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import uproot
import awkward
import hist
import numpy as np
import os
import math

def get_root_parent(index, mcparticles):
         parents = mcparticles[index].member("parents").tolist()
         if len(parents) == 0:

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import awkward
import uproot
import glob
from rich.progress import track
import multiprocessing
import concurrent.futures
import pathlib

def extract_file(c3FileName, keep):
    c3File = uproot.open(c3FileName)

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Every 1.0s: zpool iostat -vly 1 1                                                                                                                                                              godrick: Mon Sep 26 19:53:16 2022

                                                   capacity     operations     bandwidth    total_wait     disk_wait    syncq_wait    asyncq_wait  scrub   trim
pool                                             alloc   free   read  write   read  write   read  write   read  write   read  write   read  write   wait   wait
-----------------------------------------------  -----  -----  -----  -----  -----  -----  -----  -----  -----  -----  -----  -----  -----  -----  -----  -----
chrysalid                                        3.81T  10.7T     13    667  1.74M  28.3M    2ms    7ms    2ms  502us    2us    1us    2us    7ms      -      -
  mirror-0                                       1.91T  5.35T      2    295   383K  13.1M    2ms    8ms    2ms  464us    2us    1us      -    7ms      -      

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# ==========================================================================
#  AIDA Detector description implementation
# --------------------------------------------------------------------------
# Copyright (C) Organisation europeenne pour la Recherche nucleaire (CERN)
# All rights reserved.
#
# For the licensing terms see $DD4hepINSTALL/LICENSE.
# For the list of contributors see $DD4hepINSTALL/doc/CREDITS.
#
# ==========================================================================

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import random
from dask.distributed import Client, worker_client, as_completed, secede, rejoin
import pprint
import time

def reduce_chunks(items):
    out = 0
    for item in items:
        out += item #.result()
    return out

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import torch
from torch_cmspepr.gravnet_model import GravnetModelWithNoiseFilter

model = GravnetModelWithNoiseFilter(input_dim=9, output_dim=6, k=50, signal_threshold=.05)

weights = torch.load("ckpt_train_taus_integrated_noise_Oct20_212115_best_397.pth.tar")

model.load_state_dict(weights["model"])

jitted = torch.jit.script(model)

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import torch
from torch_cmspepr.dataset import TauDataset
import json
from tqdm import tqdm

dataset = TauDataset("/mnt/d/mldata/hgcalml/npzs_all")

out_list = []

for i, data in tqdm(enumerate(dataset[:1000])):

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from coffea import hist
import numpy as np
import random
import string
from tqdm import tqdm

N_ds = 45
N_reg = 3
N_ds_full_syst = 6
N_syst = 53

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def loadData(fileName):

    # 1. Load Data #
    #fileName = "/uscms/home/dkim2/nobackup/safetodel/nano_mc2017_1-11_Skim.root"
    file = uproot.open(fileName)
    printer.info("file: "+ fileName)

    events = file["Events"].arrays(how="zip")

    mask = events.pfcand_pt > 0

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ARG SERVERBASE=20.09-py3

FROM nvcr.io/nvidia/pytorch:${SERVERBASE}

ENV FORCE_CUDA=1
ARG LIB_WITH_CUDA=ON
ARG NPROC=4

RUN git clone https://github.com/rusty1s/pytorch_cluster.git
RUN pushd pytorch_cluster &&\