -1, 23, 23, 23, 23, ..., -423, -1, -1, -421, -421, 111, 111],\n",
       " [None, None, 21, 21, 23, -1, 23, 23, ..., -15, -15, -15, -15, -15, 111, 111],\n",
       " [None, None, 21, 21, 23, 23, 23, 23, ..., 13, 13, -13, 1, None, None, 2, 2],\n",
       " [None, None, 4, 23, 23, 23, 23, 23, ..., -15, -15, -15, 15, 15, 15, 423, 311],\n",
       " [None, None, 2, 2, 2, 23, 23, 2, 23, ..., -13, 13, 2, 2, 2, 2, 111, 111, 111],\n",
       " [None, None, -2, -2, 23, 21, 21, 23, 23, ..., 21, 21, 21, 21, None, 423, 2, 2],\n",
       " [None, None, 2, 23, 23, 23, 23, 23, ..., -15, -15, -15, -15, 111, 111, 311],\n",
       " [None, None, 1, 1, 1, 23, 21, 23, 23, 23, ..., -411, 21, 21, 1, 1, 1, 1, 3, 3],\n",
       " ...,\n",
       " [None, None, 1, 23, 23, 23, 23, 23, ..., -15, -15, -15, -15, 1, 1, 111, 111],\n",
       " [None, None, -2, 23, 23, 23, 23, 23, 13, 13, -13, -13, -13, 13],\n",
       " [None, None, 2, 2, 2, 23, 2, 23, ..., None, -413, 413, 413, 2, 2, -421, -421],\n",
       " [None, None, 1, 23, 23, 23, 23, 23, ..., -15, 15, 15, 15, -15, -15, -15, -15],\n",
       " [None, None, 2, 2, 23, 23, 21, 23, ..., 15, 15, 15, -15, -15, -15, 111, 111],\n",
       " [None, None, -2, 23, 23, None, 23, 23, ..., -15, -15, -15, 423, 4, 4, 3, 3],\n",
       " [None, None, -1, 23, 23, 23],\n",
       " [None, None, 2, 23, 23, 23, 23, -11, -11, 11],\n",
       " [None, None, 1, 1, 23, 23, 23, 23, ..., -15, -15, -15, 111, 111, 111, 111]]\n",
       "--------------------------------------------------------------------------------\n",
       "backend: cpu\n",
       "nbytes: 13.1 kB\n",
       "type: 40 * var * ?int32[parameters={"__doc__": "PDG id", "typename": "int32_t[]"}]
" ], "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "events.GenPart.parent.pdgId" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition, using the parent index, a helper method computes the inverse mapping, namely, `children`. As such, one can find particle siblings with:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
[[None, None, [23, 21], ..., [-16, 111, ..., 211, -211], [22, 22], [22, 22]],\n",
       " [None, None, [23], [23], [23], [23], ..., None, None, None, None, None, [431]],\n",
       " [None, None, [23, -1], [23, -1], [23], ..., [13, -14], [13, -14], [22], [22]],\n",
       " [None, None, [23, -1], ..., [-16, 111, ..., 211, -211], [22, 22], [22, 22]],\n",
       " [None, None, [23, 1], [23, 1], [23], ..., None, None, [11, -11], [11, -11]],\n",
       " [None, None, [23], [23], ..., [16, 13, -14], [16, 13, -14], [421], [310]],\n",
       " [None, None, [23, 2, 2], [23, 2, 2], ..., [...], [22], [11, -11], [11, -11]],\n",
       " [None, None, [23, 21], [23, 21], [23], ..., None, [421], [11, -11], [11, -11]],\n",
       " [None, None, [23], [23], ..., [-16, 111, ..., 311], [22, 22], [22, 22], [310]],\n",
       " [None, None, [23, 21, 21], [23, ...], ..., [11, -11], [11, -11], [11, -11]],\n",
       " ...,\n",
       " [None, None, [23], [23], [23], ..., [11, -11], [11, -11], [22, 22], [22, 22]],\n",
       " [None, None, [23], [23], [23], ..., [...], [-13], [-13, 22], [-13, 22], [13]],\n",
       " [None, None, [23, 2, 21], ..., [2, 21, ..., 11, -11], [13, -14], [13, -14]],\n",
       " [None, None, [23], ..., [...], [-16, 211, 211, -211], [-16, 211, 211, -211]],\n",
       " [None, None, [23, 21], [23, 21], ..., [-16, -11, 12], [22, 22], [22, 22]],\n",
       " [None, None, [23], [23], ..., [423, -421, 11, -11], [11, -11], [11, -11]],\n",
       " [None, None, [23], [23], [-13, 13], [-13, 13]],\n",
       " [None, None, [23], [23], [23], ..., [-11, 11], [-11, 22], [-11, 22], [11]],\n",
       " [None, None, [23, 21], [23, 21], ..., [22, ...], [22, 22], [22, 22], [22, 22]]]\n",
       "--------------------------------------------------------------------------------\n",
       "backend: cpu\n",
       "nbytes: 39.9 kB\n",
       "type: 40 * var * option[var * ?int32[parameters={"__doc__": "PDG id", "typename": "int32_t[]"}]]
" ], "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "events.GenPart.parent.children.pdgId\n", "# notice this is a doubly-jagged array" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since often one wants to shortcut repeated particles in a decay sequence, a helper method `distinctParent` is also available. Here we use it to find the parent particle ID for all prompt electrons:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
[[],\n",
       " [23, 23],\n",
       " [23, 23],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [23, 23],\n",
       " [],\n",
       " [23, 23],\n",
       " ...,\n",
       " [],\n",
       " [],\n",
       " [23, 23],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [23, 23],\n",
       " []]\n",
       "----------\n",
       "backend: cpu\n",
       "nbytes: 4.8 kB\n",
       "type: 40 * var * ?int32[parameters={"__doc__": "PDG id", "typename": "int32_t[]"}]
" ], "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "events.GenPart[(abs(events.GenPart.pdgId) == 11) & events.GenPart.hasFlags([\"isPrompt\", \"isLastCopy\"])].distinctParent.pdgId" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Events can be filtered like any other awkward array using boolean fancy-indexing" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
[94.6,\n",
       " 87.6,\n",
       " 88,\n",
       " 90.4,\n",
       " 89.1,\n",
       " 31.6]\n",
       "------\n",
       "backend: cpu\n",
       "nbytes: 24 B\n",
       "type: 6 * float32[parameters={"typename": "float[]"}]
" ], "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mmevents = events[ak.num(events.Muon) == 2]\n", "zmm = mmevents.Muon[:, 0] + mmevents.Muon[:, 1]\n", "zmm.mass" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
[94.6,\n",
       " 87.6,\n",
       " 88,\n",
       " 90.4,\n",
       " 89.1,\n",
       " 31.6]\n",
       "------\n",
       "backend: cpu\n",
       "nbytes: 24 B\n",
       "type: 6 * float32
" ], "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# a convenience method is available to sum vectors along an axis:\n", "mmevents.Muon.sum(axis=1).mass" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "As expected for this sample, most of the dimuon events have a pair invariant mass close to that of a Z boson. But what about the last event? Let's take a look at the generator information:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-13, 13]\n", "[False, False]\n" ] } ], "source": [ "print(mmevents[-1].Muon.matched_gen.pdgId)\n", "print(mmevents[-1].Muon.matched_gen.hasFlags([\"isPrompt\"]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So they are real generated muons, but they are not prompt (i.e. from the initial decay of a heavy resonance)\n", "\n", "Let's look at their parent particles:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
[-15,\n",
       " 15]\n",
       "-----\n",
       "backend: cpu\n",
       "nbytes: 4.3 kB\n",
       "type: 2 * ?int32[parameters={"__doc__": "PDG id", "typename": "int32_t[]"}]
" ], "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mmevents[-1].Muon.matched_gen.parent.pdgId" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "aha! They are muons coming from tau lepton decays, and hence a fair amount of the Z mass is carried away by the neutrinos:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "31.265434\n", "91.683304\n" ] } ], "source": [ "print(mmevents.Muon.matched_gen.sum().mass[-1])\n", "print(mmevents.Muon.matched_gen.parent.sum().mass[-1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One can assign new variables to the arrays, with some caveats:\n", "\n", " * Assignment must use setitem (`events[\"path\", \"to\", \"name\"] = value`)\n", " * Assignment to a sliced `events` won't be accessible from the original variable\n", " * New variables are not visible from cross-references" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
[[],\n",
       " [121],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " []]\n",
       "-------\n",
       "backend: cpu\n",
       "nbytes: 60 B\n",
       "type: 6 * var * float32[parameters={"typename": "float[]"}]
" ], "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mmevents[\"Electron\", \"myvariable\"] = mmevents.Electron.pt + zmm.mass\n", "mmevents.Electron.myvariable" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Virtual mode\n", "\n", "NanoEvents supports reading lazily in \"virtual\" mode. In that mode, branches will only be loaded from the root file when an explicit computation needs them. This is the default mode of operation of NanoEvents and we will be using this moving forward." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "fname = \"coffea/tests/samples/nano_dy.root\"\n", "access_log = []\n", "events = NanoEventsFactory.from_root(\n", " {fname: \"Events\"},\n", " schemaclass=NanoAODSchema,\n", " metadata={\"dataset\": \"DYJets\"},\n", " mode=\"virtual\",\n", " access_log=access_log,\n", ").events()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[None, None, None, None, None], [Electron, Electron, ..., Electron], ..., [None, None]]\n", "['nJet', 'nElectron', 'Jet_pt', 'Jet_phi', 'Jet_eta', 'Electron_pt', 'Electron_phi', 'Electron_eta']\n" ] } ], "source": [ "print(events.Jet.nearest(events.Electron))\n", "print(access_log)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
[??,\n",
       " ??,\n",
       " ??,\n",
       " ??,\n",
       " ??,\n",
       " ??,\n",
       " ??,\n",
       " ??,\n",
       " ??,\n",
       " ??,\n",
       " ...,\n",
       " ??,\n",
       " ??,\n",
       " ??,\n",
       " ??,\n",
       " ??,\n",
       " ??,\n",
       " ??,\n",
       " ??,\n",
       " ??]\n",
       "-----\n",
       "backend: cpu\n",
       "nbytes: 1.1 kB\n",
       "type: 40 * var * int32[parameters={"__doc__": "Jet ID flags bit1 is loose (always false in 2017 since it does not exist), bit2 is tight, bit3 is tightLepVeto", "typename": "int32_t[]"}]
" ], "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "events.Jet.jetId" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
[[True, True, True, True, True],\n",
       " [True, True, True, True, True, True, True, False],\n",
       " [True, True, True, True, True],\n",
       " [False, True, True],\n",
       " [True, True, True, True, True],\n",
       " [True, False, True, True, True, True, True, True],\n",
       " [True, False, True, True],\n",
       " [True, True, True, True],\n",
       " [True],\n",
       " [False, True, True, True, True, True, True, True, True],\n",
       " ...,\n",
       " [True, True],\n",
       " [True, True, True, True],\n",
       " [True, True, True, True, True, True, True, True, True],\n",
       " [True, True, True],\n",
       " [True, True],\n",
       " [True, True, True],\n",
       " [True],\n",
       " [True, True, False, True, True, True],\n",
       " [True, True]]\n",
       "---------------------------------------------------------\n",
       "backend: cpu\n",
       "nbytes: 516 B\n",
       "type: 40 * var * bool[parameters={"__doc__": "Jet ID flags bit1 is loose (always false in 2017 since it does not exist), bit2 is tight, bit3 is tightLepVeto", "typename": "int32_t[]"}]
" ], "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "events.Jet.isTight" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['nJet',\n", " 'nElectron',\n", " 'Jet_pt',\n", " 'Jet_phi',\n", " 'Jet_eta',\n", " 'Electron_pt',\n", " 'Electron_phi',\n", " 'Electron_eta',\n", " 'Jet_jetId']" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "access_log" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
[[6, 6, 6, 6, 6],\n",
       " [6, 2, 6, 6, 6, 6, 6, 0],\n",
       " [6, 2, 2, 6, 6],\n",
       " [0, 6, 6],\n",
       " [6, 2, 6, 6, 6],\n",
       " [6, 0, 2, 6, 6, 6, 6, 6],\n",
       " [6, 0, 6, 6],\n",
       " [6, 6, 6, 6],\n",
       " [6],\n",
       " [0, 2, 6, 6, 6, 6, 6, 6, 6],\n",
       " ...,\n",
       " [2, 6],\n",
       " [6, 6, 6, 6],\n",
       " [2, 6, 6, 6, 6, 6, 6, 6, 6],\n",
       " [6, 6, 6],\n",
       " [6, 6],\n",
       " [6, 6, 6],\n",
       " [6],\n",
       " [6, 6, 0, 6, 6, 6],\n",
       " [6, 6]]\n",
       "-----------------------------\n",
       "backend: cpu\n",
       "nbytes: 1.1 kB\n",
       "type: 40 * var * int32[parameters={"__doc__": "Jet ID flags bit1 is loose (always false in 2017 since it does not exist), bit2 is tight, bit3 is tightLepVeto", "typename": "int32_t[]"}]
" ], "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "events.Jet.jetId" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
[94.6,\n",
       " 87.6,\n",
       " 88,\n",
       " 90.4,\n",
       " 89.1,\n",
       " 31.6]\n",
       "------\n",
       "backend: cpu\n",
       "nbytes: 24 B\n",
       "type: 6 * float32[parameters={"typename": "float[]"}]
" ], "text/plain": [ "" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mmevents = events[ak.num(events.Muon) == 2]\n", "zmm = mmevents.Muon[:, 0] + mmevents.Muon[:, 1]\n", "zmm.mass" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['nJet',\n", " 'nElectron',\n", " 'Jet_pt',\n", " 'Jet_phi',\n", " 'Jet_eta',\n", " 'Electron_pt',\n", " 'Electron_phi',\n", " 'Electron_eta',\n", " 'Jet_jetId',\n", " 'nMuon',\n", " 'Muon_pt',\n", " 'Muon_phi',\n", " 'Muon_eta',\n", " 'Muon_mass',\n", " 'Muon_charge']" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "access_log" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `access_log` keeps track of the branch we load in a time-ordered manner. Notice how after every cell above, only the branches that are actually required to evaluate that cell are loaded." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dask mode\n", "\n", "Finally, NanoEvents supports \"dask\" mode. In that mode, the whole `events` object is a lazy `dask-awkward` (https://dask-awkward.readthedocs.io/en/stable/#) array. All the operations create a computational graph of \"to be evaluated\" steps that actually only run when `compute` gets called. Feel free to read about `dask` and `dask.array` too to understand general concepts of how it works." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "import dask\n", "\n", "fname = \"coffea/tests/samples/nano_dy.root\"\n", "events = NanoEventsFactory.from_root(\n", " {fname: \"Events\"},\n", " schemaclass=NanoAODSchema,\n", " metadata={\"dataset\": \"DYJets\"},\n", " mode=\"dask\",\n", ").events()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dask.awkward" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "events.Jet.nearest(events.Electron)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[6, 6, 6, 6, 6], [6, 2, 6, 6, 6, 6, 6, 0], ..., [6, 6, 0, ..., 6, 6], [6, 6]]\n", "[[True, True, True, True, True], [True, True, ..., False], ..., [True, True]]\n" ] } ], "source": [ "a, b = dask.compute(events.Jet.jetId, events.Jet.isTight)\n", "print(a)\n", "print(b)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "mmevents = events[ak.num(events.Muon) == 2]\n", "zmm = mmevents.Muon[:, 0] + mmevents.Muon[:, 1]\n", "zmm_mass = zmm.mass" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "zmm_mass.visualize(rankdir=\"LR\", optimize_graph=False)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from hist.dask import Hist\n", "\n", "h = Hist.new.Reg(4, 80, 100).Double().fill(zmm_mass)\n", "h.visualize(rankdir=\"LR\", optimize_graph=False)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[StairsArtists(stairs=, errorbar=, legend_artist=)]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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