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#!/usr/bin/env bash
set -e pipefail

NC='\033[0m'
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[0;33m'

gpuminer_image="local/gpuminer:latest"
bcnode_image="local/bcnode:latest"

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#
# Copyright 2015 gRPC authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software

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32.866 test_corrected_jets_factory  test_jetmet_tools.py:347
└─ 32.866 build  coffea/jetmet_tools/CorrectedJetsFactory.py:275
   ├─ 32.376 __setitem__  awkward1/highlevel.py:966
   │  └─ 32.176 with_field  awkward1/operations/structure.py:438
   │     ├─ 25.561 broadcast_and_apply  awkward1/_util.py:492
   │     │  ├─ 21.474 apply  awkward1/_util.py:549
   │     │  │  ├─ 8.790 apply  awkward1/_util.py:549
   │     │  │  │  ├─ 4.407 getfunction  awkward1/operations/structure.py:500
   │     │  │  │  │  └─ 4.406 of  awkward1/nplike.py:10
   │     │  │  │  │     └─ 4.404 kernels  awkward1/operations/convert.py:532

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class GraphWeightsNetwork(nn.Module):
    def __init__ (self, continuous_dim, cat_dim, output_dim=1, hidden_dim=32, conv_depth=1):
        super(GraphMETNetwork, self).__init__()
        
        self.embed_charge = nn.Embedding(3, hidden_dim//4)
        self.embed_pdgid = nn.Embedding(7, hidden_dim//4)
        self.embed_pv = nn.Embedding(8, hidden_dim//4)
        
        self.embed_continuous = nn.Sequential(nn.Linear(continuous_dim,hidden_dim//2),
                                              nn.ELU(),                                             

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class GraphMETNetwork(nn.Module):
    def __init__ (self, continuous_dim, cat_dim, output_dim=1, hidden_dim=32, conv_depth=1):
        super(GraphMETNetwork, self).__init__()
        
        self.embed_charge = nn.Embedding(3, hidden_dim//4)
        self.embed_pdgid = nn.Embedding(8, hidden_dim//4)
        self.embed_pv = nn.Embedding(2, hidden_dim//4)
        
        self.embed_continuous = nn.Sequential(nn.Linear(continuous_dim,hidden_dim//2),
                                              nn.ELU(),                                             

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class GraphMETNetwork(nn.Module):
    def __init__ (self, continuous_dim, cat_dim, output_dim=1, hidden_dim=32, conv_depth=1):
        super(GraphMETNetwork, self).__init__()
        
        self.embed_charge = nn.Embedding(3, hidden_dim//4)
        self.embed_pdgid = nn.Embedding(8, hidden_dim//4)
        self.embed_pv = nn.Embedding(2, hidden_dim//4)
        
        self.embed_continuous = nn.Sequential(nn.Linear(continuous_dim,hidden_dim),
                                             nn.ReLU(),

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pt_true = []
matched_pt_true = []
pred_pt = []

eta_true = []
matched_eta_true = []
pred_eta = []

phi_true = []
matched_phi_true = []

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#!/bin/bash

cmsrel CMSSW_11_1_0_pre6
cd CMSSW_11_1_0_pre6/src
cmsenv
git cms-init
git remote add thomas-cmssw git@github.com:tklijnsma/cmssw.git
git fetch thomas-cmssw dev-finecalo:dev-finecalo
git cms-merge-topic tklijnsma:dev-finecalo
git checkout dev-finecalo

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    def forward(self, x, batch: OptTensor=None):
        x = self.datanorm * x
        x = self.inputnet(x)        
        
        for ec in self.edgeconvs:
            edge_index = knn_graph(x, self.k, batch, loop=False, flow=ec.flow)
            x = ec(x, edge_index)
        
        out = self.output(x) # output the embedding directly from the dgcnn
        

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import torch
import numpy as np
import awkward as ak

def make_embedding_truth(truth_label_by_hits, device='cpu'):
    truth_ordering = np.argsort(truth_label_by_hits)
    uniques, counts = np.unique(truth_label_by_hits, return_counts=True)
    truth_indices = ak.JaggedArray.fromcounts(counts, truth_ordering) # delete noise?   
    pairwise_truth = truth_indices.choose(2)
    other_samples = np.zeros(pairwise_truth.counts.sum() + (pairwise_truth.counts==0).sum(), dtype=np.int64)