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feat: add small tests (#73)
* add minimal ServiceX + coffea test * add servicex-databinder example * query examples for ServiceX
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‎.gitignore‎

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.ipynb_checkpoints
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servicex.yml
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servicex.yaml
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analyses/**/*.root
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analyses/**/*.pdf
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# CMS ttbar
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analyses/cms-open-data-ttbar/workspace.json
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analyses/cms-open-data-ttbar/dask-worker-space
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# dask
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dask-worker-space/

‎tests/README.md‎

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# tests
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This is a space to collect miscellaneous scripts / notebooks etc. that do not fit anywhere else but are useful for testing.

‎tests/coffea_ServiceX_minimal.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "c8dc945e-0f70-4542-8725-7fbfa7b47865",
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"metadata": {},
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"outputs": [],
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"source": [
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"import asyncio\n",
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"import time\n",
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"\n",
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"import awkward as ak\n",
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"from coffea.processor import servicex\n",
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"from func_adl import ObjectStream\n",
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"from func_adl_servicex import ServiceXSourceUpROOT\n",
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"import hist\n",
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"import matplotlib.pyplot as plt\n",
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"from servicex import ServiceXDataset"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8d8fd413-6c3e-46a0-9945-75a4c1bc6725",
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"metadata": {},
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"source": [
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"Configuration options: enable / disable `dask` and the use of caching with `ServiceX` (to force re-running transforms)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "9900ed24-64ac-4661-aa31-0f4714c7db4e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# enable Dask\n",
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"USE_DASK = False\n",
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"\n",
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"# ServiceX behavior: ignore cache with repeated queries\n",
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"SERVICEX_IGNORE_CACHE = True"
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]
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},
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{
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"cell_type": "markdown",
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"id": "545eacca-c610-4069-b96d-6c44d8083abf",
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"metadata": {},
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"source": [
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"The processor used here: select jets with $p_T > 25$ GeV and calculate $\\textrm{H}_\\textrm{T}^{\\textrm{had}}$ (scalar sum of jet $p_T$) as observable."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "d4e60d23-795c-4417-86c6-1696be3b65ec",
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"metadata": {},
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"outputs": [],
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"source": [
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"class TtbarAnalysis(servicex.Analysis):\n",
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" def __init__(self):\n",
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" self.hist = hist.Hist.new.Reg(50, 0, 1000, name=\"ht\", label=\"HT\").Weight()\n",
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"\n",
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" def process(self, events):\n",
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" histogram = self.hist.copy()\n",
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"\n",
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" # select jets with pT > 25 GeV\n",
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" selected_jets = events.jet[events.jet.pt > 25]\n",
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"\n",
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" # use HT (scalar sum of jet pT) as observable\n",
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" ht = ak.sum(selected_jets.pt, axis=-1)\n",
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" histogram.fill(ht=ht, weight=1.0)\n",
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"\n",
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" return histogram\n",
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"\n",
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" def postprocess(self, accumulator):\n",
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" return accumulator"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6702d39b-70b2-4252-a569-d9db01444469",
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"metadata": {},
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"source": [
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"Specify which data to process, using a small public file here taken from 2015 CMS Open Data."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "34588fe7-67dd-4b87-9306-b05334fc86d4",
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"metadata": {},
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"outputs": [],
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"source": [
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"# input data to process\n",
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"fileset = {\n",
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" \"ttbar\": {\n",
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" \"files\": [\"https://xrootd-local.unl.edu:1094//store/user/AGC/datasets/RunIIFall15MiniAODv2/TT_TuneCUETP8M1_13TeV-powheg-pythia8/MINIAODSIM//PU25nsData2015v1_76X_mcRun2_asymptotic_v12_ext3-v1/00000/00DF0A73-17C2-E511-B086-E41D2D08DE30.root\"],\n",
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" \"metadata\": {\"process\": \"ttbar\"}\n",
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" }\n",
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"}"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1bbe9b7b-19dd-4945-92e8-1757bb1b6d73",
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"metadata": {},
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"source": [
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"Set up the query: only requesting specific columns here without any filtering applied."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "13b93c01-24ae-49a3-a91b-3a432c7d2f2b",
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_query(source: ObjectStream) -> ObjectStream:\n",
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" \"\"\"Query for event / column selection: no filter, select single jet column\n",
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" \"\"\"\n",
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" return source.Select(lambda e: {\"jet_pt\": e.jet_pt})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2b55e5ac-885f-455c-a742-b30b364559c3",
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"metadata": {},
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"source": [
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"The following cell is mostly boilerplate, which can hopefully be improved in the future."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "4022b82b-1aee-43d8-8958-b6947e5ed975",
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"metadata": {},
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"outputs": [],
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"source": [
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"def make_datasource(fileset:dict, name: str, query: ObjectStream, ignore_cache: bool):\n",
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" \"\"\"Creates a ServiceX datasource for a particular Open Data file.\"\"\"\n",
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" datasets = [ServiceXDataset(fileset[name][\"files\"], backend_name=\"uproot\", ignore_cache=ignore_cache)]\n",
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" return servicex.DataSource(\n",
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" query=query, metadata=fileset[name][\"metadata\"], datasets=datasets\n",
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" )\n",
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"\n",
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"\n",
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"async def produce_all_histograms(fileset, query, procesor_class, use_dask=False, ignore_cache=False):\n",
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" \"\"\"Runs the histogram production, processing input files with ServiceX and\n",
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" producing histograms with coffea.\n",
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" \"\"\"\n",
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" # create the query\n",
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" ds = ServiceXSourceUpROOT(\"cernopendata://dummy\", \"events\", backend_name=\"uproot\")\n",
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" ds.return_qastle = True\n",
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" data_query = query(ds)\n",
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"\n",
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" # executor: local or Dask\n",
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" if not use_dask:\n",
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" executor = servicex.LocalExecutor()\n",
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" else:\n",
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" # for coffea-casa\n",
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" executor = servicex.DaskExecutor(client_addr=\"tls://localhost:8786\")\n",
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" # locally\n",
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" # executor = servicex.DaskExecutor()\n",
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"\n",
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" datasources = [\n",
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" make_datasource(fileset, ds_name, data_query, ignore_cache=ignore_cache)\n",
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" for ds_name in fileset.keys()\n",
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" ]\n",
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"\n",
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" # create the analysis processor\n",
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" analysis_processor = procesor_class()\n",
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"\n",
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" async def run_updates_stream(accumulator_stream):\n",
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" \"\"\"Run to get the last item in the stream\"\"\"\n",
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" coffea_info = None\n",
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" try:\n",
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" async for coffea_info in accumulator_stream:\n",
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" pass\n",
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" except Exception as e:\n",
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" raise Exception(f\"Failure while processing\") from e\n",
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" return coffea_info\n",
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"\n",
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" output = await asyncio.gather(\n",
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" *[\n",
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" run_updates_stream(executor.execute(analysis_processor, source, title=source.metadata['process']))\n",
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" for source in datasources\n",
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" ]\n",
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" )\n",
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"\n",
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" return output"
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]
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},
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{
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"cell_type": "markdown",
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"id": "908d64f0-bbfa-4f75-9da4-9b5ee3e1745e",
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"metadata": {},
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"source": [
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"Execute everything: query `ServiceX`, which sends columns back to `coffea` processors asynchronously, collect the aggregated histogram built by `coffea`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "5b1d63a1-d9e5-4f23-a709-a73eeb0460ab",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"ttbar: 0%| | 0/9000000000.0 [00:00]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"execution took 13.32 seconds\n"
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]
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}
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],
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"source": [
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"t0 = time.time()\n",
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"\n",
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"# in a notebook\n",
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"output = await produce_all_histograms(\n",
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" fileset, get_query, TtbarAnalysis, use_dask=USE_DASK, ignore_cache=SERVICEX_IGNORE_CACHE\n",
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")\n",
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"\n",
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"# as a script:\n",
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"# async def produce_all_the_histograms():\n",
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"# return await produce_all_histograms(\n",
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"# fileset, get_query, TtbarAnalysis, use_dask=USE_DASK, ignore_cache=SERVICEX_IGNORE_CACHE\n",
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"# )\n",
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"# output = asyncio.run(produce_all_the_histograms())\n",
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"\n",
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"print(f\"execution took {time.time()-t0:.2f} seconds\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "f9e969b9-9925-4d43-9d0f-201d1547681f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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",
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"output[0].plot(label=\"ttbar\")\n",
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"plt.legend();"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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