Geometric Deep Learning and Graph Neural Networks#

%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns; sns.set_theme()
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
import os
import subprocess
import torch
os.environ['TORCH'] = torch.__version__
print(torch.__version__)
#ensure that the PyTorch and the PyG are the same version
%pip install -q torch-scatter -f https://data.pyg.org/whl/torch-${TORCH}.html
%pip install -q torch-sparse -f https://data.pyg.org/whl/torch-${TORCH}.html
%pip install -q git+https://github.com/pyg-team/pytorch_geometric.git

import torch_geometric

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
import networkx as nx
from tqdm.notebook import tqdm
2.5.1+cu124
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?25h  Installing build dependencies ... ?25l?25hdone
  Getting requirements to build wheel ... ?25l?25hdone
  Preparing metadata (pyproject.toml) ... ?25l?25hdone
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 194.8/194.8 kB 11.0 MB/s eta 0:00:00
?25h  Building wheel for torch-geometric (pyproject.toml) ... ?25l?25hdone
def wget_data(url: str, local_path='./tmp_data'):
  os.makedirs(local_path, exist_ok=True)

  p = subprocess.Popen(["wget", "-nc", "-P", local_path, url], stderr=subprocess.PIPE, encoding='UTF-8')
  rc = None

  while rc is None:
    line = p.stderr.readline().strip('\n')
    if len(line) > 0:
      print(line)
    rc = p.poll()

Introduction to Graph Data#

Graphs can be seen as a system or language for modeling systems that are complex and linked together. A graph is a data type that is modelled as a set of objects which can be represented as a node or vertex and their relationships which is called edges. A graph data can also be seen as a network data where there are points connected together.

A node (vertex) of a graph is point in a graph while an edge is a component that joins edges together in a graph. Graphs can be used to represent data from a lot of domains like biology, physics, social science, chemistry and others.

https://raw.githubusercontent.com/illinois-ipaml/MachineLearningForPhysics/main/img/GraphNeuralNetworks-GraphExample.jpeg

Graph Classification#

Graphs can be classified into different categories directed/undirected graphs, weighted/binary graphs and homogenous/heterogenous graphs.

  • Directed/Undirected graphs: Directed graphs are the ones that all the edges have directtions while in undirected graphs, the edges are does not have directions

  • Weighted/Binary graphs: Weighted graphs is a type of graph that each of the edges are assigned with a value while binary graphs are the ones that the edges does not have an assigned value.

  • Homogenous/Heterogenous graphs: Homogenous graphs are the ones that all the nodes and/or edges are of the same type (e.g. friendship graph) while heterogenous graphs are graphs where the nodes and/or edges are of different types (e.g. knowledge graph).

Traditional graph analysis methods requires using searching algorithms, clustering spanning tree algorithms and so on. A major downside to using these methods for analysis of graph data is that you require a prior knowledge of the graph to be able to apply these algorithms.

Based on the structure of the graph data, traditional machine learning system will not be able to properly interprete the graph data and thus the advent of the Graph Neural Network (GNN). Graph neural network is a domain of deep learning that is mostly concerned with deep learning operations on graph datasets.

Exploring graph data with Pytorch Geometric#

Now we will explore graph and graph neural networks using the PyTorch Geometric (PyG) package (already loaded)

Let’s create a function that will help us visualize a graph data.

def visualize_graph(G, color):
    plt.figure(figsize=(7,7))
    plt.xticks([])
    plt.yticks([])
    nx.draw_networkx(G, pos=nx.spring_layout(G, seed=42), with_labels=False,
                     node_color=color, cmap="Set2")
    plt.show()

PyG provides access to a couple of graph datasets. For this notebook, we are going to use Zachary’s karate club network dataset.

Zachary’s karate club is a great example of social relationships within a small group. This set of data indicated the interactions of club members outside of the club. This dataset also documented the conflict between the instructor, Mr.Hi, and the club president, John because of course price. In the end, half of the members formed a new club around Mr.Hi, and the other half either stayed at the old karate club or gave up karate.

from torch_geometric.datasets import KarateClub

dataset = KarateClub()

Now that we have imported a graph dataset, let’s look at some of the properties of a graph dataset. We will look at some of the properties at the level of the dataset and then select a graph in the dataset to explore it’s properties.

print('Dataset properties')
print('==============================================================')
print(f'Dataset: {dataset}') #This prints the name of the dataset
print(f'Number of graphs in the dataset: {len(dataset)}')
print(f'Number of features: {dataset.num_features}') #Number of features each node in the dataset has
print(f'Number of classes: {dataset.num_classes}') #Number of classes that a node can be classified into

# Since we have one graph in the dataset, we will select the graph and explore it's properties
data = dataset[0]
print('Graph properties')
print('==============================================================')

# Gather some statistics about the graph.
print(f'Number of nodes: {data.num_nodes}') #Number of nodes in the graph
print(f'Number of edges: {data.num_edges}') #Number of edges in the graph
print(f'Average node degree: {data.num_edges / data.num_nodes:.2f}') # Average number of nodes in the graph
print(f'Contains isolated nodes: {data.has_isolated_nodes()}') #Does the graph contains nodes that are not connected
print(f'Contains self-loops: {data.has_self_loops()}') #Does the graph contains nodes that are linked to themselves
print(f'Is undirected: {data.is_undirected()}') #Is the graph an undirected graph
Dataset properties
==============================================================
Dataset: KarateClub()
Number of graphs in the dataset: 1
Number of features: 34
Number of classes: 4
Graph properties
==============================================================
Number of nodes: 34
Number of edges: 156
Average node degree: 4.59
Contains isolated nodes: False
Contains self-loops: False
Is undirected: True

Now let’s visualize the graph using the function that we created earlier. But first, we will convert the graph to networkx graph

from torch_geometric.utils import to_networkx

G = to_networkx(data, to_undirected=True)
visualize_graph(G, color=data.y)
../../_images/fa1ce54f5c50e859ebf095af0512666068a20bd832f7b797f8ba7621ff9ea292.png

Implementing a Graph Neural Network#

For this notebook, we will use a simple GNN which is the Graph Convolution Network (GCN) layer.

  • GCN is a specific type of GNN that uses convolutional operations to propagate information between nodes in a graph.

  • GCNs leverage a localized aggregation of neighboring node features to update the representations of the nodes.

  • GCNs are based on the convolutional operation commonly used in image processing, adapted to the graph domain.

  • The layers in a GCN typically apply a graph convolution operation followed by non-linear activation functions.

  • GCNs have been successful in tasks such as node classification, where nodes are labeled based on their features and graph structure.

Our GNN is defined by stacking three graph convolution layers, which corresponds to aggregating 3-hop neighborhood information around each node (all nodes up to 3 “hops” away). In addition, the GCNConv layers reduce the node feature dimensionality to 2, i.e., 34→4→4→2. Each GCNConv layer is enhanced by a tanh non-linearity. We then apply a linear layer which acts as a classifier to map the nodes to 1 out of the 4 possible classes.

import torch
from torch.nn import Linear
from torch_geometric.nn import GCNConv


class GCN(torch.nn.Module):
    def __init__(self):
        super(GCN, self).__init__()
        torch.manual_seed(12345)
        self.conv1 = GCNConv(dataset.num_features, 4)
        self.conv2 = GCNConv(4, 4)
        self.conv3 = GCNConv(4, 2)
        self.classifier = Linear(2, dataset.num_classes)

    def forward(self, x, edge_index):
        h = self.conv1(x, edge_index)
        h = h.tanh()
        h = self.conv2(h, edge_index)
        h = h.tanh()
        h = self.conv3(h, edge_index)
        h = h.tanh()  # Final GNN embedding space.

        # Apply a final (linear) classifier.
        out = self.classifier(h)

        return out, h

model = GCN()
print(model)
GCN(
  (conv1): GCNConv(34, 4)
  (conv2): GCNConv(4, 4)
  (conv3): GCNConv(4, 2)
  (classifier): Linear(in_features=2, out_features=4, bias=True)
)

Training the model#

To train the network, we will use the CrossEntropyLoss for the loss function and Adam as the gradient optimizer

model = GCN()
criterion = torch.nn.CrossEntropyLoss()  #Initialize the CrossEntropyLoss function.
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)  # Initialize the Adam optimizer.

def train(data):
    optimizer.zero_grad()  # Clear gradients.
    out, h = model(data.x, data.edge_index)  # Perform a single forward pass.
    loss = criterion(out[data.train_mask], data.y[data.train_mask])  # Compute the loss solely based on the training nodes.
    loss.backward()  # Derive gradients.
    optimizer.step()  # Update parameters based on gradients.
    return loss, h

for epoch in range(401):
    loss, h = train(data)
    print(f'Epoch: {epoch}, Loss: {loss}')
Epoch: 0, Loss: 1.414404273033142
Epoch: 1, Loss: 1.4079036712646484
Epoch: 2, Loss: 1.4017865657806396
Epoch: 3, Loss: 1.3959276676177979
Epoch: 4, Loss: 1.3902204036712646
Epoch: 5, Loss: 1.3845514059066772
Epoch: 6, Loss: 1.3788156509399414
Epoch: 7, Loss: 1.372924566268921
Epoch: 8, Loss: 1.3668041229248047
Epoch: 9, Loss: 1.3603920936584473
Epoch: 10, Loss: 1.3536320924758911
Epoch: 11, Loss: 1.346463680267334
Epoch: 12, Loss: 1.3388220071792603
Epoch: 13, Loss: 1.3306517601013184
Epoch: 14, Loss: 1.321907877922058
Epoch: 15, Loss: 1.312545895576477
Epoch: 16, Loss: 1.3025131225585938
Epoch: 17, Loss: 1.2917399406433105
Epoch: 18, Loss: 1.2801498174667358
Epoch: 19, Loss: 1.267681360244751
Epoch: 20, Loss: 1.2542929649353027
Epoch: 21, Loss: 1.2399559020996094
Epoch: 22, Loss: 1.2246456146240234
Epoch: 23, Loss: 1.208348035812378
Epoch: 24, Loss: 1.1910829544067383
Epoch: 25, Loss: 1.172907829284668
Epoch: 26, Loss: 1.1538856029510498
Epoch: 27, Loss: 1.1340937614440918
Epoch: 28, Loss: 1.113678216934204
Epoch: 29, Loss: 1.092814564704895
Epoch: 30, Loss: 1.071663737297058
Epoch: 31, Loss: 1.0504322052001953
Epoch: 32, Loss: 1.0293234586715698
Epoch: 33, Loss: 1.0085136890411377
Epoch: 34, Loss: 0.9882093667984009
Epoch: 35, Loss: 0.9685617685317993
Epoch: 36, Loss: 0.9497098326683044
Epoch: 37, Loss: 0.9317660331726074
Epoch: 38, Loss: 0.914797306060791
Epoch: 39, Loss: 0.8988678455352783
Epoch: 40, Loss: 0.8839972019195557
Epoch: 41, Loss: 0.8701906800270081
Epoch: 42, Loss: 0.8574355840682983
Epoch: 43, Loss: 0.8456954956054688
Epoch: 44, Loss: 0.8349325060844421
Epoch: 45, Loss: 0.8250938653945923
Epoch: 46, Loss: 0.8161190152168274
Epoch: 47, Loss: 0.8079493045806885
Epoch: 48, Loss: 0.8005207777023315
Epoch: 49, Loss: 0.7937697172164917
Epoch: 50, Loss: 0.7876370549201965
Epoch: 51, Loss: 0.7820647358894348
Epoch: 52, Loss: 0.7769969701766968
Epoch: 53, Loss: 0.772383451461792
Epoch: 54, Loss: 0.7681776881217957
Epoch: 55, Loss: 0.7643368244171143
Epoch: 56, Loss: 0.7608219385147095
Epoch: 57, Loss: 0.7575987577438354
Epoch: 58, Loss: 0.7546370029449463
Epoch: 59, Loss: 0.7519098520278931
Epoch: 60, Loss: 0.7493934035301208
Epoch: 61, Loss: 0.7470666766166687
Epoch: 62, Loss: 0.7449116706848145
Epoch: 63, Loss: 0.7429123520851135
Epoch: 64, Loss: 0.7410545349121094
Epoch: 65, Loss: 0.7393249273300171
Epoch: 66, Loss: 0.7377125024795532
Epoch: 67, Loss: 0.7362067699432373
Epoch: 68, Loss: 0.7347980737686157
Epoch: 69, Loss: 0.7334780097007751
Epoch: 70, Loss: 0.7322387099266052
Epoch: 71, Loss: 0.7310730814933777
Epoch: 72, Loss: 0.729974627494812
Epoch: 73, Loss: 0.7289378643035889
Epoch: 74, Loss: 0.7279574871063232
Epoch: 75, Loss: 0.727029025554657
Epoch: 76, Loss: 0.7261483669281006
Epoch: 77, Loss: 0.7253117561340332
Epoch: 78, Loss: 0.7245160937309265
Epoch: 79, Loss: 0.7237581014633179
Epoch: 80, Loss: 0.7230353355407715
Epoch: 81, Loss: 0.7223449945449829
Epoch: 82, Loss: 0.7216847538948059
Epoch: 83, Loss: 0.7210525274276733
Epoch: 84, Loss: 0.7204459309577942
Epoch: 85, Loss: 0.7198635339736938
Epoch: 86, Loss: 0.7193031311035156
Epoch: 87, Loss: 0.7187633514404297
Epoch: 88, Loss: 0.7182430028915405
Epoch: 89, Loss: 0.7177402973175049
Epoch: 90, Loss: 0.7172542810440063
Epoch: 91, Loss: 0.7167838215827942
Epoch: 92, Loss: 0.7163279056549072
Epoch: 93, Loss: 0.71588534116745
Epoch: 94, Loss: 0.7154552340507507
Epoch: 95, Loss: 0.7150367498397827
Epoch: 96, Loss: 0.7146288156509399
Epoch: 97, Loss: 0.7142307758331299
Epoch: 98, Loss: 0.7138414978981018
Epoch: 99, Loss: 0.7134605050086975
Epoch: 100, Loss: 0.7130865454673767
Epoch: 101, Loss: 0.7127190232276917
Epoch: 102, Loss: 0.7123571634292603
Epoch: 103, Loss: 0.7120001316070557
Epoch: 104, Loss: 0.7116469144821167
Epoch: 105, Loss: 0.7112967371940613
Epoch: 106, Loss: 0.7109485864639282
Epoch: 107, Loss: 0.7106013298034668
Epoch: 108, Loss: 0.7102540731430054
Epoch: 109, Loss: 0.7099054455757141
Epoch: 110, Loss: 0.7095539569854736
Epoch: 111, Loss: 0.7091981172561646
Epoch: 112, Loss: 0.7088361978530884
Epoch: 113, Loss: 0.7084661722183228
Epoch: 114, Loss: 0.7080858945846558
Epoch: 115, Loss: 0.7076926827430725
Epoch: 116, Loss: 0.7072839736938477
Epoch: 117, Loss: 0.7068567276000977
Epoch: 118, Loss: 0.7064076662063599
Epoch: 119, Loss: 0.7059327960014343
Epoch: 120, Loss: 0.7054286599159241
Epoch: 121, Loss: 0.7048908472061157
Epoch: 122, Loss: 0.7043149471282959
Epoch: 123, Loss: 0.7036959528923035
Epoch: 124, Loss: 0.703028678894043
Epoch: 125, Loss: 0.7023081183433533
Epoch: 126, Loss: 0.7015290260314941
Epoch: 127, Loss: 0.7006869912147522
Epoch: 128, Loss: 0.6997777223587036
Epoch: 129, Loss: 0.6987966299057007
Epoch: 130, Loss: 0.6977395415306091
Epoch: 131, Loss: 0.6966024041175842
Epoch: 132, Loss: 0.6953816413879395
Epoch: 133, Loss: 0.6940730214118958
Epoch: 134, Loss: 0.6926734447479248
Epoch: 135, Loss: 0.6911810040473938
Epoch: 136, Loss: 0.6895936131477356
Epoch: 137, Loss: 0.6879094839096069
Epoch: 138, Loss: 0.6861274838447571
Epoch: 139, Loss: 0.684245765209198
Epoch: 140, Loss: 0.6822646260261536
Epoch: 141, Loss: 0.6801841259002686
Epoch: 142, Loss: 0.6780048608779907
Epoch: 143, Loss: 0.6757267117500305
Epoch: 144, Loss: 0.6733516454696655
Epoch: 145, Loss: 0.6708807945251465
Epoch: 146, Loss: 0.6683158874511719
Epoch: 147, Loss: 0.6656590700149536
Epoch: 148, Loss: 0.6629128456115723
Epoch: 149, Loss: 0.660078763961792
Epoch: 150, Loss: 0.6571604013442993
Epoch: 151, Loss: 0.6541599035263062
Epoch: 152, Loss: 0.651080310344696
Epoch: 153, Loss: 0.6479244232177734
Epoch: 154, Loss: 0.6446950435638428
Epoch: 155, Loss: 0.641395092010498
Epoch: 156, Loss: 0.6380276679992676
Epoch: 157, Loss: 0.6345993280410767
Epoch: 158, Loss: 0.6311562657356262
Epoch: 159, Loss: 0.6280287504196167
Epoch: 160, Loss: 0.624786376953125
Epoch: 161, Loss: 0.6203908920288086
Epoch: 162, Loss: 0.6176195740699768
Epoch: 163, Loss: 0.6130890250205994
Epoch: 164, Loss: 0.6100022792816162
Epoch: 165, Loss: 0.6056147813796997
Epoch: 166, Loss: 0.6024754643440247
Epoch: 167, Loss: 0.5980788469314575
Epoch: 168, Loss: 0.5947566628456116
Epoch: 169, Loss: 0.5904714465141296
Epoch: 170, Loss: 0.5870352983474731
Epoch: 171, Loss: 0.5828232765197754
Epoch: 172, Loss: 0.5792040824890137
Epoch: 173, Loss: 0.5751749277114868
Epoch: 174, Loss: 0.5713686943054199
Epoch: 175, Loss: 0.5675042867660522
Epoch: 176, Loss: 0.5635499358177185
Epoch: 177, Loss: 0.5598081350326538
Epoch: 178, Loss: 0.5557996034622192
Epoch: 179, Loss: 0.5521055459976196
Epoch: 180, Loss: 0.5481542348861694
Epoch: 181, Loss: 0.5443993210792542
Epoch: 182, Loss: 0.5406018495559692
Epoch: 183, Loss: 0.5367864370346069
Epoch: 184, Loss: 0.5331175327301025
Epoch: 185, Loss: 0.529340386390686
Epoch: 186, Loss: 0.525691568851471
Epoch: 187, Loss: 0.522063136100769
Epoch: 188, Loss: 0.5184276700019836
Epoch: 189, Loss: 0.5149104595184326
Epoch: 190, Loss: 0.5113943219184875
Epoch: 191, Loss: 0.5079199075698853
Epoch: 192, Loss: 0.5045404434204102
Epoch: 193, Loss: 0.5011852979660034
Epoch: 194, Loss: 0.49788326025009155
Epoch: 195, Loss: 0.49466729164123535
Epoch: 196, Loss: 0.4914972186088562
Epoch: 197, Loss: 0.4883778393268585
Epoch: 198, Loss: 0.48533716797828674
Epoch: 199, Loss: 0.482360303401947
Epoch: 200, Loss: 0.47943440079689026
Epoch: 201, Loss: 0.47657454013824463
Epoch: 202, Loss: 0.4737851321697235
Epoch: 203, Loss: 0.47105473279953003
Epoch: 204, Loss: 0.4683806896209717
Epoch: 205, Loss: 0.46577024459838867
Epoch: 206, Loss: 0.4632241129875183
Epoch: 207, Loss: 0.4607364535331726
Epoch: 208, Loss: 0.4583050012588501
Epoch: 209, Loss: 0.4559328258037567
Epoch: 210, Loss: 0.4536212682723999
Epoch: 211, Loss: 0.4513677954673767
Epoch: 212, Loss: 0.44916999340057373
Epoch: 213, Loss: 0.4470280110836029
Epoch: 214, Loss: 0.44494298100471497
Epoch: 215, Loss: 0.4429143965244293
Epoch: 216, Loss: 0.4409407377243042
Epoch: 217, Loss: 0.43902021646499634
Epoch: 218, Loss: 0.437152624130249
Epoch: 219, Loss: 0.4353374242782593
Epoch: 220, Loss: 0.43357405066490173
Epoch: 221, Loss: 0.4318607747554779
Epoch: 222, Loss: 0.4301958382129669
Epoch: 223, Loss: 0.42857807874679565
Epoch: 224, Loss: 0.4270066022872925
Epoch: 225, Loss: 0.42548036575317383
Epoch: 226, Loss: 0.42399758100509644
Epoch: 227, Loss: 0.42255696654319763
Epoch: 228, Loss: 0.4211568832397461
Epoch: 229, Loss: 0.4197961091995239
Epoch: 230, Loss: 0.41847336292266846
Epoch: 231, Loss: 0.4171873927116394
Epoch: 232, Loss: 0.41593676805496216
Epoch: 233, Loss: 0.4147202968597412
Epoch: 234, Loss: 0.4135364890098572
Epoch: 235, Loss: 0.41238415241241455
Epoch: 236, Loss: 0.4112621247768402
Epoch: 237, Loss: 0.4101693630218506
Epoch: 238, Loss: 0.4091046452522278
Epoch: 239, Loss: 0.40806683897972107
Epoch: 240, Loss: 0.40705493092536926
Epoch: 241, Loss: 0.40606772899627686
Epoch: 242, Loss: 0.40510430932044983
Epoch: 243, Loss: 0.40416350960731506
Epoch: 244, Loss: 0.4032444357872009
Epoch: 245, Loss: 0.40234607458114624
Epoch: 246, Loss: 0.40146738290786743
Epoch: 247, Loss: 0.40060728788375854
Epoch: 248, Loss: 0.3997648358345032
Epoch: 249, Loss: 0.39893925189971924
Epoch: 250, Loss: 0.39812910556793213
Epoch: 251, Loss: 0.39733362197875977
Epoch: 252, Loss: 0.39655160903930664
Epoch: 253, Loss: 0.395781934261322
Epoch: 254, Loss: 0.3950235843658447
Epoch: 255, Loss: 0.3942752182483673
Epoch: 256, Loss: 0.39353543519973755
Epoch: 257, Loss: 0.3928031027317047
Epoch: 258, Loss: 0.39207643270492554
Epoch: 259, Loss: 0.3913538455963135
Epoch: 260, Loss: 0.39063364267349243
Epoch: 261, Loss: 0.3899136781692505
Epoch: 262, Loss: 0.38919177651405334
Epoch: 263, Loss: 0.38846561312675476
Epoch: 264, Loss: 0.3877325654029846
Epoch: 265, Loss: 0.38698989152908325
Epoch: 266, Loss: 0.38623446226119995
Epoch: 267, Loss: 0.3854631781578064
Epoch: 268, Loss: 0.38467320799827576
Epoch: 269, Loss: 0.38386136293411255
Epoch: 270, Loss: 0.38302454352378845
Epoch: 271, Loss: 0.38215959072113037
Epoch: 272, Loss: 0.38126328587532043
Epoch: 273, Loss: 0.3803313076496124
Epoch: 274, Loss: 0.3793594539165497
Epoch: 275, Loss: 0.378343403339386
Epoch: 276, Loss: 0.3772798180580139
Epoch: 277, Loss: 0.37616589665412903
Epoch: 278, Loss: 0.3749985098838806
Epoch: 279, Loss: 0.37377339601516724
Epoch: 280, Loss: 0.37248608469963074
Epoch: 281, Loss: 0.37113332748413086
Epoch: 282, Loss: 0.36971211433410645
Epoch: 283, Loss: 0.36821824312210083
Epoch: 284, Loss: 0.36664748191833496
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Epoch: 399, Loss: 0.1527198851108551
Epoch: 400, Loss: 0.15191325545310974

There is much more analysis that can be done with GNNs on the Karate Club dataseet. See here for examples. Our goal in this lecture was to use it an way to introduce the implementation and use of GNNs within the Pytorch framework.

We will now turn to examples using simulated open data from the CMS experiment at the LHC. The algorithms’ inputs are features of the reconstructed charged particles in a jet and the secondary vertices associated with them. Describing the jet shower as a combination of particle-to-particle and particle-to-vertex interactions, the model is trained to learn a jet representation on which the classification problem isoptimized.

We show below an example using a community model called “DeepSets” and then compare to an Interaction Model GNN.


Deep Sets#

We will start by looking at Deep Sets networks using PyTorch. The architecture is based on the following paper: DeepSets

%pip install wget
import wget

%pip install -U PyYAML
%pip install uproot
%pip install awkward
%pip install mplhep
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wget_data("https://raw.githubusercontent.com/jmduarte/iaifi-summer-school/main/book/definitions_lorentz.yml")
--2025-03-02 23:00:59--  https://raw.githubusercontent.com/jmduarte/iaifi-summer-school/main/book/definitions_lorentz.yml
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import yaml

with open("./tmp_data/definitions_lorentz.yml") as file:
    # The FullLoader parameter handles the conversion from YAML
    # scalar values to Python the dictionary format
    definitions = yaml.load(file, Loader=yaml.FullLoader)

features   = definitions["features"]
spectators = definitions["spectators"]
labels     = definitions["labels"]

nfeatures   = definitions["nfeatures"]
nspectators = definitions["nspectators"]
nlabels     = definitions["nlabels"]
ntracks     = definitions["ntracks"]

Data Loader#

Here we have to define the dataset loader.

wget_data("https://raw.githubusercontent.com/jmduarte/iaifi-summer-school/main/book/DeepSetsDataset.py", './')
wget_data("https://raw.githubusercontent.com/jmduarte/iaifi-summer-school/main/book/utils.py", './')
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wget_data("http://opendata.cern.ch/eos/opendata/cms/datascience/HiggsToBBNtupleProducerTool/HiggsToBBNTuple_HiggsToBB_QCD_RunII_13TeV_MC/train/ntuple_merged_90.root")
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Resolving opendata.cern.ch (opendata.cern.ch)... 137.138.6.31, 2001:1458:201:8b::100:1c8
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 60850K .......... .......... .......... .......... .......... 25% 47.1M 7s
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 60950K .......... .......... .......... .......... .......... 25% 56.1M 7s
 61000K .......... .......... .......... .......... .......... 25% 59.2M 7s
 61050K .......... .......... .......... .......... .......... 25% 56.2M 7s
 61100K .......... .......... .......... .......... .......... 25% 70.2M 7s
 61150K .......... .......... .......... .......... .......... 25% 65.0M 7s
 61200K .......... .......... .......... .......... .......... 25% 65.1M 7s
 61250K .......... .......... .......... .......... .......... 25% 11.8M 7s
 61300K .......... .......... .......... .......... .......... 25% 17.0M 7s
 61350K .......... .......... .......... .......... .......... 25%  205M 7s
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 61500K .......... .......... .......... .......... .......... 25%  249M 7s
 61550K .......... .......... .......... .......... .......... 26%  208M 7s
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 61800K .......... .......... .......... .......... .......... 26%  245M 7s
 61850K .......... .......... .......... .......... .......... 26% 10.2M 7s
 61900K .......... .......... .......... .......... .......... 26% 48.3M 7s
 61950K .......... .......... .......... .......... .......... 26% 57.7M 7s
 62000K .......... .......... .......... .......... .......... 26% 57.0M 7s
 62050K .......... .......... .......... .......... .......... 26% 41.3M 7s
 62100K .......... .......... .......... .......... .......... 26% 61.2M 7s
 62150K .......... .......... .......... .......... .......... 26% 12.3M 7s
 62200K .......... .......... .......... .......... .......... 26% 70.2M 7s
 62250K .......... .......... .......... .......... .......... 26% 72.2M 7s
 62300K .......... .......... .......... .......... .......... 26% 5.11M 7s
 62350K .......... .......... .......... .......... .......... 26% 59.4M 7s
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 62500K .......... .......... .......... .......... .......... 26% 46.8M 7s
 62550K .......... .......... .......... .......... .......... 26% 53.5M 7s
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 62800K .......... .......... .......... .......... .......... 26% 54.2M 7s
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 62900K .......... .......... .......... .......... .......... 26% 56.6M 7s
 62950K .......... .......... .......... .......... .......... 26% 62.6M 7s
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 63150K .......... .......... .......... .......... .......... 26% 72.3M 7s
 63200K .......... .......... .......... .......... .......... 26% 74.4M 7s
 63250K .......... .......... .......... .......... .......... 26% 28.9M 7s
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 63350K .......... .......... .......... .......... .......... 26% 24.8M 7s
 63400K .......... .......... .......... .......... .......... 26% 51.9M 7s
 63450K .......... .......... .......... .......... .......... 26% 64.4M 7s
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 63700K .......... .......... .......... .......... .......... 26% 19.3M 7s
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 63800K .......... .......... .......... .......... .......... 26% 59.9M 7s
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 63900K .......... .......... .......... .......... .......... 27% 72.9M 7s
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 64100K .......... .......... .......... .......... .......... 27% 75.2M 7s
 64150K .......... .......... .......... .......... .......... 27% 72.7M 7s
 64200K .......... .......... .......... .......... .......... 27% 78.2M 7s
 64250K .......... .......... .......... .......... .......... 27% 22.4M 7s
 64300K .......... .......... .......... .......... .......... 27% 24.9M 7s
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 64450K .......... .......... .......... .......... .......... 27% 77.6M 7s
 64500K .......... .......... .......... .......... .......... 27% 4.91M 7s
 64550K .......... .......... .......... .......... .......... 27%  120M 7s
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 64650K .......... .......... .......... .......... .......... 27% 21.4M 7s
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 64800K .......... .......... .......... .......... .......... 27% 16.6M 7s
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 65200K .......... .......... .......... .......... .......... 27% 83.6M 7s
 65250K .......... .......... .......... .......... .......... 27% 74.6M 7s
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 65350K .......... .......... .......... .......... .......... 27% 25.4M 7s
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 65450K .......... .......... .......... .......... .......... 27% 20.4M 7s
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 65550K .......... .......... .......... .......... .......... 27% 99.0M 7s
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 65750K .......... .......... .......... .......... .......... 27% 84.8M 7s
 65800K .......... .......... .......... .......... .......... 27% 58.6M 7s
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 66100K .......... .......... .......... .......... .......... 27% 36.6M 7s
 66150K .......... .......... .......... .......... .......... 27% 67.5M 7s
 66200K .......... .......... .......... .......... .......... 27% 50.8M 7s
 66250K .......... .......... .......... .......... .......... 27% 4.76M 7s
 66300K .......... .......... .......... .......... .......... 28% 64.9M 7s
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 67150K .......... .......... .......... .......... .......... 28% 63.1M 7s
 67200K .......... .......... .......... .......... .......... 28% 63.3M 7s
 67250K .......... .......... .......... .......... .......... 28% 63.9M 7s
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 67900K .......... .......... .......... .......... .......... 28% 76.7M 7s
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 68100K .......... .......... .......... .......... .......... 28% 47.8M 7s
 68150K .......... .......... .......... .......... .......... 28% 58.5M 7s
 68200K .......... .......... .......... .......... .......... 28% 63.8M 7s
 68250K .......... .......... .......... .......... .......... 28% 67.5M 7s
 68300K .......... .......... .......... .......... .......... 28% 29.0M 7s
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 71600K .......... .......... .......... .......... .......... 30% 28.7M 7s
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 71900K .......... .......... .......... .......... .......... 30%  284M 6s
 71950K .......... .......... .......... .......... .......... 30% 10.5M 7s
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 72050K .......... .......... .......... .......... .......... 30% 83.5M 6s
 72100K .......... .......... .......... .......... .......... 30% 2.25M 7s
 72150K .......... .......... .......... .......... .......... 30%  163M 7s
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 89500K .......... .......... .......... .......... .......... 37% 77.9M 5s
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 89600K .......... .......... .......... .......... .......... 37% 51.3M 5s
 89650K .......... .......... .......... .......... .......... 37% 72.3M 5s
 89700K .......... .......... .......... .......... .......... 37% 54.4M 5s
 89750K .......... .......... .......... .......... .......... 37% 56.0M 5s
 89800K .......... .......... .......... .......... .......... 37% 49.5M 5s
 89850K .......... .......... .......... .......... .......... 37% 74.6M 5s
 89900K .......... .......... .......... .......... .......... 37% 11.4M 5s
 89950K .......... .......... .......... .......... .......... 38% 28.1M 5s
 90000K .......... .......... .......... .......... .......... 38% 46.3M 5s
 90050K .......... .......... .......... .......... .......... 38% 23.0M 5s
 90100K .......... .......... .......... .......... .......... 38% 12.4M 5s
 90150K .......... .......... .......... .......... .......... 38% 37.9M 5s
 90200K .......... .......... .......... .......... .......... 38% 24.7M 5s
 90250K .......... .......... .......... .......... .......... 38% 26.4M 5s
 90300K .......... .......... .......... .......... .......... 38% 60.0M 5s
 90350K .......... .......... .......... .......... .......... 38% 23.7M 5s
 90400K .......... .......... .......... .......... .......... 38% 38.7M 5s
 90450K .......... .......... .......... .......... .......... 38% 47.5M 5s
 90500K .......... .......... .......... .......... .......... 38% 58.6M 5s
 90550K .......... .......... .......... .......... .......... 38% 62.0M 5s
 90600K .......... .......... .......... .......... .......... 38% 50.4M 5s
 90650K .......... .......... .......... .......... .......... 38% 56.5M 5s
 90700K .......... .......... .......... .......... .......... 38% 45.2M 5s
 90750K .......... .......... .......... .......... .......... 38% 52.8M 5s
 90800K .......... .......... .......... .......... .......... 38% 68.3M 5s
 90850K .......... .......... .......... .......... .......... 38% 71.1M 5s
 90900K .......... .......... .......... .......... .......... 38% 20.0M 5s
 90950K .......... .......... .......... .......... .......... 38% 45.2M 5s
 91000K .......... .......... .......... .......... .......... 38% 40.6M 5s
 91050K .......... .......... .......... .......... .......... 38% 51.1M 5s
 91100K .......... .......... .......... .......... .......... 38% 57.5M 5s
 91150K .......... .......... .......... .......... .......... 38% 55.6M 5s
 91200K .......... .......... .......... .......... .......... 38% 40.1M 5s
 91250K .......... .......... .......... .......... .......... 38% 86.7M 5s
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 91500K .......... .......... .......... .......... .......... 38%  108M 5s
 91550K .......... .......... .......... .......... .......... 38% 69.7M 5s
 91600K .......... .......... .......... .......... .......... 38% 35.7M 5s
 91650K .......... .......... .......... .......... .......... 38% 21.3M 5s
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 91750K .......... .......... .......... .......... .......... 38% 39.6M 5s
 91800K .......... .......... .......... .......... .......... 38% 54.8M 5s
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 91900K .......... .......... .......... .......... .......... 38% 55.2M 5s
 91950K .......... .......... .......... .......... .......... 38% 54.1M 5s
 92000K .......... .......... .......... .......... .......... 38% 55.3M 5s
 92050K .......... .......... .......... .......... .......... 38% 60.7M 5s
 92100K .......... .......... .......... .......... .......... 38% 2.73M 5s
 92150K .......... .......... .......... .......... .......... 38% 59.1M 5s
 92200K .......... .......... .......... .......... .......... 38% 61.8M 5s
 92250K .......... .......... .......... .......... .......... 38% 6.58M 5s
 92300K .......... .......... .......... .......... .......... 38% 54.5M 5s
 92350K .......... .......... .......... .......... .......... 39% 60.3M 5s
 92400K .......... .......... .......... .......... .......... 39% 65.3M 5s
 92450K .......... .......... .......... .......... .......... 39% 38.3M 5s
 92500K .......... .......... .......... .......... .......... 39% 56.4M 5s
 92550K .......... .......... .......... .......... .......... 39% 79.4M 5s
 92600K .......... .......... .......... .......... .......... 39% 82.8M 5s
 92650K .......... .......... .......... .......... .......... 39% 69.6M 5s
 92700K .......... .......... .......... .......... .......... 39% 89.2M 5s
 92750K .......... .......... .......... .......... .......... 39% 55.9M 5s
 92800K .......... .......... .......... .......... .......... 39% 70.7M 5s
 92850K .......... .......... .......... .......... .......... 39% 64.1M 5s
 92900K .......... .......... .......... .......... .......... 39% 64.8M 5s
 92950K .......... .......... .......... .......... .......... 39% 62.3M 5s
 93000K .......... .......... .......... .......... .......... 39% 71.7M 5s
 93050K .......... .......... .......... .......... .......... 39% 29.6M 5s
 93100K .......... .......... .......... .......... .......... 39% 15.4M 5s
 93150K .......... .......... .......... .......... .......... 39% 59.6M 5s
 93200K .......... .......... .......... .......... .......... 39% 68.2M 5s
 93250K .......... .......... .......... .......... .......... 39% 7.76M 5s
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 93400K .......... .......... .......... .......... .......... 39% 59.4M 5s
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 93500K .......... .......... .......... .......... .......... 39% 21.5M 5s
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 93600K .......... .......... .......... .......... .......... 39% 76.4M 5s
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 93700K .......... .......... .......... .......... .......... 39% 73.6M 5s
 93750K .......... .......... .......... .......... .......... 39% 18.2M 5s
 93800K .......... .......... .......... .......... .......... 39% 73.0M 5s
 93850K .......... .......... .......... .......... .......... 39% 56.9M 5s
 93900K .......... .......... .......... .......... .......... 39% 59.2M 5s
 93950K .......... .......... .......... .......... .......... 39% 72.1M 5s
 94000K .......... .......... .......... .......... .......... 39% 80.9M 5s
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 94100K .......... .......... .......... .......... .......... 39% 55.5M 5s
 94150K .......... .......... .......... .......... .......... 39% 64.4M 5s
 94200K .......... .......... .......... .......... .......... 39% 63.0M 5s
 94250K .......... .......... .......... .......... .......... 39% 41.2M 5s
 94300K .......... .......... .......... .......... .......... 39% 35.2M 5s
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 94400K .......... .......... .......... .......... .......... 39% 56.7M 5s
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 94550K .......... .......... .......... .......... .......... 39% 40.0M 5s
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 94650K .......... .......... .......... .......... .......... 39% 25.7M 5s
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 94750K .......... .......... .......... .......... .......... 40% 17.2M 5s
 94800K .......... .......... .......... .......... .......... 40% 18.5M 5s
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 94900K .......... .......... .......... .......... .......... 40% 65.2M 5s
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 95000K .......... .......... .......... .......... .......... 40% 59.8M 5s
 95050K .......... .......... .......... .......... .......... 40% 65.7M 5s
 95100K .......... .......... .......... .......... .......... 40% 78.3M 5s
 95150K .......... .......... .......... .......... .......... 40% 34.7M 5s
 95200K .......... .......... .......... .......... .......... 40% 58.3M 5s
 95250K .......... .......... .......... .......... .......... 40% 27.4M 5s
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 95400K .......... .......... .......... .......... .......... 40% 76.2M 5s
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 95650K .......... .......... .......... .......... .......... 40% 13.9M 5s
 95700K .......... .......... .......... .......... .......... 40% 71.7M 5s
 95750K .......... .......... .......... .......... .......... 40% 75.0M 5s
 95800K .......... .......... .......... .......... .......... 40% 49.4M 5s
 95850K .......... .......... .......... .......... .......... 40% 67.3M 5s
 95900K .......... .......... .......... .......... .......... 40% 82.8M 5s
 95950K .......... .......... .......... .......... .......... 40% 78.3M 5s
 96000K .......... .......... .......... .......... .......... 40% 82.0M 5s
 96050K .......... .......... .......... .......... .......... 40% 51.3M 5s
 96100K .......... .......... .......... .......... .......... 40% 19.5M 5s
 96150K .......... .......... .......... .......... .......... 40% 61.7M 5s
 96200K .......... .......... .......... .......... .......... 40% 68.4M 5s
 96250K .......... .......... .......... .......... .......... 40% 7.21M 5s
 96300K .......... .......... .......... .......... .......... 40% 53.1M 5s
 96350K .......... .......... .......... .......... .......... 40% 32.0M 5s
 96400K .......... .......... .......... .......... .......... 40%  285M 5s
 96450K .......... .......... .......... .......... .......... 40% 55.0M 5s
 96500K .......... .......... .......... .......... .......... 40%  101M 5s
 96550K .......... .......... .......... .......... .......... 40% 55.2M 5s
 96600K .......... .......... .......... .......... .......... 40% 50.7M 5s
 96650K .......... .......... .......... .......... .......... 40% 79.3M 5s
 96700K .......... .......... .......... .......... .......... 40% 80.2M 5s
 96750K .......... .......... .......... .......... .......... 40% 48.9M 5s
 96800K .......... .......... .......... .......... .......... 40% 68.8M 5s
 96850K .......... .......... .......... .......... .......... 40% 79.4M 5s
 96900K .......... .......... .......... .......... .......... 40% 59.1M 5s
 96950K .......... .......... .......... .......... .......... 40% 81.0M 5s
 97000K .......... .......... .......... .......... .......... 40% 60.7M 5s
 97050K .......... .......... .......... .......... .......... 41% 88.1M 5s
 97100K .......... .......... .......... .......... .......... 41% 50.7M 5s
 97150K .......... .......... .......... .......... .......... 41% 62.7M 5s
 97200K .......... .......... .......... .......... .......... 41% 16.1M 5s
 97250K .......... .......... .......... .......... .......... 41% 50.5M 5s
 97300K .......... .......... .......... .......... .......... 41% 6.34M 5s
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 97400K .......... .......... .......... .......... .......... 41%  178M 5s
 97450K .......... .......... .......... .......... .......... 41% 27.8M 5s
 97500K .......... .......... .......... .......... .......... 41% 59.9M 5s
 97550K .......... .......... .......... .......... .......... 41% 37.2M 5s
 97600K .......... .......... .......... .......... .......... 41% 14.6M 5s
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 97700K .......... .......... .......... .......... .......... 41%  185M 5s
 97750K .......... .......... .......... .......... .......... 41%  170M 5s
 97800K .......... .......... .......... .......... .......... 41% 36.9M 5s
 97850K .......... .......... .......... .......... .......... 41%  116M 5s
 97900K .......... .......... .......... .......... .......... 41% 15.8M 5s
 97950K .......... .......... .......... .......... .......... 41%  244M 5s
 98000K .......... .......... .......... .......... .......... 41% 91.3M 5s
 98050K .......... .......... .......... .......... .......... 41% 22.0M 5s
 98100K .......... .......... .......... .......... .......... 41% 67.0M 5s
 98150K .......... .......... .......... .......... .......... 41% 81.2M 5s
 98200K .......... .......... .......... .......... .......... 41% 16.9M 5s
 98250K .......... .......... .......... .......... .......... 41% 72.1M 5s
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 98500K .......... .......... .......... .......... .......... 41% 51.0M 5s
 98550K .......... .......... .......... .......... .......... 41% 33.5M 5s
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 98900K .......... .......... .......... .......... .......... 41% 72.2M 5s
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 99000K .......... .......... .......... .......... .......... 41% 50.1M 5s
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 99100K .......... .......... .......... .......... .......... 41% 50.0M 5s
 99150K .......... .......... .......... .......... .......... 41% 73.0M 5s
 99200K .......... .......... .......... .......... .......... 41% 55.3M 5s
 99250K .......... .......... .......... .......... .......... 41% 55.3M 5s
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 99500K .......... .......... .......... .......... .......... 42% 4.14M 5s
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 99800K .......... .......... .......... .......... .......... 42% 86.8M 5s
 99850K .......... .......... .......... .......... .......... 42% 70.1M 5s
 99900K .......... .......... .......... .......... .......... 42% 99.6M 5s
 99950K .......... .......... .......... .......... .......... 42% 68.5M 5s
100000K .......... .......... .......... .......... .......... 42% 45.7M 5s
100050K .......... .......... .......... .......... .......... 42% 58.0M 5s
100100K .......... .......... .......... .......... .......... 42% 61.5M 5s
100150K .......... .......... .......... .......... .......... 42% 64.4M 5s
100200K .......... .......... .......... .......... .......... 42% 62.1M 5s
100250K .......... .......... .......... .......... .......... 42% 23.5M 5s
100300K .......... .......... .......... .......... .......... 42% 48.4M 5s
100350K .......... .......... .......... .......... .......... 42% 12.6M 5s
100400K .......... .......... .......... .......... .......... 42% 45.7M 5s
100450K .......... .......... .......... .......... .......... 42% 50.2M 5s
100500K .......... .......... .......... .......... .......... 42% 60.4M 5s
100550K .......... .......... .......... .......... .......... 42% 51.5M 5s
100600K .......... .......... .......... .......... .......... 42% 66.0M 5s
100650K .......... .......... .......... .......... .......... 42% 60.2M 5s
100700K .......... .......... .......... .......... .......... 42% 58.5M 5s
100750K .......... .......... .......... .......... .......... 42% 59.2M 5s
100800K .......... .......... .......... .......... .......... 42% 51.1M 5s
100850K .......... .......... .......... .......... .......... 42% 6.17M 5s
100900K .......... .......... .......... .......... .......... 42% 5.95M 5s
100950K .......... .......... .......... .......... .......... 42% 52.4M 5s
101000K .......... .......... .......... .......... .......... 42% 62.0M 5s
101050K .......... .......... .......... .......... .......... 42% 67.1M 5s
101100K .......... .......... .......... .......... .......... 42% 65.8M 5s
101150K .......... .......... .......... .......... .......... 42% 8.67M 5s
101200K .......... .......... .......... .......... .......... 42% 65.8M 5s
101250K .......... .......... .......... .......... .......... 42% 64.3M 5s
101300K .......... .......... .......... .......... .......... 42% 51.1M 5s
101350K .......... .......... .......... .......... .......... 42% 51.5M 5s
101400K .......... .......... .......... .......... .......... 42% 9.44M 5s
101450K .......... .......... .......... .......... .......... 42% 47.4M 5s
101500K .......... .......... .......... .......... .......... 42% 58.1M 5s
101550K .......... .......... .......... .......... .......... 42% 54.8M 5s
101600K .......... .......... .......... .......... .......... 42% 59.8M 5s
101650K .......... .......... .......... .......... .......... 42% 65.7M 5s
101700K .......... .......... .......... .......... .......... 42% 56.9M 5s
101750K .......... .......... .......... .......... .......... 42% 56.3M 5s
101800K .......... .......... .......... .......... .......... 43% 71.9M 5s
101850K .......... .......... .......... .......... .......... 43% 61.9M 5s
101900K .......... .......... .......... .......... .......... 43% 9.36M 5s
101950K .......... .......... .......... .......... .......... 43% 56.6M 5s
102000K .......... .......... .......... .......... .......... 43% 50.0M 5s
102050K .......... .......... .......... .......... .......... 43% 58.8M 5s
102100K .......... .......... .......... .......... .......... 43% 59.2M 5s
102150K .......... .......... .......... .......... .......... 43% 54.4M 5s
102200K .......... .......... .......... .......... .......... 43% 56.5M 5s
102250K .......... .......... .......... .......... .......... 43% 67.6M 5s
102300K .......... .......... .......... .......... .......... 43% 56.6M 5s
102350K .......... .......... .......... .......... .......... 43% 50.6M 5s
102400K .......... .......... .......... .......... .......... 43% 6.82M 5s
102450K .......... .......... .......... .......... .......... 43% 66.9M 5s
102500K .......... .......... .......... .......... .......... 43% 54.4M 5s
102550K .......... .......... .......... .......... .......... 43% 58.3M 5s
102600K .......... .......... .......... .......... .......... 43% 71.6M 5s
102650K .......... .......... .......... .......... .......... 43% 67.0M 5s
102700K .......... .......... .......... .......... .......... 43% 69.5M 5s
102750K .......... .......... .......... .......... .......... 43% 67.2M 5s
102800K .......... .......... .......... .......... .......... 43% 70.4M 5s
102850K .......... .......... .......... .......... .......... 43% 70.7M 5s
102900K .......... .......... .......... .......... .......... 43% 18.5M 5s
102950K .......... .......... .......... .......... .......... 43% 60.8M 5s
103000K .......... .......... .......... .......... .......... 43% 68.5M 5s
103050K .......... .......... .......... .......... .......... 43% 74.1M 5s
103100K .......... .......... .......... .......... .......... 43% 79.6M 5s
103150K .......... .......... .......... .......... .......... 43% 47.2M 5s
103200K .......... .......... .......... .......... .......... 43% 48.9M 5s
103250K .......... .......... .......... .......... .......... 43% 60.0M 5s
103300K .......... .......... .......... .......... .......... 43% 13.5M 5s
103350K .......... .......... .......... .......... .......... 43% 55.4M 5s
103400K .......... .......... .......... .......... .......... 43% 10.9M 5s
103450K .......... .......... .......... .......... .......... 43% 46.2M 5s
103500K .......... .......... .......... .......... .......... 43% 51.3M 5s
103550K .......... .......... .......... .......... .......... 43% 41.0M 5s
103600K .......... .......... .......... .......... .......... 43% 61.8M 5s
103650K .......... .......... .......... .......... .......... 43% 48.8M 5s
103700K .......... .......... .......... .......... .......... 43% 25.4M 5s
103750K .......... .......... .......... .......... .......... 43% 58.0M 5s
103800K .......... .......... .......... .......... .......... 43% 6.82M 5s
103850K .......... .......... .......... .......... .......... 43% 66.9M 5s
103900K .......... .......... .......... .......... .......... 43% 50.6M 5s
103950K .......... .......... .......... .......... .......... 43% 45.2M 5s
104000K .......... .......... .......... .......... .......... 43% 69.1M 5s
104050K .......... .......... .......... .......... .......... 43% 72.2M 5s
104100K .......... .......... .......... .......... .......... 43% 70.4M 5s
104150K .......... .......... .......... .......... .......... 44% 62.4M 5s
104200K .......... .......... .......... .......... .......... 44% 69.0M 5s
104250K .......... .......... .......... .......... .......... 44% 56.7M 5s
104300K .......... .......... .......... .......... .......... 44% 73.3M 5s
104350K .......... .......... .......... .......... .......... 44% 64.4M 5s
104400K .......... .......... .......... .......... .......... 44% 68.0M 5s
104450K .......... .......... .......... .......... .......... 44% 9.44M 5s
104500K .......... .......... .......... .......... .......... 44% 73.7M 5s
104550K .......... .......... .......... .......... .......... 44% 54.1M 5s
104600K .......... .......... .......... .......... .......... 44% 67.0M 5s
104650K .......... .......... .......... .......... .......... 44% 68.2M 5s
104700K .......... .......... .......... .......... .......... 44% 57.3M 5s
104750K .......... .......... .......... .......... .......... 44% 54.4M 5s
104800K .......... .......... .......... .......... .......... 44% 72.4M 5s
104850K .......... .......... .......... .......... .......... 44% 74.1M 5s
104900K .......... .......... .......... .......... .......... 44% 78.7M 5s
104950K .......... .......... .......... .......... .......... 44% 7.61M 5s
105000K .......... .......... .......... .......... .......... 44% 68.7M 5s
105050K .......... .......... .......... .......... .......... 44% 69.8M 5s
105100K .......... .......... .......... .......... .......... 44% 74.1M 5s
105150K .......... .......... .......... .......... .......... 44% 66.5M 5s
105200K .......... .......... .......... .......... .......... 44% 59.7M 5s
105250K .......... .......... .......... .......... .......... 44% 70.0M 5s
105300K .......... .......... .......... .......... .......... 44% 67.4M 5s
105350K .......... .......... .......... .......... .......... 44% 54.3M 5s
105400K .......... .......... .......... .......... .......... 44% 60.8M 5s
105450K .......... .......... .......... .......... .......... 44% 58.0M 5s
105500K .......... .......... .......... .......... .......... 44% 70.5M 5s
105550K .......... .......... .......... .......... .......... 44% 54.9M 5s
105600K .......... .......... .......... .......... .......... 44% 67.5M 5s
105650K .......... .......... .......... .......... .......... 44% 66.7M 5s
105700K .......... .......... .......... .......... .......... 44% 64.2M 5s
105750K .......... .......... .......... .......... .......... 44% 44.4M 5s
105800K .......... .......... .......... .......... .......... 44% 59.5M 5s
105850K .......... .......... .......... .......... .......... 44% 7.90M 5s
105900K .......... .......... .......... .......... .......... 44% 49.3M 5s
105950K .......... .......... .......... .......... .......... 44% 8.10M 5s
106000K .......... .......... .......... .......... .......... 44% 69.3M 5s
106050K .......... .......... .......... .......... .......... 44% 76.0M 5s
106100K .......... .......... .......... .......... .......... 44% 73.9M 5s
106150K .......... .......... .......... .......... .......... 44% 67.0M 5s
106200K .......... .......... .......... .......... .......... 44% 76.0M 5s
106250K .......... .......... .......... .......... .......... 44% 74.7M 5s
106300K .......... .......... .......... .......... .......... 44% 76.4M 5s
106350K .......... .......... .......... .......... .......... 44% 6.92M 5s
106400K .......... .......... .......... .......... .......... 44% 73.6M 5s
106450K .......... .......... .......... .......... .......... 44% 35.6M 5s
106500K .......... .......... .......... .......... .......... 44%  251M 5s
106550K .......... .......... .......... .......... .......... 45%  197M 5s
106600K .......... .......... .......... .......... .......... 45% 8.31M 5s
106650K .......... .......... .......... .......... .......... 45% 54.2M 5s
106700K .......... .......... .......... .......... .......... 45% 51.8M 5s
106750K .......... .......... .......... .......... .......... 45% 8.27M 5s
106800K .......... .......... .......... .......... .......... 45%  253M 5s
106850K .......... .......... .......... .......... .......... 45%  230M 5s
106900K .......... .......... .......... .......... .......... 45%  170M 5s
106950K .......... .......... .......... .......... .......... 45% 41.7M 5s
107000K .......... .......... .......... .......... .......... 45%  251M 5s
107050K .......... .......... .......... .......... .......... 45%  258M 5s
107100K .......... .......... .......... .......... .......... 45%  188M 5s
107150K .......... .......... .......... .......... .......... 45%  216M 5s
107200K .......... .......... .......... .......... .......... 45%  244M 5s
107250K .......... .......... .......... .......... .......... 45%  188M 5s
107300K .......... .......... .......... .......... .......... 45%  139M 5s
107350K .......... .......... .......... .......... .......... 45%  193M 5s
107400K .......... .......... .......... .......... .......... 45%  231M 5s
107450K .......... .......... .......... .......... .......... 45% 45.0M 5s
107500K .......... .......... .......... .......... .......... 45%  205M 5s
107550K .......... .......... .......... .......... .......... 45%  227M 5s
107600K .......... .......... .......... .......... .......... 45%  215M 5s
107650K .......... .......... .......... .......... .......... 45% 71.0M 5s
107700K .......... .......... .......... .......... .......... 45%  134M 5s
107750K .......... .......... .......... .......... .......... 45% 55.6M 5s
107800K .......... .......... .......... .......... .......... 45% 52.4M 5s
107850K .......... .......... .......... .......... .......... 45% 4.27M 5s
107900K .......... .......... .......... .......... .......... 45% 59.6M 5s
107950K .......... .......... .......... .......... .......... 45% 65.1M 5s
108000K .......... .......... .......... .......... .......... 45% 14.7M 5s
108050K .......... .......... .......... .......... .......... 45% 21.7M 5s
108100K .......... .......... .......... .......... .......... 45% 56.5M 5s
108150K .......... .......... .......... .......... .......... 45% 25.8M 5s
108200K .......... .......... .......... .......... .......... 45% 24.1M 5s
108250K .......... .......... .......... .......... .......... 45% 41.2M 5s
108300K .......... .......... .......... .......... .......... 45% 54.9M 4s
108350K .......... .......... .......... .......... .......... 45% 55.6M 4s
108400K .......... .......... .......... .......... .......... 45% 63.3M 4s
108450K .......... .......... .......... .......... .......... 45% 71.3M 4s
108500K .......... .......... .......... .......... .......... 45% 29.7M 4s
108550K .......... .......... .......... .......... .......... 45%  231M 4s
108600K .......... .......... .......... .......... .......... 45%  277M 4s
108650K .......... .......... .......... .......... .......... 45%  248M 4s
108700K .......... .......... .......... .......... .......... 45%  316M 4s
108750K .......... .......... .......... .......... .......... 45%  256M 4s
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108850K .......... .......... .......... .......... .......... 45%  234M 4s
108900K .......... .......... .......... .......... .......... 46%  282M 4s
108950K .......... .......... .......... .......... .......... 46%  109M 4s
109000K .......... .......... .......... .......... .......... 46% 71.3M 4s
109050K .......... .......... .......... .......... .......... 46% 15.1M 4s
109100K .......... .......... .......... .......... .......... 46% 88.2M 4s
109150K .......... .......... .......... .......... .......... 46% 13.7M 4s
109200K .......... .......... .......... .......... .......... 46% 80.3M 4s
109250K .......... .......... .......... .......... .......... 46% 42.7M 4s
109300K .......... .......... .......... .......... .......... 46%  127M 4s
109350K .......... .......... .......... .......... .......... 46% 72.3M 4s
109400K .......... .......... .......... .......... .......... 46% 55.9M 4s
109450K .......... .......... .......... .......... .......... 46% 77.3M 4s
109500K .......... .......... .......... .......... .......... 46% 39.6M 4s
109550K .......... .......... .......... .......... .......... 46%  170M 4s
109600K .......... .......... .......... .......... .......... 46% 66.8M 4s
109650K .......... .......... .......... .......... .......... 46% 62.8M 4s
109700K .......... .......... .......... .......... .......... 46% 35.2M 4s
109750K .......... .......... .......... .......... .......... 46% 66.1M 4s
109800K .......... .......... .......... .......... .......... 46% 9.05M 4s
109850K .......... .......... .......... .......... .......... 46% 37.9M 4s
109900K .......... .......... .......... .......... .......... 46% 64.4M 4s
109950K .......... .......... .......... .......... .......... 46% 58.4M 4s
110000K .......... .......... .......... .......... .......... 46% 4.80M 4s
110050K .......... .......... .......... .......... .......... 46%  127M 4s
110100K .......... .......... .......... .......... .......... 46% 72.9M 4s
110150K .......... .......... .......... .......... .......... 46% 92.6M 4s
110200K .......... .......... .......... .......... .......... 46% 59.0M 4s
110250K .......... .......... .......... .......... .......... 46% 55.3M 4s
110300K .......... .......... .......... .......... .......... 46% 77.3M 4s
110350K .......... .......... .......... .......... .......... 46% 5.40M 4s
110400K .......... .......... .......... .......... .......... 46% 30.1M 4s
110450K .......... .......... .......... .......... .......... 46% 61.0M 4s
110500K .......... .......... .......... .......... .......... 46% 20.3M 4s
110550K .......... .......... .......... .......... .......... 46% 72.2M 4s
110600K .......... .......... .......... .......... .......... 46% 71.1M 4s
110650K .......... .......... .......... .......... .......... 46% 70.1M 4s
110700K .......... .......... .......... .......... .......... 46% 19.3M 4s
110750K .......... .......... .......... .......... .......... 46% 58.4M 4s
110800K .......... .......... .......... .......... .......... 46% 57.6M 4s
110850K .......... .......... .......... .......... .......... 46% 60.6M 4s
110900K .......... .......... .......... .......... .......... 46% 61.3M 4s
110950K .......... .......... .......... .......... .......... 46% 66.7M 4s
111000K .......... .......... .......... .......... .......... 46% 60.2M 4s
111050K .......... .......... .......... .......... .......... 46% 47.2M 4s
111100K .......... .......... .......... .......... .......... 46%  192M 4s
111150K .......... .......... .......... .......... .......... 46%  193M 4s
111200K .......... .......... .......... .......... .......... 46%  249M 4s
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111300K .......... .......... .......... .......... .......... 47% 59.7M 4s
111350K .......... .......... .......... .......... .......... 47% 57.7M 4s
111400K .......... .......... .......... .......... .......... 47% 65.5M 4s
111450K .......... .......... .......... .......... .......... 47% 52.1M 4s
111500K .......... .......... .......... .......... .......... 47% 67.3M 4s
111550K .......... .......... .......... .......... .......... 47% 51.0M 4s
111600K .......... .......... .......... .......... .......... 47% 60.7M 4s
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111700K .......... .......... .......... .......... .......... 47% 73.6M 4s
111750K .......... .......... .......... .......... .......... 47% 78.4M 4s
111800K .......... .......... .......... .......... .......... 47% 30.5M 4s
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111900K .......... .......... .......... .......... .......... 47% 67.2M 4s
111950K .......... .......... .......... .......... .......... 47% 11.0M 4s
112000K .......... .......... .......... .......... .......... 47% 48.3M 4s
112050K .......... .......... .......... .......... .......... 47% 18.4M 4s
112100K .......... .......... .......... .......... .......... 47% 50.8M 4s
112150K .......... .......... .......... .......... .......... 47% 70.8M 4s
112200K .......... .......... .......... .......... .......... 47% 59.6M 4s
112250K .......... .......... .......... .......... .......... 47% 57.2M 4s
112300K .......... .......... .......... .......... .......... 47% 48.7M 4s
112350K .......... .......... .......... .......... .......... 47% 61.2M 4s
112400K .......... .......... .......... .......... .......... 47% 57.9M 4s
112450K .......... .......... .......... .......... .......... 47% 55.1M 4s
112500K .......... .......... .......... .......... .......... 47% 13.7M 4s
112550K .......... .......... .......... .......... .......... 47% 17.4M 4s
112600K .......... .......... .......... .......... .......... 47% 6.72M 4s
112650K .......... .......... .......... .......... .......... 47% 25.0M 4s
112700K .......... .......... .......... .......... .......... 47% 10.8M 4s
112750K .......... .......... .......... .......... .......... 47% 69.9M 4s
112800K .......... .......... .......... .......... .......... 47% 69.6M 4s
112850K .......... .......... .......... .......... .......... 47% 21.6M 4s
112900K .......... .......... .......... .......... .......... 47% 52.3M 4s
112950K .......... .......... .......... .......... .......... 47% 59.7M 4s
113000K .......... .......... .......... .......... .......... 47% 63.4M 4s
113050K .......... .......... .......... .......... .......... 47% 25.6M 4s
113100K .......... .......... .......... .......... .......... 47% 62.0M 4s
113150K .......... .......... .......... .......... .......... 47% 79.1M 4s
113200K .......... .......... .......... .......... .......... 47% 73.4M 4s
113250K .......... .......... .......... .......... .......... 47% 73.8M 4s
113300K .......... .......... .......... .......... .......... 47% 68.0M 4s
113350K .......... .......... .......... .......... .......... 47% 62.1M 4s
113400K .......... .......... .......... .......... .......... 47% 53.6M 4s
113450K .......... .......... .......... .......... .......... 47% 66.1M 4s
113500K .......... .......... .......... .......... .......... 47% 69.2M 4s
113550K .......... .......... .......... .......... .......... 47% 34.9M 4s
113600K .......... .......... .......... .......... .......... 47%  249M 4s
113650K .......... .......... .......... .......... .......... 48%  245M 4s
113700K .......... .......... .......... .......... .......... 48%  218M 4s
113750K .......... .......... .......... .......... .......... 48%  246M 4s
113800K .......... .......... .......... .......... .......... 48%  223M 4s
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113900K .......... .......... .......... .......... .......... 48%  185M 4s
113950K .......... .......... .......... .......... .......... 48%  251M 4s
114000K .......... .......... .......... .......... .......... 48%  179M 4s
114050K .......... .......... .......... .......... .......... 48% 44.5M 4s
114100K .......... .......... .......... .......... .......... 48%  162M 4s
114150K .......... .......... .......... .......... .......... 48%  199M 4s
114200K .......... .......... .......... .......... .......... 48%  105M 4s
114250K .......... .......... .......... .......... .......... 48% 48.0M 4s
114300K .......... .......... .......... .......... .......... 48% 28.9M 4s
114350K .......... .......... .......... .......... .......... 48% 44.5M 4s
114400K .......... .......... .......... .......... .......... 48% 4.92M 4s
114450K .......... .......... .......... .......... .......... 48% 50.4M 4s
114500K .......... .......... .......... .......... .......... 48% 19.9M 4s
114550K .......... .......... .......... .......... .......... 48% 70.0M 4s
114600K .......... .......... .......... .......... .......... 48% 15.9M 4s
114650K .......... .......... .......... .......... .......... 48% 61.9M 4s
114700K .......... .......... .......... .......... .......... 48% 73.6M 4s
114750K .......... .......... .......... .......... .......... 48% 59.0M 4s
114800K .......... .......... .......... .......... .......... 48% 59.4M 4s
114850K .......... .......... .......... .......... .......... 48% 54.6M 4s
114900K .......... .......... .......... .......... .......... 48% 54.1M 4s
114950K .......... .......... .......... .......... .......... 48% 16.6M 4s
115000K .......... .......... .......... .......... .......... 48% 46.3M 4s
115050K .......... .......... .......... .......... .......... 48% 54.1M 4s
115100K .......... .......... .......... .......... .......... 48% 32.5M 4s
115150K .......... .......... .......... .......... .......... 48% 32.0M 4s
115200K .......... .......... .......... .......... .......... 48% 68.4M 4s
115250K .......... .......... .......... .......... .......... 48% 28.2M 4s
115300K .......... .......... .......... .......... .......... 48% 31.0M 4s
115350K .......... .......... .......... .......... .......... 48% 24.2M 4s
115400K .......... .......... .......... .......... .......... 48% 72.9M 4s
115450K .......... .......... .......... .......... .......... 48% 81.8M 4s
115500K .......... .......... .......... .......... .......... 48% 25.2M 4s
115550K .......... .......... .......... .......... .......... 48% 63.5M 4s
115600K .......... .......... .......... .......... .......... 48% 39.6M 4s
115650K .......... .......... .......... .......... .......... 48% 73.6M 4s
115700K .......... .......... .......... .......... .......... 48% 74.0M 4s
115750K .......... .......... .......... .......... .......... 48% 27.4M 4s
115800K .......... .......... .......... .......... .......... 48% 66.5M 4s
115850K .......... .......... .......... .......... .......... 48% 78.0M 4s
115900K .......... .......... .......... .......... .......... 48% 73.1M 4s
115950K .......... .......... .......... .......... .......... 48% 62.1M 4s
116000K .......... .......... .......... .......... .......... 49% 83.2M 4s
116050K .......... .......... .......... .......... .......... 49% 64.4M 4s
116100K .......... .......... .......... .......... .......... 49% 33.3M 4s
116150K .......... .......... .......... .......... .......... 49%  204M 4s
116200K .......... .......... .......... .......... .......... 49%  233M 4s
116250K .......... .......... .......... .......... .......... 49% 40.3M 4s
116300K .......... .......... .......... .......... .......... 49% 52.9M 4s
116350K .......... .......... .......... .......... .......... 49% 62.2M 4s
116400K .......... .......... .......... .......... .......... 49% 65.5M 4s
116450K .......... .......... .......... .......... .......... 49% 63.5M 4s
116500K .......... .......... .......... .......... .......... 49% 56.9M 4s
116550K .......... .......... .......... .......... .......... 49% 29.4M 4s
116600K .......... .......... .......... .......... .......... 49% 28.7M 4s
116650K .......... .......... .......... .......... .......... 49% 9.59M 4s
116700K .......... .......... .......... .......... .......... 49% 12.0M 4s
116750K .......... .......... .......... .......... .......... 49%  137M 4s
116800K .......... .......... .......... .......... .......... 49% 70.6M 4s
116850K .......... .......... .......... .......... .......... 49% 86.7M 4s
116900K .......... .......... .......... .......... .......... 49% 9.43M 4s
116950K .......... .......... .......... .......... .......... 49% 11.4M 4s
117000K .......... .......... .......... .......... .......... 49%  114M 4s
117050K .......... .......... .......... .......... .......... 49% 86.3M 4s
117100K .......... .......... .......... .......... .......... 49% 47.0M 4s
117150K .......... .......... .......... .......... .......... 49% 83.6M 4s
117200K .......... .......... .......... .......... .......... 49% 86.0M 4s
117250K .......... .......... .......... .......... .......... 49% 32.6M 4s
117300K .......... .......... .......... .......... .......... 49% 16.7M 4s
117350K .......... .......... .......... .......... .......... 49% 96.4M 4s
117400K .......... .......... .......... .......... .......... 49% 79.1M 4s
117450K .......... .......... .......... .......... .......... 49% 88.5M 4s
117500K .......... .......... .......... .......... .......... 49% 80.5M 4s
117550K .......... .......... .......... .......... .......... 49% 84.7M 4s
117600K .......... .......... .......... .......... .......... 49% 20.9M 4s
117650K .......... .......... .......... .......... .......... 49% 82.2M 4s
117700K .......... .......... .......... .......... .......... 49% 74.0M 4s
117750K .......... .......... .......... .......... .......... 49%  117M 4s
117800K .......... .......... .......... .......... .......... 49% 64.9M 4s
117850K .......... .......... .......... .......... .......... 49% 81.8M 4s
117900K .......... .......... .......... .......... .......... 49%  162M 4s
117950K .......... .......... .......... .......... .......... 49% 81.4M 4s
118000K .......... .......... .......... .......... .......... 49% 66.1M 4s
118050K .......... .......... .......... .......... .......... 49% 49.4M 4s
118100K .......... .......... .......... .......... .......... 49% 63.4M 4s
118150K .......... .......... .......... .......... .......... 49% 31.3M 4s
118200K .......... .......... .......... .......... .......... 49% 71.0M 4s
118250K .......... .......... .......... .......... .......... 49% 72.7M 4s
118300K .......... .......... .......... .......... .......... 49% 64.9M 4s
118350K .......... .......... .......... .......... .......... 49% 77.0M 4s
118400K .......... .......... .......... .......... .......... 50% 21.9M 4s
118450K .......... .......... .......... .......... .......... 50% 56.9M 4s
118500K .......... .......... .......... .......... .......... 50% 13.4M 4s
118550K .......... .......... .......... .......... .......... 50% 5.72M 4s
118600K .......... .......... .......... .......... .......... 50% 12.2M 4s
118650K .......... .......... .......... .......... .......... 50% 27.5M 4s
118700K .......... .......... .......... .......... .......... 50% 18.7M 4s
118750K .......... .......... .......... .......... .......... 50% 72.8M 4s
118800K .......... .......... .......... .......... .......... 50% 66.7M 4s
118850K .......... .......... .......... .......... .......... 50% 63.0M 4s
118900K .......... .......... .......... .......... .......... 50%  126M 4s
118950K .......... .......... .......... .......... .......... 50% 94.4M 4s
119000K .......... .......... .......... .......... .......... 50% 71.9M 4s
119050K .......... .......... .......... .......... .......... 50% 63.8M 4s
119100K .......... .......... .......... .......... .......... 50% 60.0M 4s
119150K .......... .......... .......... .......... .......... 50% 29.3M 4s
119200K .......... .......... .......... .......... .......... 50% 73.4M 4s
119250K .......... .......... .......... .......... .......... 50% 78.6M 4s
119300K .......... .......... .......... .......... .......... 50% 68.4M 4s
119350K .......... .......... .......... .......... .......... 50% 78.0M 4s
119400K .......... .......... .......... .......... .......... 50% 76.7M 4s
119450K .......... .......... .......... .......... .......... 50% 67.1M 4s
119500K .......... .......... .......... .......... .......... 50% 58.8M 4s
119550K .......... .......... .......... .......... .......... 50% 63.0M 4s
119600K .......... .......... .......... .......... .......... 50% 64.0M 4s
119650K .......... .......... .......... .......... .......... 50% 30.5M 4s
119700K .......... .......... .......... .......... .......... 50% 10.0M 4s
119750K .......... .......... .......... .......... .......... 50%  154M 4s
119800K .......... .......... .......... .......... .......... 50%  214M 4s
119850K .......... .......... .......... .......... .......... 50%  150M 4s
119900K .......... .......... .......... .......... .......... 50%  224M 4s
119950K .......... .......... .......... .......... .......... 50%  195M 4s
120000K .......... .......... .......... .......... .......... 50%  139M 4s
120050K .......... .......... .......... .......... .......... 50%  148M 4s
120100K .......... .......... .......... .......... .......... 50% 96.5M 4s
120150K .......... .......... .......... .......... .......... 50% 23.7M 4s
120200K .......... .......... .......... .......... .......... 50% 67.9M 4s
120250K .......... .......... .......... .......... .......... 50% 62.0M 4s
120300K .......... .......... .......... .......... .......... 50% 68.8M 4s
120350K .......... .......... .......... .......... .......... 50% 67.2M 4s
120400K .......... .......... .......... .......... .......... 50% 65.2M 4s
120450K .......... .......... .......... .......... .......... 50% 7.09M 4s
120500K .......... .......... .......... .......... .......... 50% 71.6M 4s
120550K .......... .......... .......... .......... .......... 50% 38.3M 4s
120600K .......... .......... .......... .......... .......... 50% 29.5M 4s
120650K .......... .......... .......... .......... .......... 50% 30.7M 4s
120700K .......... .......... .......... .......... .......... 50% 66.4M 4s
120750K .......... .......... .......... .......... .......... 51% 23.8M 4s
120800K .......... .......... .......... .......... .......... 51% 57.1M 4s
120850K .......... .......... .......... .......... .......... 51% 63.1M 4s
120900K .......... .......... .......... .......... .......... 51% 49.3M 4s
120950K .......... .......... .......... .......... .......... 51% 9.32M 4s
121000K .......... .......... .......... .......... .......... 51% 41.9M 4s
121050K .......... .......... .......... .......... .......... 51% 97.2M 4s
121100K .......... .......... .......... .......... .......... 51% 49.4M 4s
121150K .......... .......... .......... .......... .......... 51% 66.5M 4s
121200K .......... .......... .......... .......... .......... 51% 76.4M 4s
121250K .......... .......... .......... .......... .......... 51%  105M 4s
121300K .......... .......... .......... .......... .......... 51% 64.3M 4s
121350K .......... .......... .......... .......... .......... 51% 14.9M 4s
121400K .......... .......... .......... .......... .......... 51% 96.3M 4s
121450K .......... .......... .......... .......... .......... 51% 30.8M 4s
121500K .......... .......... .......... .......... .......... 51%  194M 4s
121550K .......... .......... .......... .......... .......... 51%  120M 4s
121600K .......... .......... .......... .......... .......... 51% 60.7M 4s
121650K .......... .......... .......... .......... .......... 51% 35.4M 4s
121700K .......... .......... .......... .......... .......... 51% 95.0M 4s
121750K .......... .......... .......... .......... .......... 51% 96.4M 4s
121800K .......... .......... .......... .......... .......... 51% 43.9M 4s
121850K .......... .......... .......... .......... .......... 51% 55.6M 4s
121900K .......... .......... .......... .......... .......... 51% 93.1M 4s
121950K .......... .......... .......... .......... .......... 51% 74.2M 4s
122000K .......... .......... .......... .......... .......... 51% 47.9M 4s
122050K .......... .......... .......... .......... .......... 51% 82.1M 4s
122100K .......... .......... .......... .......... .......... 51% 28.4M 4s
122150K .......... .......... .......... .......... .......... 51%  129M 4s
122200K .......... .......... .......... .......... .......... 51%  104M 4s
122250K .......... .......... .......... .......... .......... 51% 49.1M 4s
122300K .......... .......... .......... .......... .......... 51% 13.5M 4s
122350K .......... .......... .......... .......... .......... 51%  150M 4s
122400K .......... .......... .......... .......... .......... 51% 91.4M 4s
122450K .......... .......... .......... .......... .......... 51% 91.8M 4s
122500K .......... .......... .......... .......... .......... 51% 27.2M 4s
122550K .......... .......... .......... .......... .......... 51% 42.5M 4s
122600K .......... .......... .......... .......... .......... 51% 5.79M 4s
122650K .......... .......... .......... .......... .......... 51% 69.9M 4s
122700K .......... .......... .......... .......... .......... 51% 5.56M 4s
122750K .......... .......... .......... .......... .......... 51% 67.6M 4s
122800K .......... .......... .......... .......... .......... 51% 91.8M 4s
122850K .......... .......... .......... .......... .......... 51% 43.8M 4s
122900K .......... .......... .......... .......... .......... 51%  107M 4s
122950K .......... .......... .......... .......... .......... 51% 72.2M 4s
123000K .......... .......... .......... .......... .......... 51% 81.5M 4s
123050K .......... .......... .......... .......... .......... 51% 32.9M 4s
123100K .......... .......... .......... .......... .......... 52%  160M 4s
123150K .......... .......... .......... .......... .......... 52% 72.3M 4s
123200K .......... .......... .......... .......... .......... 52% 33.1M 4s
123250K .......... .......... .......... .......... .......... 52% 32.7M 4s
123300K .......... .......... .......... .......... .......... 52% 53.6M 4s
123350K .......... .......... .......... .......... .......... 52%  231M 4s
123400K .......... .......... .......... .......... .......... 52% 47.2M 4s
123450K .......... .......... .......... .......... .......... 52% 50.7M 4s
123500K .......... .......... .......... .......... .......... 52% 59.8M 4s
123550K .......... .......... .......... .......... .......... 52% 68.7M 4s
123600K .......... .......... .......... .......... .......... 52% 58.3M 4s
123650K .......... .......... .......... .......... .......... 52% 72.2M 4s
123700K .......... .......... .......... .......... .......... 52% 30.0M 4s
123750K .......... .......... .......... .......... .......... 52% 26.2M 4s
123800K .......... .......... .......... .......... .......... 52% 62.8M 4s
123850K .......... .......... .......... .......... .......... 52% 71.1M 4s
123900K .......... .......... .......... .......... .......... 52% 58.9M 4s
123950K .......... .......... .......... .......... .......... 52% 67.5M 4s
124000K .......... .......... .......... .......... .......... 52% 70.6M 4s
124050K .......... .......... .......... .......... .......... 52% 66.1M 4s
124100K .......... .......... .......... .......... .......... 52% 58.9M 4s
124150K .......... .......... .......... .......... .......... 52% 52.4M 4s
124200K .......... .......... .......... .......... .......... 52% 65.6M 4s
124250K .......... .......... .......... .......... .......... 52% 29.9M 4s
124300K .......... .......... .......... .......... .......... 52% 61.9M 4s
124350K .......... .......... .......... .......... .......... 52% 74.9M 4s
124400K .......... .......... .......... .......... .......... 52% 76.1M 4s
124450K .......... .......... .......... .......... .......... 52% 9.09M 4s
124500K .......... .......... .......... .......... .......... 52% 60.9M 4s
124550K .......... .......... .......... .......... .......... 52% 67.2M 4s
124600K .......... .......... .......... .......... .......... 52% 19.3M 4s
124650K .......... .......... .......... .......... .......... 52% 59.3M 4s
124700K .......... .......... .......... .......... .......... 52% 72.7M 4s
124750K .......... .......... .......... .......... .......... 52% 20.4M 4s
124800K .......... .......... .......... .......... .......... 52% 66.5M 4s
124850K .......... .......... .......... .......... .......... 52% 20.5M 4s
124900K .......... .......... .......... .......... .......... 52% 12.2M 4s
124950K .......... .......... .......... .......... .......... 52% 64.3M 4s
125000K .......... .......... .......... .......... .......... 52% 64.7M 4s
125050K .......... .......... .......... .......... .......... 52%  107M 4s
125100K .......... .......... .......... .......... .......... 52% 72.0M 4s
125150K .......... .......... .......... .......... .......... 52% 55.4M 4s
125200K .......... .......... .......... .......... .......... 52% 97.4M 4s
125250K .......... .......... .......... .......... .......... 52% 40.3M 4s
125300K .......... .......... .......... .......... .......... 52%  127M 4s
125350K .......... .......... .......... .......... .......... 52% 24.5M 4s
125400K .......... .......... .......... .......... .......... 52% 55.6M 4s
125450K .......... .......... .......... .......... .......... 52% 57.7M 4s
125500K .......... .......... .......... .......... .......... 53% 50.0M 4s
125550K .......... .......... .......... .......... .......... 53% 53.3M 4s
125600K .......... .......... .......... .......... .......... 53% 57.1M 4s
125650K .......... .......... .......... .......... .......... 53% 70.4M 4s
125700K .......... .......... .......... .......... .......... 53% 52.7M 4s
125750K .......... .......... .......... .......... .......... 53% 15.4M 4s
125800K .......... .......... .......... .......... .......... 53% 52.4M 4s
125850K .......... .......... .......... .......... .......... 53% 61.4M 4s
125900K .......... .......... .......... .......... .......... 53% 55.2M 4s
125950K .......... .......... .......... .......... .......... 53% 24.7M 4s
126000K .......... .......... .......... .......... .......... 53% 53.4M 4s
126050K .......... .......... .......... .......... .......... 53% 74.3M 4s
126100K .......... .......... .......... .......... .......... 53% 63.8M 4s
126150K .......... .......... .......... .......... .......... 53% 75.0M 4s
126200K .......... .......... .......... .......... .......... 53% 68.3M 4s
126250K .......... .......... .......... .......... .......... 53% 18.7M 4s
126300K .......... .......... .......... .......... .......... 53%  179M 4s
126350K .......... .......... .......... .......... .......... 53%  214M 4s
126400K .......... .......... .......... .......... .......... 53%  197M 4s
126450K .......... .......... .......... .......... .......... 53%  111M 4s
126500K .......... .......... .......... .......... .......... 53%  149M 4s
126550K .......... .......... .......... .......... .......... 53%  235M 4s
126600K .......... .......... .......... .......... .......... 53% 36.0M 4s
126650K .......... .......... .......... .......... .......... 53% 31.9M 4s
126700K .......... .......... .......... .......... .......... 53% 5.53M 4s
126750K .......... .......... .......... .......... .......... 53% 10.7M 4s
126800K .......... .......... .......... .......... .......... 53% 12.9M 4s
126850K .......... .......... .......... .......... .......... 53% 49.3M 4s
126900K .......... .......... .......... .......... .......... 53% 46.5M 4s
126950K .......... .......... .......... .......... .......... 53% 59.8M 4s
127000K .......... .......... .......... .......... .......... 53% 62.7M 4s
127050K .......... .......... .......... .......... .......... 53% 17.3M 4s
127100K .......... .......... .......... .......... .......... 53% 61.4M 4s
127150K .......... .......... .......... .......... .......... 53% 75.6M 4s
127200K .......... .......... .......... .......... .......... 53% 16.5M 4s
127250K .......... .......... .......... .......... .......... 53% 67.1M 4s
127300K .......... .......... .......... .......... .......... 53% 10.8M 4s
127350K .......... .......... .......... .......... .......... 53% 76.6M 4s
127400K .......... .......... .......... .......... .......... 53% 58.7M 4s
127450K .......... .......... .......... .......... .......... 53% 48.7M 4s
127500K .......... .......... .......... .......... .......... 53% 57.1M 4s
127550K .......... .......... .......... .......... .......... 53% 60.5M 4s
127600K .......... .......... .......... .......... .......... 53% 65.0M 4s
127650K .......... .......... .......... .......... .......... 53% 61.2M 4s
127700K .......... .......... .......... .......... .......... 53% 45.4M 4s
127750K .......... .......... .......... .......... .......... 53% 64.4M 4s
127800K .......... .......... .......... .......... .......... 53% 26.8M 4s
127850K .......... .......... .......... .......... .......... 54% 68.7M 4s
127900K .......... .......... .......... .......... .......... 54% 53.3M 4s
127950K .......... .......... .......... .......... .......... 54% 66.9M 4s
128000K .......... .......... .......... .......... .......... 54% 66.9M 4s
128050K .......... .......... .......... .......... .......... 54% 74.6M 4s
128100K .......... .......... .......... .......... .......... 54% 68.3M 4s
128150K .......... .......... .......... .......... .......... 54% 73.8M 4s
128200K .......... .......... .......... .......... .......... 54% 61.4M 4s
128250K .......... .......... .......... .......... .......... 54% 57.3M 4s
128300K .......... .......... .......... .......... .......... 54% 34.3M 4s
128350K .......... .......... .......... .......... .......... 54% 86.8M 4s
128400K .......... .......... .......... .......... .......... 54% 79.4M 4s
128450K .......... .......... .......... .......... .......... 54% 89.5M 4s
128500K .......... .......... .......... .......... .......... 54% 82.3M 4s
128550K .......... .......... .......... .......... .......... 54% 79.9M 4s
128600K .......... .......... .......... .......... .......... 54% 52.6M 4s
128650K .......... .......... .......... .......... .......... 54% 62.2M 4s
128700K .......... .......... .......... .......... .......... 54% 37.7M 4s
128750K .......... .......... .......... .......... .......... 54% 10.8M 4s
128800K .......... .......... .......... .......... .......... 54% 15.6M 4s
128850K .......... .......... .......... .......... .......... 54%  273M 4s
128900K .......... .......... .......... .......... .......... 54%  265M 4s
128950K .......... .......... .......... .......... .......... 54% 22.9M 4s
129000K .......... .......... .......... .......... .......... 54% 70.1M 4s
129050K .......... .......... .......... .......... .......... 54% 49.9M 4s
129100K .......... .......... .......... .......... .......... 54% 65.1M 4s
129150K .......... .......... .......... .......... .......... 54% 58.6M 4s
129200K .......... .......... .......... .......... .......... 54%  117M 4s
129250K .......... .......... .......... .......... .......... 54% 98.3M 4s
129300K .......... .......... .......... .......... .......... 54% 20.0M 4s
129350K .......... .......... .......... .......... .......... 54% 95.3M 4s
129400K .......... .......... .......... .......... .......... 54% 46.5M 4s
129450K .......... .......... .......... .......... .......... 54% 85.1M 4s
129500K .......... .......... .......... .......... .......... 54% 59.3M 4s
129550K .......... .......... .......... .......... .......... 54% 63.2M 4s
129600K .......... .......... .......... .......... .......... 54% 39.3M 4s
129650K .......... .......... .......... .......... .......... 54% 46.1M 4s
129700K .......... .......... .......... .......... .......... 54%  129M 4s
129750K .......... .......... .......... .......... .......... 54% 13.7M 4s
129800K .......... .......... .......... .......... .......... 54% 80.3M 4s
129850K .......... .......... .......... .......... .......... 54%  102M 4s
129900K .......... .......... .......... .......... .......... 54% 62.9M 4s
129950K .......... .......... .......... .......... .......... 54% 62.7M 4s
130000K .......... .......... .......... .......... .......... 54% 24.5M 4s
130050K .......... .......... .......... .......... .......... 54%  104M 4s
130100K .......... .......... .......... .......... .......... 54% 20.3M 4s
130150K .......... .......... .......... .......... .......... 54%  110M 4s
130200K .......... .......... .......... .......... .......... 55% 71.5M 4s
130250K .......... .......... .......... .......... .......... 55%  111M 4s
130300K .......... .......... .......... .......... .......... 55% 28.0M 4s
130350K .......... .......... .......... .......... .......... 55% 68.8M 4s
130400K .......... .......... .......... .......... .......... 55%  104M 4s
130450K .......... .......... .......... .......... .......... 55% 53.1M 4s
130500K .......... .......... .......... .......... .......... 55% 69.3M 4s
130550K .......... .......... .......... .......... .......... 55%  106M 4s
130600K .......... .......... .......... .......... .......... 55% 48.3M 4s
130650K .......... .......... .......... .......... .......... 55%  115M 4s
130700K .......... .......... .......... .......... .......... 55% 7.66M 4s
130750K .......... .......... .......... .......... .......... 55% 30.7M 4s
130800K .......... .......... .......... .......... .......... 55% 11.7M 4s
130850K .......... .......... .......... .......... .......... 55% 10.8M 4s
130900K .......... .......... .......... .......... .......... 55% 26.6M 4s
130950K .......... .......... .......... .......... .......... 55% 55.8M 4s
131000K .......... .......... .......... .......... .......... 55% 46.1M 4s
131050K .......... .......... .......... .......... .......... 55% 18.0M 4s
131100K .......... .......... .......... .......... .......... 55%  166M 4s
131150K .......... .......... .......... .......... .......... 55% 13.2M 4s
131200K .......... .......... .......... .......... .......... 55% 45.5M 4s
131250K .......... .......... .......... .......... .......... 55%  177M 4s
131300K .......... .......... .......... .......... .......... 55% 14.2M 4s
131350K .......... .......... .......... .......... .......... 55% 89.7M 4s
131400K .......... .......... .......... .......... .......... 55% 22.5M 4s
131450K .......... .......... .......... .......... .......... 55% 72.4M 4s
131500K .......... .......... .......... .......... .......... 55% 38.6M 4s
131550K .......... .......... .......... .......... .......... 55% 56.7M 4s
131600K .......... .......... .......... .......... .......... 55% 94.3M 4s
131650K .......... .......... .......... .......... .......... 55% 63.5M 4s
131700K .......... .......... .......... .......... .......... 55% 84.2M 3s
131750K .......... .......... .......... .......... .......... 55% 84.0M 3s
131800K .......... .......... .......... .......... .......... 55% 77.5M 3s
131850K .......... .......... .......... .......... .......... 55% 21.6M 3s
131900K .......... .......... .......... .......... .......... 55% 73.7M 3s
131950K .......... .......... .......... .......... .......... 55%  110M 3s
132000K .......... .......... .......... .......... .......... 55% 44.3M 3s
132050K .......... .......... .......... .......... .......... 55% 21.3M 3s
132100K .......... .......... .......... .......... .......... 55% 66.3M 3s
132150K .......... .......... .......... .......... .......... 55% 53.0M 3s
132200K .......... .......... .......... .......... .......... 55%  117M 3s
132250K .......... .......... .......... .......... .......... 55% 64.2M 3s
132300K .......... .......... .......... .......... .......... 55%  119M 3s
132350K .......... .......... .......... .......... .......... 55% 41.1M 3s
132400K .......... .......... .......... .......... .......... 55% 83.7M 3s
132450K .......... .......... .......... .......... .......... 55% 35.7M 3s
132500K .......... .......... .......... .......... .......... 55%  111M 3s
132550K .......... .......... .......... .......... .......... 55% 88.2M 3s
132600K .......... .......... .......... .......... .......... 56% 54.8M 3s
132650K .......... .......... .......... .......... .......... 56% 66.9M 3s
132700K .......... .......... .......... .......... .......... 56% 86.1M 3s
132750K .......... .......... .......... .......... .......... 56%  113M 3s
132800K .......... .......... .......... .......... .......... 56% 51.6M 3s
132850K .......... .......... .......... .......... .......... 56% 10.5M 3s
132900K .......... .......... .......... .......... .......... 56% 18.5M 3s
132950K .......... .......... .......... .......... .......... 56%  170M 3s
133000K .......... .......... .......... .......... .......... 56% 71.2M 3s
133050K .......... .......... .......... .......... .......... 56% 48.0M 3s
133100K .......... .......... .......... .......... .......... 56% 59.1M 3s
133150K .......... .......... .......... .......... .......... 56% 40.3M 3s
133200K .......... .......... .......... .......... .......... 56%  128M 3s
133250K .......... .......... .......... .......... .......... 56% 48.2M 3s
133300K .......... .......... .......... .......... .......... 56%  147M 3s
133350K .......... .......... .......... .......... .......... 56% 25.1M 3s
133400K .......... .......... .......... .......... .......... 56% 55.0M 3s
133450K .......... .......... .......... .......... .......... 56%  165M 3s
133500K .......... .......... .......... .......... .......... 56% 51.1M 3s
133550K .......... .......... .......... .......... .......... 56% 49.2M 3s
133600K .......... .......... .......... .......... .......... 56% 43.1M 3s
133650K .......... .......... .......... .......... .......... 56% 61.3M 3s
133700K .......... .......... .......... .......... .......... 56% 60.1M 3s
133750K .......... .......... .......... .......... .......... 56% 14.0M 3s
133800K .......... .......... .......... .......... .......... 56% 89.9M 3s
133850K .......... .......... .......... .......... .......... 56% 63.5M 3s
133900K .......... .......... .......... .......... .......... 56% 83.4M 3s
133950K .......... .......... .......... .......... .......... 56% 51.4M 3s
134000K .......... .......... .......... .......... .......... 56% 47.7M 3s
134050K .......... .......... .......... .......... .......... 56% 66.5M 3s
134100K .......... .......... .......... .......... .......... 56% 35.8M 3s
134150K .......... .......... .......... .......... .......... 56%  101M 3s
134200K .......... .......... .......... .......... .......... 56% 26.9M 3s
134250K .......... .......... .......... .......... .......... 56% 47.1M 3s
134300K .......... .......... .......... .......... .......... 56% 69.5M 3s
134350K .......... .......... .......... .......... .......... 56% 55.3M 3s
134400K .......... .......... .......... .......... .......... 56% 60.1M 3s
134450K .......... .......... .......... .......... .......... 56% 50.0M 3s
134500K .......... .......... .......... .......... .......... 56% 68.6M 3s
134550K .......... .......... .......... .......... .......... 56% 51.0M 3s
134600K .......... .......... .......... .......... .......... 56% 53.8M 3s
134650K .......... .......... .......... .......... .......... 56% 76.5M 3s
134700K .......... .......... .......... .......... .......... 56% 8.18M 3s
134750K .......... .......... .......... .......... .......... 56% 53.7M 3s
134800K .......... .......... .......... .......... .......... 56% 19.0M 3s
134850K .......... .......... .......... .......... .......... 56% 3.93M 3s
134900K .......... .......... .......... .......... .......... 56%  226M 3s
134950K .......... .......... .......... .......... .......... 57%  301M 3s
135000K .......... .......... .......... .......... .......... 57%  205M 3s
135050K .......... .......... .......... .......... .......... 57%  295M 3s
135100K .......... .......... .......... .......... .......... 57% 65.6M 3s
135150K .......... .......... .......... .......... .......... 57% 11.9M 3s
135200K .......... .......... .......... .......... .......... 57% 95.0M 3s
135250K .......... .......... .......... .......... .......... 57%  130M 3s
135300K .......... .......... .......... .......... .......... 57% 83.8M 3s
135350K .......... .......... .......... .......... .......... 57% 54.4M 3s
135400K .......... .......... .......... .......... .......... 57% 10.8M 3s
135450K .......... .......... .......... .......... .......... 57% 48.1M 3s
135500K .......... .......... .......... .......... .......... 57% 52.2M 3s
135550K .......... .......... .......... .......... .......... 57% 54.0M 3s
135600K .......... .......... .......... .......... .......... 57% 26.4M 3s
135650K .......... .......... .......... .......... .......... 57% 62.0M 3s
135700K .......... .......... .......... .......... .......... 57% 65.8M 3s
135750K .......... .......... .......... .......... .......... 57% 56.3M 3s
135800K .......... .......... .......... .......... .......... 57% 58.4M 3s
135850K .......... .......... .......... .......... .......... 57% 48.1M 3s
135900K .......... .......... .......... .......... .......... 57%  246M 3s
135950K .......... .......... .......... .......... .......... 57%  146M 3s
136000K .......... .......... .......... .......... .......... 57%  111M 3s
136050K .......... .......... .......... .......... .......... 57% 43.8M 3s
136100K .......... .......... .......... .......... .......... 57% 5.56M 3s
136150K .......... .......... .......... .......... .......... 57%  274M 3s
136200K .......... .......... .......... .......... .......... 57%  260M 3s
136250K .......... .......... .......... .......... .......... 57%  237M 3s
136300K .......... .......... .......... .......... .......... 57%  203M 3s
136350K .......... .......... .......... .......... .......... 57% 7.54M 3s
136400K .......... .......... .......... .......... .......... 57% 71.9M 3s
136450K .......... .......... .......... .......... .......... 57% 67.9M 3s
136500K .......... .......... .......... .......... .......... 57% 66.5M 3s
136550K .......... .......... .......... .......... .......... 57% 21.1M 3s
136600K .......... .......... .......... .......... .......... 57% 32.6M 3s
136650K .......... .......... .......... .......... .......... 57% 17.5M 3s
136700K .......... .......... .......... .......... .......... 57% 54.4M 3s
136750K .......... .......... .......... .......... .......... 57% 60.9M 3s
136800K .......... .......... .......... .......... .......... 57% 62.0M 3s
136850K .......... .......... .......... .......... .......... 57% 39.7M 3s
136900K .......... .......... .......... .......... .......... 57% 54.0M 3s
136950K .......... .......... .......... .......... .......... 57% 53.3M 3s
137000K .......... .......... .......... .......... .......... 57% 51.4M 3s
137050K .......... .......... .......... .......... .......... 57% 67.6M 3s
137100K .......... .......... .......... .......... .......... 57% 26.0M 3s
137150K .......... .......... .......... .......... .......... 57% 69.8M 3s
137200K .......... .......... .......... .......... .......... 57% 82.6M 3s
137250K .......... .......... .......... .......... .......... 57% 70.4M 3s
137300K .......... .......... .......... .......... .......... 57% 43.2M 3s
137350K .......... .......... .......... .......... .......... 58% 67.5M 3s
137400K .......... .......... .......... .......... .......... 58% 71.6M 3s
137450K .......... .......... .......... .......... .......... 58% 64.4M 3s
137500K .......... .......... .......... .......... .......... 58% 53.9M 3s
137550K .......... .......... .......... .......... .......... 58% 66.5M 3s
137600K .......... .......... .......... .......... .......... 58% 71.4M 3s
137650K .......... .......... .......... .......... .......... 58% 53.9M 3s
137700K .......... .......... .......... .......... .......... 58% 65.6M 3s
137750K .......... .......... .......... .......... .......... 58% 69.6M 3s
137800K .......... .......... .......... .......... .......... 58% 68.5M 3s
137850K .......... .......... .......... .......... .......... 58% 65.3M 3s
137900K .......... .......... .......... .......... .......... 58% 55.4M 3s
137950K .......... .......... .......... .......... .......... 58% 62.1M 3s
138000K .......... .......... .......... .......... .......... 58% 60.8M 3s
138050K .......... .......... .......... .......... .......... 58% 64.5M 3s
138100K .......... .......... .......... .......... .......... 58% 49.4M 3s
138150K .......... .......... .......... .......... .......... 58% 61.0M 3s
138200K .......... .......... .......... .......... .......... 58% 64.2M 3s
138250K .......... .......... .......... .......... .......... 58% 64.6M 3s
138300K .......... .......... .......... .......... .......... 58% 62.0M 3s
138350K .......... .......... .......... .......... .......... 58% 37.1M 3s
138400K .......... .......... .......... .......... .......... 58% 66.4M 3s
138450K .......... .......... .......... .......... .......... 58% 74.1M 3s
138500K .......... .......... .......... .......... .......... 58% 67.2M 3s
138550K .......... .......... .......... .......... .......... 58% 76.9M 3s
138600K .......... .......... .......... .......... .......... 58% 73.6M 3s
138650K .......... .......... .......... .......... .......... 58% 39.3M 3s
138700K .......... .......... .......... .......... .......... 58%  218M 3s
138750K .......... .......... .......... .......... .......... 58%  266M 3s
138800K .......... .......... .......... .......... .......... 58% 49.4M 3s
138850K .......... .......... .......... .......... .......... 58% 21.4M 3s
138900K .......... .......... .......... .......... .......... 58% 19.0M 3s
138950K .......... .......... .......... .......... .......... 58% 4.53M 3s
139000K .......... .......... .......... .......... .......... 58% 45.4M 3s
139050K .......... .......... .......... .......... .......... 58% 61.6M 3s
139100K .......... .......... .......... .......... .......... 58% 73.2M 3s
139150K .......... .......... .......... .......... .......... 58% 29.0M 3s
139200K .......... .......... .......... .......... .......... 58% 48.4M 3s
139250K .......... .......... .......... .......... .......... 58% 65.9M 3s
139300K .......... .......... .......... .......... .......... 58% 58.5M 3s
139350K .......... .......... .......... .......... .......... 58% 73.2M 3s
139400K .......... .......... .......... .......... .......... 58% 32.9M 3s
139450K .......... .......... .......... .......... .......... 58% 18.5M 3s
139500K .......... .......... .......... .......... .......... 58% 33.2M 3s
139550K .......... .......... .......... .......... .......... 58% 5.93M 3s
139600K .......... .......... .......... .......... .......... 58% 57.7M 3s
139650K .......... .......... .......... .......... .......... 58% 10.2M 3s
139700K .......... .......... .......... .......... .......... 59% 66.1M 3s
139750K .......... .......... .......... .......... .......... 59% 71.3M 3s
139800K .......... .......... .......... .......... .......... 59% 63.0M 3s
139850K .......... .......... .......... .......... .......... 59% 72.5M 3s
139900K .......... .......... .......... .......... .......... 59% 71.8M 3s
139950K .......... .......... .......... .......... .......... 59% 75.9M 3s
140000K .......... .......... .......... .......... .......... 59% 64.0M 3s
140050K .......... .......... .......... .......... .......... 59% 51.5M 3s
140100K .......... .......... .......... .......... .......... 59% 6.29M 3s
140150K .......... .......... .......... .......... .......... 59% 31.8M 3s
140200K .......... .......... .......... .......... .......... 59% 72.2M 3s
140250K .......... .......... .......... .......... .......... 59% 92.0M 3s
140300K .......... .......... .......... .......... .......... 59% 78.5M 3s
140350K .......... .......... .......... .......... .......... 59% 83.7M 3s
140400K .......... .......... .......... .......... .......... 59% 68.2M 3s
140450K .......... .......... .......... .......... .......... 59% 74.9M 3s
140500K .......... .......... .......... .......... .......... 59% 81.7M 3s
140550K .......... .......... .......... .......... .......... 59% 70.9M 3s
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140650K .......... .......... .......... .......... .......... 59% 13.7M 3s
140700K .......... .......... .......... .......... .......... 59% 60.5M 3s
140750K .......... .......... .......... .......... .......... 59% 69.9M 3s
140800K .......... .......... .......... .......... .......... 59% 60.5M 3s
140850K .......... .......... .......... .......... .......... 59% 68.4M 3s
140900K .......... .......... .......... .......... .......... 59% 72.8M 3s
140950K .......... .......... .......... .......... .......... 59% 72.7M 3s
141000K .......... .......... .......... .......... .......... 59% 6.79M 3s
141050K .......... .......... .......... .......... .......... 59% 6.62M 3s
141100K .......... .......... .......... .......... .......... 59% 4.93M 3s
141150K .......... .......... .......... .......... .......... 59% 16.6M 3s
141200K .......... .......... .......... .......... .......... 59% 62.5M 3s
141250K .......... .......... .......... .......... .......... 59% 72.9M 3s
141300K .......... .......... .......... .......... .......... 59% 73.6M 3s
141350K .......... .......... .......... .......... .......... 59% 70.6M 3s
141400K .......... .......... .......... .......... .......... 59% 29.8M 3s
141450K .......... .......... .......... .......... .......... 59% 76.6M 3s
141500K .......... .......... .......... .......... .......... 59% 74.6M 3s
141550K .......... .......... .......... .......... .......... 59% 45.1M 3s
141600K .......... .......... .......... .......... .......... 59% 29.2M 3s
141650K .......... .......... .......... .......... .......... 59% 77.0M 3s
141700K .......... .......... .......... .......... .......... 59% 70.6M 3s
141750K .......... .......... .......... .......... .......... 59% 77.1M 3s
141800K .......... .......... .......... .......... .......... 59% 69.2M 3s
141850K .......... .......... .......... .......... .......... 59% 74.6M 3s
141900K .......... .......... .......... .......... .......... 59% 78.3M 3s
141950K .......... .......... .......... .......... .......... 59% 83.4M 3s
142000K .......... .......... .......... .......... .......... 59% 70.6M 3s
142050K .......... .......... .......... .......... .......... 60% 76.0M 3s
142100K .......... .......... .......... .......... .......... 60% 77.0M 3s
142150K .......... .......... .......... .......... .......... 60% 75.0M 3s
142200K .......... .......... .......... .......... .......... 60% 56.6M 3s
142250K .......... .......... .......... .......... .......... 60% 63.8M 3s
142300K .......... .......... .......... .......... .......... 60% 81.1M 3s
142350K .......... .......... .......... .......... .......... 60% 79.6M 3s
142400K .......... .......... .......... .......... .......... 60% 70.6M 3s
142450K .......... .......... .......... .......... .......... 60%  108M 3s
142500K .......... .......... .......... .......... .......... 60%  341M 3s
142550K .......... .......... .......... .......... .......... 60%  269M 3s
142600K .......... .......... .......... .......... .......... 60%  166M 3s
142650K .......... .......... .......... .......... .......... 60%  204M 3s
142700K .......... .......... .......... .......... .......... 60%  163M 3s
142750K .......... .......... .......... .......... .......... 60%  128M 3s
142800K .......... .......... .......... .......... .......... 60%  184M 3s
142850K .......... .......... .......... .......... .......... 60%  300M 3s
142900K .......... .......... .......... .......... .......... 60%  319M 3s
142950K .......... .......... .......... .......... .......... 60% 89.5M 3s
143000K .......... .......... .......... .......... .......... 60%  106M 3s
143050K .......... .......... .......... .......... .......... 60%  303M 3s
143100K .......... .......... .......... .......... .......... 60%  316M 3s
143150K .......... .......... .......... .......... .......... 60%  264M 3s
143200K .......... .......... .......... .......... .......... 60%  220M 3s
143250K .......... .......... .......... .......... .......... 60%  220M 3s
143300K .......... .......... .......... .......... .......... 60%  271M 3s
143350K .......... .......... .......... .......... .......... 60%  259M 3s
143400K .......... .......... .......... .......... .......... 60%  172M 3s
143450K .......... .......... .......... .......... .......... 60% 19.9M 3s
143500K .......... .......... .......... .......... .......... 60%  176M 3s
143550K .......... .......... .......... .......... .......... 60% 76.2M 3s
143600K .......... .......... .......... .......... .......... 60% 5.71M 3s
143650K .......... .......... .......... .......... .......... 60% 87.0M 3s
143700K .......... .......... .......... .......... .......... 60% 23.5M 3s
143750K .......... .......... .......... .......... .......... 60% 8.04M 3s
143800K .......... .......... .......... .......... .......... 60% 59.2M 3s
143850K .......... .......... .......... .......... .......... 60%  103M 3s
143900K .......... .......... .......... .......... .......... 60% 76.7M 3s
143950K .......... .......... .......... .......... .......... 60% 91.2M 3s
144000K .......... .......... .......... .......... .......... 60% 46.8M 3s
144050K .......... .......... .......... .......... .......... 60% 7.14M 3s
144100K .......... .......... .......... .......... .......... 60%  117M 3s
144150K .......... .......... .......... .......... .......... 60% 83.7M 3s
144200K .......... .......... .......... .......... .......... 60% 67.7M 3s
144250K .......... .......... .......... .......... .......... 60% 30.5M 3s
144300K .......... .......... .......... .......... .......... 60%  198M 3s
144350K .......... .......... .......... .......... .......... 60% 51.4M 3s
144400K .......... .......... .......... .......... .......... 60% 57.3M 3s
144450K .......... .......... .......... .......... .......... 61% 64.5M 3s
144500K .......... .......... .......... .......... .......... 61%  137M 3s
144550K .......... .......... .......... .......... .......... 61% 39.0M 3s
144600K .......... .......... .......... .......... .......... 61%  122M 3s
144650K .......... .......... .......... .......... .......... 61% 53.5M 3s
144700K .......... .......... .......... .......... .......... 61% 16.7M 3s
144750K .......... .......... .......... .......... .......... 61%  144M 3s
144800K .......... .......... .......... .......... .......... 61% 74.6M 3s
144850K .......... .......... .......... .......... .......... 61% 59.4M 3s
144900K .......... .......... .......... .......... .......... 61% 78.0M 3s
144950K .......... .......... .......... .......... .......... 61% 28.4M 3s
145000K .......... .......... .......... .......... .......... 61% 7.33M 3s
145050K .......... .......... .......... .......... .......... 61% 87.1M 3s
145100K .......... .......... .......... .......... .......... 61%  101M 3s
145150K .......... .......... .......... .......... .......... 61% 52.5M 3s
145200K .......... .......... .......... .......... .......... 61% 5.23M 3s
145250K .......... .......... .......... .......... .......... 61%  275M 3s
145300K .......... .......... .......... .......... .......... 61% 8.92M 3s
145350K .......... .......... .......... .......... .......... 61% 71.2M 3s
145400K .......... .......... .......... .......... .......... 61% 18.9M 3s
145450K .......... .......... .......... .......... .......... 61% 64.8M 3s
145500K .......... .......... .......... .......... .......... 61% 83.2M 3s
145550K .......... .......... .......... .......... .......... 61% 45.6M 3s
145600K .......... .......... .......... .......... .......... 61% 63.5M 3s
145650K .......... .......... .......... .......... .......... 61% 94.3M 3s
145700K .......... .......... .......... .......... .......... 61% 35.5M 3s
145750K .......... .......... .......... .......... .......... 61% 5.09M 3s
145800K .......... .......... .......... .......... .......... 61%  103M 3s
145850K .......... .......... .......... .......... .......... 61% 56.6M 3s
145900K .......... .......... .......... .......... .......... 61%  118M 3s
145950K .......... .......... .......... .......... .......... 61% 84.8M 3s
146000K .......... .......... .......... .......... .......... 61% 89.4M 3s
146050K .......... .......... .......... .......... .......... 61% 89.9M 3s
146100K .......... .......... .......... .......... .......... 61% 76.0M 3s
146150K .......... .......... .......... .......... .......... 61% 75.8M 3s
146200K .......... .......... .......... .......... .......... 61% 72.8M 3s
146250K .......... .......... .......... .......... .......... 61% 78.2M 3s
146300K .......... .......... .......... .......... .......... 61% 79.5M 3s
146350K .......... .......... .......... .......... .......... 61% 48.4M 3s
146400K .......... .......... .......... .......... .......... 61% 98.6M 3s
146450K .......... .......... .......... .......... .......... 61% 83.5M 3s
146500K .......... .......... .......... .......... .......... 61% 88.3M 3s
146550K .......... .......... .......... .......... .......... 61% 56.4M 3s
146600K .......... .......... .......... .......... .......... 61%  116M 3s
146650K .......... .......... .......... .......... .......... 61%  127M 3s
146700K .......... .......... .......... .......... .......... 61% 79.7M 3s
146750K .......... .......... .......... .......... .......... 61% 96.7M 3s
146800K .......... .......... .......... .......... .......... 62% 71.4M 3s
146850K .......... .......... .......... .......... .......... 62% 27.9M 3s
146900K .......... .......... .......... .......... .......... 62%  160M 3s
146950K .......... .......... .......... .......... .......... 62%  246M 3s
147000K .......... .......... .......... .......... .......... 62% 24.1M 3s
147050K .......... .......... .......... .......... .......... 62% 67.8M 3s
147100K .......... .......... .......... .......... .......... 62% 72.4M 3s
147150K .......... .......... .......... .......... .......... 62% 66.9M 3s
147200K .......... .......... .......... .......... .......... 62% 58.3M 3s
147250K .......... .......... .......... .......... .......... 62% 69.5M 3s
147300K .......... .......... .......... .......... .......... 62% 67.3M 3s
147350K .......... .......... .......... .......... .......... 62% 72.0M 3s
147400K .......... .......... .......... .......... .......... 62% 63.3M 3s
147450K .......... .......... .......... .......... .......... 62% 68.7M 3s
147500K .......... .......... .......... .......... .......... 62% 70.8M 3s
147550K .......... .......... .......... .......... .......... 62% 60.7M 3s
147600K .......... .......... .......... .......... .......... 62% 65.8M 3s
147650K .......... .......... .......... .......... .......... 62% 66.8M 3s
147700K .......... .......... .......... .......... .......... 62% 73.5M 3s
147750K .......... .......... .......... .......... .......... 62% 75.5M 3s
147800K .......... .......... .......... .......... .......... 62% 15.1M 3s
147850K .......... .......... .......... .......... .......... 62% 59.5M 3s
147900K .......... .......... .......... .......... .......... 62% 71.6M 3s
147950K .......... .......... .......... .......... .......... 62% 67.9M 3s
148000K .......... .......... .......... .......... .......... 62% 72.0M 3s
148050K .......... .......... .......... .......... .......... 62% 11.1M 3s
148100K .......... .......... .......... .......... .......... 62% 18.3M 3s
148150K .......... .......... .......... .......... .......... 62% 17.5M 3s
148200K .......... .......... .......... .......... .......... 62% 54.2M 3s
148250K .......... .......... .......... .......... .......... 62% 47.3M 3s
148300K .......... .......... .......... .......... .......... 62% 61.4M 3s
148350K .......... .......... .......... .......... .......... 62% 51.4M 3s
148400K .......... .......... .......... .......... .......... 62% 53.0M 3s
148450K .......... .......... .......... .......... .......... 62% 23.1M 3s
148500K .......... .......... .......... .......... .......... 62% 69.8M 3s
148550K .......... .......... .......... .......... .......... 62% 65.6M 3s
148600K .......... .......... .......... .......... .......... 62% 7.20M 3s
148650K .......... .......... .......... .......... .......... 62% 50.3M 3s
148700K .......... .......... .......... .......... .......... 62% 49.4M 3s
148750K .......... .......... .......... .......... .......... 62% 49.1M 3s
148800K .......... .......... .......... .......... .......... 62% 16.9M 3s
148850K .......... .......... .......... .......... .......... 62% 62.4M 3s
148900K .......... .......... .......... .......... .......... 62% 69.9M 3s
148950K .......... .......... .......... .......... .......... 62% 66.4M 3s
149000K .......... .......... .......... .......... .......... 62% 6.30M 3s
149050K .......... .......... .......... .......... .......... 62% 66.2M 3s
149100K .......... .......... .......... .......... .......... 62% 77.5M 3s
149150K .......... .......... .......... .......... .......... 63% 72.7M 3s
149200K .......... .......... .......... .......... .......... 63% 65.3M 3s
149250K .......... .......... .......... .......... .......... 63% 80.2M 3s
149300K .......... .......... .......... .......... .......... 63% 6.70M 3s
149350K .......... .......... .......... .......... .......... 63%  272M 3s
149400K .......... .......... .......... .......... .......... 63%  299M 3s
149450K .......... .......... .......... .......... .......... 63%  270M 3s
149500K .......... .......... .......... .......... .......... 63%  309M 3s
149550K .......... .......... .......... .......... .......... 63%  309M 3s
149600K .......... .......... .......... .......... .......... 63%  126M 3s
149650K .......... .......... .......... .......... .......... 63% 54.6M 3s
149700K .......... .......... .......... .......... .......... 63% 39.7M 3s
149750K .......... .......... .......... .......... .......... 63%  115M 3s
149800K .......... .......... .......... .......... .......... 63% 5.29M 3s
149850K .......... .......... .......... .......... .......... 63% 60.7M 3s
149900K .......... .......... .......... .......... .......... 63% 53.0M 3s
149950K .......... .......... .......... .......... .......... 63% 45.0M 3s
150000K .......... .......... .......... .......... .......... 63% 92.2M 3s
150050K .......... .......... .......... .......... .......... 63% 58.3M 3s
150100K .......... .......... .......... .......... .......... 63% 28.6M 3s
150150K .......... .......... .......... .......... .......... 63%  212M 3s
150200K .......... .......... .......... .......... .......... 63%  312M 3s
150250K .......... .......... .......... .......... .......... 63%  129M 3s
150300K .......... .......... .......... .......... .......... 63% 84.8M 3s
150350K .......... .......... .......... .......... .......... 63%  122M 3s
150400K .......... .......... .......... .......... .......... 63% 64.6M 3s
150450K .......... .......... .......... .......... .......... 63% 82.6M 3s
150500K .......... .......... .......... .......... .......... 63% 79.5M 3s
150550K .......... .......... .......... .......... .......... 63% 41.4M 3s
150600K .......... .......... .......... .......... .......... 63% 58.1M 3s
150650K .......... .......... .......... .......... .......... 63%  123M 3s
150700K .......... .......... .......... .......... .......... 63% 93.4M 3s
150750K .......... .......... .......... .......... .......... 63% 51.8M 3s
150800K .......... .......... .......... .......... .......... 63%  131M 3s
150850K .......... .......... .......... .......... .......... 63% 61.0M 3s
150900K .......... .......... .......... .......... .......... 63% 44.3M 3s
150950K .......... .......... .......... .......... .......... 63%  142M 3s
151000K .......... .......... .......... .......... .......... 63% 72.3M 3s
151050K .......... .......... .......... .......... .......... 63% 57.8M 3s
151100K .......... .......... .......... .......... .......... 63% 53.4M 3s
151150K .......... .......... .......... .......... .......... 63% 32.7M 3s
151200K .......... .......... .......... .......... .......... 63%  105M 3s
151250K .......... .......... .......... .......... .......... 63% 69.1M 3s
151300K .......... .......... .......... .......... .......... 63% 84.0M 3s
151350K .......... .......... .......... .......... .......... 63% 59.0M 3s
151400K .......... .......... .......... .......... .......... 63% 86.3M 3s
151450K .......... .......... .......... .......... .......... 63%  105M 3s
151500K .......... .......... .......... .......... .......... 63% 51.2M 3s
151550K .......... .......... .......... .......... .......... 64% 85.7M 3s
151600K .......... .......... .......... .......... .......... 64% 17.6M 3s
151650K .......... .......... .......... .......... .......... 64%  208M 3s
151700K .......... .......... .......... .......... .......... 64% 67.5M 3s
151750K .......... .......... .......... .......... .......... 64% 24.4M 3s
151800K .......... .......... .......... .......... .......... 64%  110M 3s
151850K .......... .......... .......... .......... .......... 64% 47.7M 3s
151900K .......... .......... .......... .......... .......... 64% 44.4M 3s
151950K .......... .......... .......... .......... .......... 64% 48.0M 3s
152000K .......... .......... .......... .......... .......... 64% 65.4M 3s
152050K .......... .......... .......... .......... .......... 64% 60.9M 3s
152100K .......... .......... .......... .......... .......... 64% 28.3M 3s
152150K .......... .......... .......... .......... .......... 64% 11.0M 3s
152200K .......... .......... .......... .......... .......... 64% 33.0M 3s
152250K .......... .......... .......... .......... .......... 64% 22.5M 3s
152300K .......... .......... .......... .......... .......... 64% 85.6M 3s
152350K .......... .......... .......... .......... .......... 64% 75.9M 3s
152400K .......... .......... .......... .......... .......... 64% 47.1M 3s
152450K .......... .......... .......... .......... .......... 64% 62.4M 3s
152500K .......... .......... .......... .......... .......... 64% 14.2M 3s
152550K .......... .......... .......... .......... .......... 64%  137M 3s
152600K .......... .......... .......... .......... .......... 64% 75.6M 3s
152650K .......... .......... .......... .......... .......... 64% 3.52M 3s
152700K .......... .......... .......... .......... .......... 64%  233M 3s
152750K .......... .......... .......... .......... .......... 64%  211M 3s
152800K .......... .......... .......... .......... .......... 64%  202M 3s
152850K .......... .......... .......... .......... .......... 64%  268M 3s
152900K .......... .......... .......... .......... .......... 64%  297M 3s
152950K .......... .......... .......... .......... .......... 64%  254M 3s
153000K .......... .......... .......... .......... .......... 64%  316M 3s
153050K .......... .......... .......... .......... .......... 64% 7.27M 3s
153100K .......... .......... .......... .......... .......... 64% 8.37M 3s
153150K .......... .......... .......... .......... .......... 64%  145M 3s
153200K .......... .......... .......... .......... .......... 64% 55.4M 3s
153250K .......... .......... .......... .......... .......... 64%  140M 3s
153300K .......... .......... .......... .......... .......... 64% 94.6M 3s
153350K .......... .......... .......... .......... .......... 64% 20.5M 3s
153400K .......... .......... .......... .......... .......... 64% 51.9M 3s
153450K .......... .......... .......... .......... .......... 64% 75.6M 3s
153500K .......... .......... .......... .......... .......... 64%  106M 3s
153550K .......... .......... .......... .......... .......... 64% 92.5M 3s
153600K .......... .......... .......... .......... .......... 64% 89.1M 3s
153650K .......... .......... .......... .......... .......... 64% 42.3M 3s
153700K .......... .......... .......... .......... .......... 64% 78.9M 3s
153750K .......... .......... .......... .......... .......... 64% 86.3M 3s
153800K .......... .......... .......... .......... .......... 64% 45.3M 3s
153850K .......... .......... .......... .......... .......... 64% 7.49M 3s
153900K .......... .......... .......... .......... .......... 65% 65.7M 3s
153950K .......... .......... .......... .......... .......... 65% 48.5M 3s
154000K .......... .......... .......... .......... .......... 65% 81.5M 3s
154050K .......... .......... .......... .......... .......... 65% 46.4M 3s
154100K .......... .......... .......... .......... .......... 65% 19.2M 3s
154150K .......... .......... .......... .......... .......... 65% 66.4M 3s
154200K .......... .......... .......... .......... .......... 65%  157M 3s
154250K .......... .......... .......... .......... .......... 65% 52.1M 3s
154300K .......... .......... .......... .......... .......... 65% 83.9M 3s
154350K .......... .......... .......... .......... .......... 65% 57.2M 3s
154400K .......... .......... .......... .......... .......... 65% 54.3M 3s
154450K .......... .......... .......... .......... .......... 65% 66.2M 3s
154500K .......... .......... .......... .......... .......... 65% 60.8M 3s
154550K .......... .......... .......... .......... .......... 65% 53.9M 3s
154600K .......... .......... .......... .......... .......... 65% 50.3M 3s
154650K .......... .......... .......... .......... .......... 65% 49.1M 3s
154700K .......... .......... .......... .......... .......... 65% 38.2M 3s
154750K .......... .......... .......... .......... .......... 65% 50.0M 3s
154800K .......... .......... .......... .......... .......... 65% 53.2M 3s
154850K .......... .......... .......... .......... .......... 65% 50.3M 3s
154900K .......... .......... .......... .......... .......... 65% 52.7M 3s
154950K .......... .......... .......... .......... .......... 65% 65.5M 3s
155000K .......... .......... .......... .......... .......... 65% 63.6M 3s
155050K .......... .......... .......... .......... .......... 65% 50.9M 3s
155100K .......... .......... .......... .......... .......... 65% 55.7M 3s
155150K .......... .......... .......... .......... .......... 65% 62.6M 3s
155200K .......... .......... .......... .......... .......... 65% 73.9M 3s
155250K .......... .......... .......... .......... .......... 65% 78.9M 3s
155300K .......... .......... .......... .......... .......... 65% 65.4M 3s
155350K .......... .......... .......... .......... .......... 65% 88.0M 3s
155400K .......... .......... .......... .......... .......... 65% 94.7M 3s
155450K .......... .......... .......... .......... .......... 65% 85.9M 3s
155500K .......... .......... .......... .......... .......... 65% 35.3M 3s
155550K .......... .......... .......... .......... .......... 65% 3.61M 3s
155600K .......... .......... .......... .......... .......... 65% 68.4M 3s
155650K .......... .......... .......... .......... .......... 65% 59.0M 3s
155700K .......... .......... .......... .......... .......... 65% 61.0M 3s
155750K .......... .......... .......... .......... .......... 65% 60.4M 3s
155800K .......... .......... .......... .......... .......... 65% 5.20M 3s
155850K .......... .......... .......... .......... .......... 65% 2.89M 3s
155900K .......... .......... .......... .......... .......... 65% 13.5M 3s
155950K .......... .......... .......... .......... .......... 65% 11.4M 3s
156000K .......... .......... .......... .......... .......... 65% 60.4M 3s
156050K .......... .......... .......... .......... .......... 65% 19.5M 3s
156100K .......... .......... .......... .......... .......... 65% 42.0M 3s
156150K .......... .......... .......... .......... .......... 65% 59.4M 3s
156200K .......... .......... .......... .......... .......... 65% 49.8M 3s
156250K .......... .......... .......... .......... .......... 66% 56.0M 3s
156300K .......... .......... .......... .......... .......... 66% 9.17M 3s
156350K .......... .......... .......... .......... .......... 66% 56.0M 3s
156400K .......... .......... .......... .......... .......... 66% 35.1M 3s
156450K .......... .......... .......... .......... .......... 66% 15.3M 3s
156500K .......... .......... .......... .......... .......... 66% 23.9M 3s
156550K .......... .......... .......... .......... .......... 66% 63.7M 3s
156600K .......... .......... .......... .......... .......... 66% 62.1M 3s
156650K .......... .......... .......... .......... .......... 66% 4.77M 3s
156700K .......... .......... .......... .......... .......... 66% 69.1M 3s
156750K .......... .......... .......... .......... .......... 66% 59.0M 3s
156800K .......... .......... .......... .......... .......... 66% 61.3M 3s
156850K .......... .......... .......... .......... .......... 66% 39.9M 3s
156900K .......... .......... .......... .......... .......... 66% 53.7M 3s
156950K .......... .......... .......... .......... .......... 66% 60.2M 3s
157000K .......... .......... .......... .......... .......... 66% 64.5M 3s
157050K .......... .......... .......... .......... .......... 66% 72.3M 3s
157100K .......... .......... .......... .......... .......... 66% 38.5M 3s
157150K .......... .......... .......... .......... .......... 66% 24.0M 3s
157200K .......... .......... .......... .......... .......... 66% 12.6M 3s
157250K .......... .......... .......... .......... .......... 66% 57.2M 3s
157300K .......... .......... .......... .......... .......... 66% 9.98M 3s
157350K .......... .......... .......... .......... .......... 66% 70.7M 3s
157400K .......... .......... .......... .......... .......... 66% 68.8M 3s
157450K .......... .......... .......... .......... .......... 66% 78.9M 3s
157500K .......... .......... .......... .......... .......... 66% 63.6M 3s
157550K .......... .......... .......... .......... .......... 66% 13.1M 3s
157600K .......... .......... .......... .......... .......... 66% 67.3M 3s
157650K .......... .......... .......... .......... .......... 66% 77.8M 3s
157700K .......... .......... .......... .......... .......... 66% 72.9M 3s
157750K .......... .......... .......... .......... .......... 66% 58.2M 3s
157800K .......... .......... .......... .......... .......... 66% 16.9M 3s
157850K .......... .......... .......... .......... .......... 66% 79.3M 3s
157900K .......... .......... .......... .......... .......... 66% 9.17M 3s
157950K .......... .......... .......... .......... .......... 66% 76.0M 3s
158000K .......... .......... .......... .......... .......... 66% 74.6M 3s
158050K .......... .......... .......... .......... .......... 66% 74.5M 3s
158100K .......... .......... .......... .......... .......... 66% 6.61M 3s
158150K .......... .......... .......... .......... .......... 66% 17.9M 3s
158200K .......... .......... .......... .......... .......... 66% 70.8M 3s
158250K .......... .......... .......... .......... .......... 66% 19.2M 3s
158300K .......... .......... .......... .......... .......... 66% 30.1M 3s
158350K .......... .......... .......... .......... .......... 66% 62.3M 3s
158400K .......... .......... .......... .......... .......... 66% 53.6M 3s
158450K .......... .......... .......... .......... .......... 66% 5.64M 3s
158500K .......... .......... .......... .......... .......... 66% 47.7M 3s
158550K .......... .......... .......... .......... .......... 66% 18.1M 3s
158600K .......... .......... .......... .......... .......... 66% 21.3M 3s
158650K .......... .......... .......... .......... .......... 67% 13.8M 3s
158700K .......... .......... .......... .......... .......... 67% 23.9M 3s
158750K .......... .......... .......... .......... .......... 67% 14.6M 3s
158800K .......... .......... .......... .......... .......... 67% 73.7M 3s
158850K .......... .......... .......... .......... .......... 67% 76.5M 3s
158900K .......... .......... .......... .......... .......... 67% 67.4M 3s
158950K .......... .......... .......... .......... .......... 67% 78.1M 3s
159000K .......... .......... .......... .......... .......... 67% 71.6M 3s
159050K .......... .......... .......... .......... .......... 67% 8.44M 3s
159100K .......... .......... .......... .......... .......... 67% 64.0M 3s
159150K .......... .......... .......... .......... .......... 67% 75.1M 3s
159200K .......... .......... .......... .......... .......... 67% 75.8M 3s
159250K .......... .......... .......... .......... .......... 67% 74.9M 3s
159300K .......... .......... .......... .......... .......... 67% 67.3M 3s
159350K .......... .......... .......... .......... .......... 67% 75.1M 3s
159400K .......... .......... .......... .......... .......... 67% 78.5M 3s
159450K .......... .......... .......... .......... .......... 67% 76.6M 3s
159500K .......... .......... .......... .......... .......... 67% 65.8M 3s
159550K .......... .......... .......... .......... .......... 67% 74.0M 3s
159600K .......... .......... .......... .......... .......... 67% 76.5M 2s
159650K .......... .......... .......... .......... .......... 67% 77.9M 2s
159700K .......... .......... .......... .......... .......... 67% 58.6M 2s
159750K .......... .......... .......... .......... .......... 67% 16.9M 2s
159800K .......... .......... .......... .......... .......... 67% 64.5M 2s
159850K .......... .......... .......... .......... .......... 67% 56.2M 2s
159900K .......... .......... .......... .......... .......... 67% 50.7M 2s
159950K .......... .......... .......... .......... .......... 67% 31.4M 2s
160000K .......... .......... .......... .......... .......... 67% 59.2M 2s
160050K .......... .......... .......... .......... .......... 67% 31.6M 2s
160100K .......... .......... .......... .......... .......... 67% 26.2M 2s
160150K .......... .......... .......... .......... .......... 67% 74.2M 2s
160200K .......... .......... .......... .......... .......... 67% 17.9M 2s
160250K .......... .......... .......... .......... .......... 67% 68.0M 2s
160300K .......... .......... .......... .......... .......... 67% 66.1M 2s
160350K .......... .......... .......... .......... .......... 67% 77.6M 2s
160400K .......... .......... .......... .......... .......... 67% 80.4M 2s
160450K .......... .......... .......... .......... .......... 67% 74.8M 2s
160500K .......... .......... .......... .......... .......... 67% 16.0M 2s
160550K .......... .......... .......... .......... .......... 67% 63.9M 2s
160600K .......... .......... .......... .......... .......... 67% 73.7M 2s
160650K .......... .......... .......... .......... .......... 67% 74.7M 2s
160700K .......... .......... .......... .......... .......... 67% 64.5M 2s
160750K .......... .......... .......... .......... .......... 67% 82.0M 2s
160800K .......... .......... .......... .......... .......... 67% 36.1M 2s
160850K .......... .......... .......... .......... .......... 67% 78.0M 2s
160900K .......... .......... .......... .......... .......... 67% 34.7M 2s
160950K .......... .......... .......... .......... .......... 67% 77.9M 2s
161000K .......... .......... .......... .......... .......... 68% 34.5M 2s
161050K .......... .......... .......... .......... .......... 68% 65.5M 2s
161100K .......... .......... .......... .......... .......... 68% 31.8M 2s
161150K .......... .......... .......... .......... .......... 68% 30.2M 2s
161200K .......... .......... .......... .......... .......... 68% 64.5M 2s
161250K .......... .......... .......... .......... .......... 68% 35.2M 2s
161300K .......... .......... .......... .......... .......... 68% 24.6M 2s
161350K .......... .......... .......... .......... .......... 68% 79.2M 2s
161400K .......... .......... .......... .......... .......... 68% 80.2M 2s
161450K .......... .......... .......... .......... .......... 68% 75.9M 2s
161500K .......... .......... .......... .......... .......... 68% 20.2M 2s
161550K .......... .......... .......... .......... .......... 68% 63.2M 2s
161600K .......... .......... .......... .......... .......... 68% 71.5M 2s
161650K .......... .......... .......... .......... .......... 68% 72.5M 2s
161700K .......... .......... .......... .......... .......... 68% 27.5M 2s
161750K .......... .......... .......... .......... .......... 68% 76.7M 2s
161800K .......... .......... .......... .......... .......... 68% 81.7M 2s
161850K .......... .......... .......... .......... .......... 68% 18.9M 2s
161900K .......... .......... .......... .......... .......... 68% 6.40M 2s
161950K .......... .......... .......... .......... .......... 68% 57.9M 2s
162000K .......... .......... .......... .......... .......... 68% 65.0M 2s
162050K .......... .......... .......... .......... .......... 68% 60.2M 2s
162100K .......... .......... .......... .......... .......... 68% 64.1M 2s
162150K .......... .......... .......... .......... .......... 68% 77.6M 2s
162200K .......... .......... .......... .......... .......... 68% 72.4M 2s
162250K .......... .......... .......... .......... .......... 68% 76.2M 2s
162300K .......... .......... .......... .......... .......... 68% 64.7M 2s
162350K .......... .......... .......... .......... .......... 68% 69.0M 2s
162400K .......... .......... .......... .......... .......... 68% 74.8M 2s
162450K .......... .......... .......... .......... .......... 68% 67.7M 2s
162500K .......... .......... .......... .......... .......... 68% 54.5M 2s
162550K .......... .......... .......... .......... .......... 68% 54.6M 2s
162600K .......... .......... .......... .......... .......... 68% 57.5M 2s
162650K .......... .......... .......... .......... .......... 68% 61.9M 2s
162700K .......... .......... .......... .......... .......... 68% 14.9M 2s
162750K .......... .......... .......... .......... .......... 68% 12.0M 2s
162800K .......... .......... .......... .......... .......... 68% 68.5M 2s
162850K .......... .......... .......... .......... .......... 68% 55.2M 2s
162900K .......... .......... .......... .......... .......... 68% 55.5M 2s
162950K .......... .......... .......... .......... .......... 68% 75.2M 2s
163000K .......... .......... .......... .......... .......... 68% 65.3M 2s
163050K .......... .......... .......... .......... .......... 68% 63.0M 2s
163100K .......... .......... .......... .......... .......... 68% 70.9M 2s
163150K .......... .......... .......... .......... .......... 68% 74.5M 2s
163200K .......... .......... .......... .......... .......... 68% 13.9M 2s
163250K .......... .......... .......... .......... .......... 68% 49.5M 2s
163300K .......... .......... .......... .......... .......... 68% 71.6M 2s
163350K .......... .......... .......... .......... .......... 68% 17.6M 2s
163400K .......... .......... .......... .......... .......... 69% 49.4M 2s
163450K .......... .......... .......... .......... .......... 69% 5.05M 2s
163500K .......... .......... .......... .......... .......... 69%  220M 2s
163550K .......... .......... .......... .......... .......... 69%  202M 2s
163600K .......... .......... .......... .......... .......... 69%  290M 2s
163650K .......... .......... .......... .......... .......... 69%  235M 2s
163700K .......... .......... .......... .......... .......... 69%  278M 2s
163750K .......... .......... .......... .......... .......... 69% 12.6M 2s
163800K .......... .......... .......... .......... .......... 69% 53.2M 2s
163850K .......... .......... .......... .......... .......... 69% 63.5M 2s
163900K .......... .......... .......... .......... .......... 69% 53.4M 2s
163950K .......... .......... .......... .......... .......... 69% 59.6M 2s
164000K .......... .......... .......... .......... .......... 69% 47.1M 2s
164050K .......... .......... .......... .......... .......... 69% 68.6M 2s
164100K .......... .......... .......... .......... .......... 69% 47.1M 2s
164150K .......... .......... .......... .......... .......... 69% 6.68M 2s
164200K .......... .......... .......... .......... .......... 69% 39.1M 2s
164250K .......... .......... .......... .......... .......... 69% 57.2M 2s
164300K .......... .......... .......... .......... .......... 69% 55.0M 2s
164350K .......... .......... .......... .......... .......... 69% 60.2M 2s
164400K .......... .......... .......... .......... .......... 69% 67.6M 2s
164450K .......... .......... .......... .......... .......... 69% 76.1M 2s
164500K .......... .......... .......... .......... .......... 69% 64.9M 2s
164550K .......... .......... .......... .......... .......... 69% 75.4M 2s
164600K .......... .......... .......... .......... .......... 69% 74.6M 2s
164650K .......... .......... .......... .......... .......... 69% 73.4M 2s
164700K .......... .......... .......... .......... .......... 69% 65.0M 2s
164750K .......... .......... .......... .......... .......... 69% 68.0M 2s
164800K .......... .......... .......... .......... .......... 69% 49.4M 2s
164850K .......... .......... .......... .......... .......... 69% 54.1M 2s
164900K .......... .......... .......... .......... .......... 69% 49.4M 2s
164950K .......... .......... .......... .......... .......... 69% 23.9M 2s
165000K .......... .......... .......... .......... .......... 69% 47.1M 2s
165050K .......... .......... .......... .......... .......... 69% 6.83M 2s
165100K .......... .......... .......... .......... .......... 69% 39.6M 2s
165150K .......... .......... .......... .......... .......... 69% 59.7M 2s
165200K .......... .......... .......... .......... .......... 69% 71.2M 2s
165250K .......... .......... .......... .......... .......... 69% 73.9M 2s
165300K .......... .......... .......... .......... .......... 69% 67.4M 2s
165350K .......... .......... .......... .......... .......... 69% 75.1M 2s
165400K .......... .......... .......... .......... .......... 69% 74.8M 2s
165450K .......... .......... .......... .......... .......... 69% 78.8M 2s
165500K .......... .......... .......... .......... .......... 69% 6.04M 2s
165550K .......... .......... .......... .......... .......... 69% 65.3M 2s
165600K .......... .......... .......... .......... .......... 69% 58.2M 2s
165650K .......... .......... .......... .......... .......... 69% 65.2M 2s
165700K .......... .......... .......... .......... .......... 69% 39.1M 2s
165750K .......... .......... .......... .......... .......... 70% 69.6M 2s
165800K .......... .......... .......... .......... .......... 70% 68.1M 2s
165850K .......... .......... .......... .......... .......... 70% 71.8M 2s
165900K .......... .......... .......... .......... .......... 70% 62.1M 2s
165950K .......... .......... .......... .......... .......... 70% 71.7M 2s
166000K .......... .......... .......... .......... .......... 70% 63.9M 2s
166050K .......... .......... .......... .......... .......... 70% 57.0M 2s
166100K .......... .......... .......... .......... .......... 70% 53.6M 2s
166150K .......... .......... .......... .......... .......... 70% 65.3M 2s
166200K .......... .......... .......... .......... .......... 70% 66.8M 2s
166250K .......... .......... .......... .......... .......... 70% 68.8M 2s
166300K .......... .......... .......... .......... .......... 70% 38.2M 2s
166350K .......... .......... .......... .......... .......... 70% 6.68M 2s
166400K .......... .......... .......... .......... .......... 70% 56.9M 2s
166450K .......... .......... .......... .......... .......... 70% 67.0M 2s
166500K .......... .......... .......... .......... .......... 70% 55.9M 2s
166550K .......... .......... .......... .......... .......... 70% 64.9M 2s
166600K .......... .......... .......... .......... .......... 70% 68.3M 2s
166650K .......... .......... .......... .......... .......... 70% 65.2M 2s
166700K .......... .......... .......... .......... .......... 70% 8.44M 2s
166750K .......... .......... .......... .......... .......... 70% 63.2M 2s
166800K .......... .......... .......... .......... .......... 70% 58.5M 2s
166850K .......... .......... .......... .......... .......... 70% 65.0M 2s
166900K .......... .......... .......... .......... .......... 70% 63.5M 2s
166950K .......... .......... .......... .......... .......... 70% 79.0M 2s
167000K .......... .......... .......... .......... .......... 70% 73.7M 2s
167050K .......... .......... .......... .......... .......... 70% 74.4M 2s
167100K .......... .......... .......... .......... .......... 70% 67.0M 2s
167150K .......... .......... .......... .......... .......... 70% 72.5M 2s
167200K .......... .......... .......... .......... .......... 70% 35.5M 2s
167250K .......... .......... .......... .......... .......... 70% 69.4M 2s
167300K .......... .......... .......... .......... .......... 70% 9.08M 2s
167350K .......... .......... .......... .......... .......... 70% 59.7M 2s
167400K .......... .......... .......... .......... .......... 70% 65.1M 2s
167450K .......... .......... .......... .......... .......... 70% 63.6M 2s
167500K .......... .......... .......... .......... .......... 70% 57.7M 2s
167550K .......... .......... .......... .......... .......... 70% 67.1M 2s
167600K .......... .......... .......... .......... .......... 70% 59.8M 2s
167650K .......... .......... .......... .......... .......... 70% 61.9M 2s
167700K .......... .......... .......... .......... .......... 70% 59.1M 2s
167750K .......... .......... .......... .......... .......... 70% 15.9M 2s
167800K .......... .......... .......... .......... .......... 70% 68.7M 2s
167850K .......... .......... .......... .......... .......... 70% 79.1M 2s
167900K .......... .......... .......... .......... .......... 70% 66.0M 2s
167950K .......... .......... .......... .......... .......... 70% 71.2M 2s
168000K .......... .......... .......... .......... .......... 70% 79.7M 2s
168050K .......... .......... .......... .......... .......... 70% 76.4M 2s
168100K .......... .......... .......... .......... .......... 71% 66.1M 2s
168150K .......... .......... .......... .......... .......... 71% 6.03M 2s
168200K .......... .......... .......... .......... .......... 71% 63.6M 2s
168250K .......... .......... .......... .......... .......... 71% 69.2M 2s
168300K .......... .......... .......... .......... .......... 71% 74.9M 2s
168350K .......... .......... .......... .......... .......... 71% 67.4M 2s
168400K .......... .......... .......... .......... .......... 71% 80.2M 2s
168450K .......... .......... .......... .......... .......... 71% 41.1M 2s
168500K .......... .......... .......... .......... .......... 71%  196M 2s
168550K .......... .......... .......... .......... .......... 71%  169M 2s
168600K .......... .......... .......... .......... .......... 71%  244M 2s
168650K .......... .......... .......... .......... .......... 71%  222M 2s
168700K .......... .......... .......... .......... .......... 71% 65.6M 2s
168750K .......... .......... .......... .......... .......... 71%  227M 2s
168800K .......... .......... .......... .......... .......... 71% 25.4M 2s
168850K .......... .......... .......... .......... .......... 71%  240M 2s
168900K .......... .......... .......... .......... .......... 71%  228M 2s
168950K .......... .......... .......... .......... .......... 71% 2.86M 2s
169000K .......... .......... .......... .......... .......... 71%  253M 2s
169050K .......... .......... .......... .......... .......... 71%  250M 2s
169100K .......... .......... .......... .......... .......... 71%  259M 2s
169150K .......... .......... .......... .......... .......... 71%  201M 2s
169200K .......... .......... .......... .......... .......... 71%  239M 2s
169250K .......... .......... .......... .......... .......... 71%  280M 2s
169300K .......... .......... .......... .......... .......... 71%  256M 2s
169350K .......... .......... .......... .......... .......... 71% 11.0M 2s
169400K .......... .......... .......... .......... .......... 71%  186M 2s
169450K .......... .......... .......... .......... .......... 71%  200M 2s
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169550K .......... .......... .......... .......... .......... 71%  230M 2s
169600K .......... .......... .......... .......... .......... 71%  191M 2s
169650K .......... .......... .......... .......... .......... 71% 16.1M 2s
169700K .......... .......... .......... .......... .......... 71%  210M 2s
169750K .......... .......... .......... .......... .......... 71%  260M 2s
169800K .......... .......... .......... .......... .......... 71% 16.3M 2s
169850K .......... .......... .......... .......... .......... 71% 13.6M 2s
169900K .......... .......... .......... .......... .......... 71%  271M 2s
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170000K .......... .......... .......... .......... .......... 71%  228M 2s
170050K .......... .......... .......... .......... .......... 71%  175M 2s
170100K .......... .......... .......... .......... .......... 71%  176M 2s
170150K .......... .......... .......... .......... .......... 71%  219M 2s
170200K .......... .......... .......... .......... .......... 71% 95.7M 2s
170250K .......... .......... .......... .......... .......... 71% 53.8M 2s
170300K .......... .......... .......... .......... .......... 71% 59.5M 2s
170350K .......... .......... .......... .......... .......... 71% 3.98M 2s
170400K .......... .......... .......... .......... .......... 71%  186M 2s
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170600K .......... .......... .......... .......... .......... 72%  243M 2s
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170800K .......... .......... .......... .......... .......... 72% 4.57M 2s
170850K .......... .......... .......... .......... .......... 72%  277M 2s
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171000K .......... .......... .......... .......... .......... 72%  264M 2s
171050K .......... .......... .......... .......... .......... 72% 11.2M 2s
171100K .......... .......... .......... .......... .......... 72%  223M 2s
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171200K .......... .......... .......... .......... .......... 72%  204M 2s
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171400K .......... .......... .......... .......... .......... 72% 11.1M 2s
171450K .......... .......... .......... .......... .......... 72%  259M 2s
171500K .......... .......... .......... .......... .......... 72%  208M 2s
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171600K .......... .......... .......... .......... .......... 72%  209M 2s
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171700K .......... .......... .......... .......... .......... 72%  243M 2s
171750K .......... .......... .......... .......... .......... 72% 87.4M 2s
171800K .......... .......... .......... .......... .......... 72% 54.5M 2s
171850K .......... .......... .......... .......... .......... 72% 5.92M 2s
171900K .......... .......... .......... .......... .......... 72%  102M 2s
171950K .......... .......... .......... .......... .......... 72% 71.2M 2s
172000K .......... .......... .......... .......... .......... 72% 59.7M 2s
172050K .......... .......... .......... .......... .......... 72%  116M 2s
172100K .......... .......... .......... .......... .......... 72% 86.3M 2s
172150K .......... .......... .......... .......... .......... 72% 75.1M 2s
172200K .......... .......... .......... .......... .......... 72% 78.3M 2s
172250K .......... .......... .......... .......... .......... 72% 33.6M 2s
172300K .......... .......... .......... .......... .......... 72% 60.6M 2s
172350K .......... .......... .......... .......... .......... 72% 12.5M 2s
172400K .......... .......... .......... .......... .......... 72%  215M 2s
172450K .......... .......... .......... .......... .......... 72% 42.0M 2s
172500K .......... .......... .......... .......... .......... 72%  139M 2s
172550K .......... .......... .......... .......... .......... 72%  100M 2s
172600K .......... .......... .......... .......... .......... 72% 61.3M 2s
172650K .......... .......... .......... .......... .......... 72% 64.0M 2s
172700K .......... .......... .......... .......... .......... 72% 60.4M 2s
172750K .......... .......... .......... .......... .......... 72% 53.2M 2s
172800K .......... .......... .......... .......... .......... 72% 48.8M 2s
172850K .......... .......... .......... .......... .......... 73% 56.7M 2s
172900K .......... .......... .......... .......... .......... 73% 63.5M 2s
172950K .......... .......... .......... .......... .......... 73% 74.8M 2s
173000K .......... .......... .......... .......... .......... 73% 3.78M 2s
173050K .......... .......... .......... .......... .......... 73% 16.7M 2s
173100K .......... .......... .......... .......... .......... 73% 82.5M 2s
173150K .......... .......... .......... .......... .......... 73% 89.5M 2s
173200K .......... .......... .......... .......... .......... 73% 65.0M 2s
173250K .......... .......... .......... .......... .......... 73% 69.3M 2s
173300K .......... .......... .......... .......... .......... 73% 66.7M 2s
173350K .......... .......... .......... .......... .......... 73%  100M 2s
173400K .......... .......... .......... .......... .......... 73% 71.2M 2s
173450K .......... .......... .......... .......... .......... 73% 10.4M 2s
173500K .......... .......... .......... .......... .......... 73%  280M 2s
173550K .......... .......... .......... .......... .......... 73%  253M 2s
173600K .......... .......... .......... .......... .......... 73%  187M 2s
173650K .......... .......... .......... .......... .......... 73%  186M 2s
173700K .......... .......... .......... .......... .......... 73%  307M 2s
173750K .......... .......... .......... .......... .......... 73%  219M 2s
173800K .......... .......... .......... .......... .......... 73%  237M 2s
173850K .......... .......... .......... .......... .......... 73% 53.4M 2s
173900K .......... .......... .......... .......... .......... 73% 21.3M 2s
173950K .......... .......... .......... .......... .......... 73% 50.6M 2s
174000K .......... .......... .......... .......... .......... 73% 59.8M 2s
174050K .......... .......... .......... .......... .......... 73% 55.2M 2s
174100K .......... .......... .......... .......... .......... 73% 60.0M 2s
174150K .......... .......... .......... .......... .......... 73% 64.5M 2s
174200K .......... .......... .......... .......... .......... 73% 53.3M 2s
174250K .......... .......... .......... .......... .......... 73% 60.4M 2s
174300K .......... .......... .......... .......... .......... 73% 76.8M 2s
174350K .......... .......... .......... .......... .......... 73% 6.90M 2s
174400K .......... .......... .......... .......... .......... 73% 7.62M 2s
174450K .......... .......... .......... .......... .......... 73%  207M 2s
174500K .......... .......... .......... .......... .......... 73% 86.0M 2s
174550K .......... .......... .......... .......... .......... 73%  155M 2s
174600K .......... .......... .......... .......... .......... 73%  202M 2s
174650K .......... .......... .......... .......... .......... 73%  303M 2s
174700K .......... .......... .......... .......... .......... 73%  195M 2s
174750K .......... .......... .......... .......... .......... 73%  168M 2s
174800K .......... .......... .......... .......... .......... 73%  174M 2s
174850K .......... .......... .......... .......... .......... 73% 8.40M 2s
174900K .......... .......... .......... .......... .......... 73% 42.2M 2s
174950K .......... .......... .......... .......... .......... 73% 56.6M 2s
175000K .......... .......... .......... .......... .......... 73% 53.3M 2s
175050K .......... .......... .......... .......... .......... 73% 59.3M 2s
175100K .......... .......... .......... .......... .......... 73% 65.1M 2s
175150K .......... .......... .......... .......... .......... 73% 62.8M 2s
175200K .......... .......... .......... .......... .......... 74% 66.7M 2s
175250K .......... .......... .......... .......... .......... 74% 58.7M 2s
175300K .......... .......... .......... .......... .......... 74% 54.7M 2s
175350K .......... .......... .......... .......... .......... 74% 30.7M 2s
175400K .......... .......... .......... .......... .......... 74% 67.1M 2s
175450K .......... .......... .......... .......... .......... 74% 9.13M 2s
175500K .......... .......... .......... .......... .......... 74%  263M 2s
175550K .......... .......... .......... .......... .......... 74%  169M 2s
175600K .......... .......... .......... .......... .......... 74%  252M 2s
175650K .......... .......... .......... .......... .......... 74%  217M 2s
175700K .......... .......... .......... .......... .......... 74%  254M 2s
175750K .......... .......... .......... .......... .......... 74%  231M 2s
175800K .......... .......... .......... .......... .......... 74%  261M 2s
175850K .......... .......... .......... .......... .......... 74%  220M 2s
175900K .......... .......... .......... .......... .......... 74% 11.9M 2s
175950K .......... .......... .......... .......... .......... 74% 10.5M 2s
176000K .......... .......... .......... .......... .......... 74% 46.2M 2s
176050K .......... .......... .......... .......... .......... 74%  317M 2s
176100K .......... .......... .......... .......... .......... 74%  318M 2s
176150K .......... .......... .......... .......... .......... 74%  250M 2s
176200K .......... .......... .......... .......... .......... 74%  292M 2s
176250K .......... .......... .......... .......... .......... 74%  225M 2s
176300K .......... .......... .......... .......... .......... 74%  316M 2s
176350K .......... .......... .......... .......... .......... 74% 12.2M 2s
176400K .......... .......... .......... .......... .......... 74%  115M 2s
176450K .......... .......... .......... .......... .......... 74%  268M 2s
176500K .......... .......... .......... .......... .......... 74% 56.7M 2s
176550K .......... .......... .......... .......... .......... 74% 63.0M 2s
176600K .......... .......... .......... .......... .......... 74% 61.1M 2s
176650K .......... .......... .......... .......... .......... 74% 66.8M 2s
176700K .......... .......... .......... .......... .......... 74% 60.0M 2s
176750K .......... .......... .......... .......... .......... 74% 40.4M 2s
176800K .......... .......... .......... .......... .......... 74%  112M 2s
176850K .......... .......... .......... .......... .......... 74% 55.9M 2s
176900K .......... .......... .......... .......... .......... 74% 2.89M 2s
176950K .......... .......... .......... .......... .......... 74%  300M 2s
177000K .......... .......... .......... .......... .......... 74%  319M 2s
177050K .......... .......... .......... .......... .......... 74%  241M 2s
177100K .......... .......... .......... .......... .......... 74%  182M 2s
177150K .......... .......... .......... .......... .......... 74% 70.5M 2s
177200K .......... .......... .......... .......... .......... 74% 62.7M 2s
177250K .......... .......... .......... .......... .......... 74% 45.7M 2s
177300K .......... .......... .......... .......... .......... 74% 62.8M 2s
177350K .......... .......... .......... .......... .......... 74% 53.3M 2s
177400K .......... .......... .......... .......... .......... 74% 58.9M 2s
177450K .......... .......... .......... .......... .......... 74% 20.3M 2s
177500K .......... .......... .......... .......... .......... 74% 24.2M 2s
177550K .......... .......... .......... .......... .......... 74% 48.9M 2s
177600K .......... .......... .......... .......... .......... 75% 7.78M 2s
177650K .......... .......... .......... .......... .......... 75% 80.7M 2s
177700K .......... .......... .......... .......... .......... 75% 76.4M 2s
177750K .......... .......... .......... .......... .......... 75% 62.3M 2s
177800K .......... .......... .......... .......... .......... 75% 64.1M 2s
177850K .......... .......... .......... .......... .......... 75% 62.1M 2s
177900K .......... .......... .......... .......... .......... 75% 70.0M 2s
177950K .......... .......... .......... .......... .......... 75% 48.5M 2s
178000K .......... .......... .......... .......... .......... 75% 4.71M 2s
178050K .......... .......... .......... .......... .......... 75% 70.4M 2s
178100K .......... .......... .......... .......... .......... 75% 25.2M 2s
178150K .......... .......... .......... .......... .......... 75% 51.2M 2s
178200K .......... .......... .......... .......... .......... 75% 6.59M 2s
178250K .......... .......... .......... .......... .......... 75% 72.8M 2s
178300K .......... .......... .......... .......... .......... 75% 91.4M 2s
178350K .......... .......... .......... .......... .......... 75% 78.3M 2s
178400K .......... .......... .......... .......... .......... 75% 77.2M 2s
178450K .......... .......... .......... .......... .......... 75% 55.2M 2s
178500K .......... .......... .......... .......... .......... 75% 50.9M 2s
178550K .......... .......... .......... .......... .......... 75% 51.4M 2s
178600K .......... .......... .......... .......... .......... 75% 58.9M 2s
178650K .......... .......... .......... .......... .......... 75% 7.52M 2s
178700K .......... .......... .......... .......... .......... 75% 59.1M 2s
178750K .......... .......... .......... .......... .......... 75% 50.9M 2s
178800K .......... .......... .......... .......... .......... 75% 60.8M 2s
178850K .......... .......... .......... .......... .......... 75% 57.6M 2s
178900K .......... .......... .......... .......... .......... 75% 56.1M 2s
178950K .......... .......... .......... .......... .......... 75% 58.2M 2s
179000K .......... .......... .......... .......... .......... 75% 3.81M 2s
179050K .......... .......... .......... .......... .......... 75% 49.5M 2s
179100K .......... .......... .......... .......... .......... 75% 56.4M 2s
179150K .......... .......... .......... .......... .......... 75% 52.9M 2s
179200K .......... .......... .......... .......... .......... 75% 5.93M 2s
179250K .......... .......... .......... .......... .......... 75%  237M 2s
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179750K .......... .......... .......... .......... .......... 75%  226M 2s
179800K .......... .......... .......... .......... .......... 75%  265M 2s
179850K .......... .......... .......... .......... .......... 75% 76.7M 2s
179900K .......... .......... .......... .......... .......... 75%  285M 2s
179950K .......... .......... .......... .......... .......... 76%  228M 2s
180000K .......... .......... .......... .......... .......... 76%  268M 2s
180050K .......... .......... .......... .......... .......... 76%  257M 2s
180100K .......... .......... .......... .......... .......... 76%  239M 2s
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180200K .......... .......... .......... .......... .......... 76%  223M 2s
180250K .......... .......... .......... .......... .......... 76%  249M 2s
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180850K .......... .......... .......... .......... .......... 76% 13.9M 2s
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180950K .......... .......... .......... .......... .......... 76% 5.72M 2s
181000K .......... .......... .......... .......... .......... 76%  241M 2s
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181450K .......... .......... .......... .......... .......... 76% 7.67M 2s
181500K .......... .......... .......... .......... .......... 76%  302M 2s
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181600K .......... .......... .......... .......... .......... 76% 6.57M 2s
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181700K .......... .......... .......... .......... .......... 76% 3.09M 2s
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182000K .......... .......... .......... .......... .......... 76%  263M 2s
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182100K .......... .......... .......... .......... .......... 76% 66.4M 2s
182150K .......... .......... .......... .......... .......... 76%  258M 2s
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182250K .......... .......... .......... .......... .......... 76%  236M 2s
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182400K .......... .......... .......... .......... .......... 77% 49.3M 2s
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182500K .......... .......... .......... .......... .......... 77% 7.35M 2s
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182750K .......... .......... .......... .......... .......... 77% 7.86M 2s
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183000K .......... .......... .......... .......... .......... 77%  146M 2s
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183100K .......... .......... .......... .......... .......... 77% 3.77M 2s
183150K .......... .......... .......... .......... .......... 77%  123M 2s
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183250K .......... .......... .......... .......... .......... 77% 94.5M 2s
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183400K .......... .......... .......... .......... .......... 77% 98.8M 2s
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183900K .......... .......... .......... .......... .......... 77% 66.6M 2s
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184000K .......... .......... .......... .......... .......... 77%  114M 2s
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184100K .......... .......... .......... .......... .......... 77%  160M 2s
184150K .......... .......... .......... .......... .......... 77% 91.5M 2s
184200K .......... .......... .......... .......... .......... 77% 84.4M 2s
184250K .......... .......... .......... .......... .......... 77%  118M 2s
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185400K .......... .......... .......... .......... .......... 78% 7.32M 2s
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185800K .......... .......... .......... .......... .......... 78% 2.53M 2s
185850K .......... .......... .......... .......... .......... 78% 93.1M 2s
185900K .......... .......... .......... .......... .......... 78% 62.0M 2s
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186000K .......... .......... .......... .......... .......... 78% 72.0M 2s
186050K .......... .......... .......... .......... .......... 78% 90.2M 2s
186100K .......... .......... .......... .......... .......... 78% 93.4M 2s
186150K .......... .......... .......... .......... .......... 78% 50.5M 2s
186200K .......... .......... .......... .......... .......... 78% 53.8M 2s
186250K .......... .......... .......... .......... .......... 78% 57.3M 2s
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186350K .......... .......... .......... .......... .......... 78% 46.6M 2s
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186450K .......... .......... .......... .......... .......... 78% 63.4M 2s
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186900K .......... .......... .......... .......... .......... 78%  210M 2s
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187000K .......... .......... .......... .......... .......... 78%  169M 2s
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187100K .......... .......... .......... .......... .......... 79%  244M 2s
187150K .......... .......... .......... .......... .......... 79% 5.04M 2s
187200K .......... .......... .......... .......... .......... 79% 53.0M 2s
187250K .......... .......... .......... .......... .......... 79% 49.1M 2s
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187350K .......... .......... .......... .......... .......... 79% 67.5M 2s
187400K .......... .......... .......... .......... .......... 79% 56.1M 2s
187450K .......... .......... .......... .......... .......... 79% 59.3M 2s
187500K .......... .......... .......... .......... .......... 79% 79.9M 2s
187550K .......... .......... .......... .......... .......... 79% 43.2M 2s
187600K .......... .......... .......... .......... .......... 79%  188M 2s
187650K .......... .......... .......... .......... .......... 79% 10.1M 2s
187700K .......... .......... .......... .......... .......... 79% 5.16M 2s
187750K .......... .......... .......... .......... .......... 79%  123M 2s
187800K .......... .......... .......... .......... .......... 79% 60.2M 2s
187850K .......... .......... .......... .......... .......... 79% 63.1M 2s
187900K .......... .......... .......... .......... .......... 79% 78.3M 2s
187950K .......... .......... .......... .......... .......... 79% 46.9M 2s
188000K .......... .......... .......... .......... .......... 79% 47.9M 2s
188050K .......... .......... .......... .......... .......... 79%  253M 2s
188100K .......... .......... .......... .......... .......... 79% 52.3M 2s
188150K .......... .......... .......... .......... .......... 79% 57.1M 2s
188200K .......... .......... .......... .......... .......... 79% 64.0M 2s
188250K .......... .......... .......... .......... .......... 79%  124M 2s
188300K .......... .......... .......... .......... .......... 79% 7.01M 2s
188350K .......... .......... .......... .......... .......... 79% 48.1M 2s
188400K .......... .......... .......... .......... .......... 79% 57.5M 2s
188450K .......... .......... .......... .......... .......... 79% 51.5M 2s
188500K .......... .......... .......... .......... .......... 79% 46.4M 2s
188550K .......... .......... .......... .......... .......... 79% 45.6M 2s
188600K .......... .......... .......... .......... .......... 79% 62.3M 2s
188650K .......... .......... .......... .......... .......... 79% 42.3M 2s
188700K .......... .......... .......... .......... .......... 79% 57.5M 2s
188750K .......... .......... .......... .......... .......... 79% 60.0M 2s
188800K .......... .......... .......... .......... .......... 79% 69.6M 2s
188850K .......... .......... .......... .......... .......... 79% 65.3M 2s
188900K .......... .......... .......... .......... .......... 79% 4.43M 2s
188950K .......... .......... .......... .......... .......... 79% 31.4M 2s
189000K .......... .......... .......... .......... .......... 79% 72.0M 2s
189050K .......... .......... .......... .......... .......... 79% 63.0M 2s
189100K .......... .......... .......... .......... .......... 79% 77.7M 2s
189150K .......... .......... .......... .......... .......... 79% 76.1M 1s
189200K .......... .......... .......... .......... .......... 79% 58.3M 1s
189250K .......... .......... .......... .......... .......... 79% 55.3M 1s
189300K .......... .......... .......... .......... .......... 79% 21.7M 1s
189350K .......... .......... .......... .......... .......... 79% 73.3M 1s
189400K .......... .......... .......... .......... .......... 79% 9.62M 1s
189450K .......... .......... .......... .......... .......... 80% 12.3M 1s
189500K .......... .......... .......... .......... .......... 80% 48.3M 1s
189550K .......... .......... .......... .......... .......... 80% 7.06M 1s
189600K .......... .......... .......... .......... .......... 80% 69.1M 1s
189650K .......... .......... .......... .......... .......... 80% 66.3M 1s
189700K .......... .......... .......... .......... .......... 80% 10.3M 1s
189750K .......... .......... .......... .......... .......... 80% 15.7M 1s
189800K .......... .......... .......... .......... .......... 80% 71.2M 1s
189850K .......... .......... .......... .......... .......... 80% 12.6M 1s
189900K .......... .......... .......... .......... .......... 80% 10.7M 1s
189950K .......... .......... .......... .......... .......... 80% 56.8M 1s
190000K .......... .......... .......... .......... .......... 80% 17.1M 1s
190050K .......... .......... .......... .......... .......... 80% 9.52M 1s
190100K .......... .......... .......... .......... .......... 80% 70.7M 1s
190150K .......... .......... .......... .......... .......... 80% 15.7M 1s
190200K .......... .......... .......... .......... .......... 80% 67.1M 1s
190250K .......... .......... .......... .......... .......... 80% 52.6M 1s
190300K .......... .......... .......... .......... .......... 80% 75.4M 1s
190350K .......... .......... .......... .......... .......... 80% 70.8M 1s
190400K .......... .......... .......... .......... .......... 80% 76.3M 1s
190450K .......... .......... .......... .......... .......... 80% 69.5M 1s
190500K .......... .......... .......... .......... .......... 80% 66.6M 1s
190550K .......... .......... .......... .......... .......... 80% 55.5M 1s
190600K .......... .......... .......... .......... .......... 80% 71.1M 1s
190650K .......... .......... .......... .......... .......... 80% 54.7M 1s
190700K .......... .......... .......... .......... .......... 80% 58.8M 1s
190750K .......... .......... .......... .......... .......... 80% 57.9M 1s
190800K .......... .......... .......... .......... .......... 80% 53.8M 1s
190850K .......... .......... .......... .......... .......... 80% 53.2M 1s
190900K .......... .......... .......... .......... .......... 80% 54.5M 1s
190950K .......... .......... .......... .......... .......... 80% 57.7M 1s
191000K .......... .......... .......... .......... .......... 80% 75.3M 1s
191050K .......... .......... .......... .......... .......... 80% 61.8M 1s
191100K .......... .......... .......... .......... .......... 80% 57.4M 1s
191150K .......... .......... .......... .......... .......... 80% 59.7M 1s
191200K .......... .......... .......... .......... .......... 80% 81.1M 1s
191250K .......... .......... .......... .......... .......... 80% 67.5M 1s
191300K .......... .......... .......... .......... .......... 80% 81.3M 1s
191350K .......... .......... .......... .......... .......... 80% 78.5M 1s
191400K .......... .......... .......... .......... .......... 80% 73.7M 1s
191450K .......... .......... .......... .......... .......... 80% 47.8M 1s
191500K .......... .......... .......... .......... .......... 80% 70.8M 1s
191550K .......... .......... .......... .......... .......... 80% 61.1M 1s
191600K .......... .......... .......... .......... .......... 80% 70.0M 1s
191650K .......... .......... .......... .......... .......... 80% 67.2M 1s
191700K .......... .......... .......... .......... .......... 80% 63.6M 1s
191750K .......... .......... .......... .......... .......... 80% 72.5M 1s
191800K .......... .......... .......... .......... .......... 81% 61.6M 1s
191850K .......... .......... .......... .......... .......... 81% 53.9M 1s
191900K .......... .......... .......... .......... .......... 81% 66.3M 1s
191950K .......... .......... .......... .......... .......... 81% 69.7M 1s
192000K .......... .......... .......... .......... .......... 81% 60.3M 1s
192050K .......... .......... .......... .......... .......... 81% 63.1M 1s
192100K .......... .......... .......... .......... .......... 81% 72.6M 1s
192150K .......... .......... .......... .......... .......... 81% 74.5M 1s
192200K .......... .......... .......... .......... .......... 81% 55.1M 1s
192250K .......... .......... .......... .......... .......... 81% 54.3M 1s
192300K .......... .......... .......... .......... .......... 81% 75.2M 1s
192350K .......... .......... .......... .......... .......... 81% 72.6M 1s
192400K .......... .......... .......... .......... .......... 81% 79.5M 1s
192450K .......... .......... .......... .......... .......... 81% 68.5M 1s
192500K .......... .......... .......... .......... .......... 81% 75.0M 1s
192550K .......... .......... .......... .......... .......... 81% 79.0M 1s
192600K .......... .......... .......... .......... .......... 81% 74.8M 1s
192650K .......... .......... .......... .......... .......... 81% 72.1M 1s
192700K .......... .......... .......... .......... .......... 81% 72.4M 1s
192750K .......... .......... .......... .......... .......... 81% 74.9M 1s
192800K .......... .......... .......... .......... .......... 81% 79.0M 1s
192850K .......... .......... .......... .......... .......... 81% 65.0M 1s
192900K .......... .......... .......... .......... .......... 81% 77.7M 1s
192950K .......... .......... .......... .......... .......... 81% 30.4M 1s
193000K .......... .......... .......... .......... .......... 81% 12.5M 1s
193050K .......... .......... .......... .......... .......... 81% 65.8M 1s
193100K .......... .......... .......... .......... .......... 81% 46.5M 1s
193150K .......... .......... .......... .......... .......... 81% 32.0M 1s
193200K .......... .......... .......... .......... .......... 81% 37.7M 1s
193250K .......... .......... .......... .......... .......... 81% 60.4M 1s
193300K .......... .......... .......... .......... .......... 81% 54.1M 1s
193350K .......... .......... .......... .......... .......... 81% 76.8M 1s
193400K .......... .......... .......... .......... .......... 81% 66.2M 1s
193450K .......... .......... .......... .......... .......... 81% 18.8M 1s
193500K .......... .......... .......... .......... .......... 81% 51.2M 1s
193550K .......... .......... .......... .......... .......... 81% 74.0M 1s
193600K .......... .......... .......... .......... .......... 81% 73.3M 1s
193650K .......... .......... .......... .......... .......... 81% 60.8M 1s
193700K .......... .......... .......... .......... .......... 81% 3.92M 1s
193750K .......... .......... .......... .......... .......... 81% 47.9M 1s
193800K .......... .......... .......... .......... .......... 81% 62.2M 1s
193850K .......... .......... .......... .......... .......... 81% 75.3M 1s
193900K .......... .......... .......... .......... .......... 81% 64.5M 1s
193950K .......... .......... .......... .......... .......... 81% 61.2M 1s
194000K .......... .......... .......... .......... .......... 81% 4.88M 1s
194050K .......... .......... .......... .......... .......... 81% 48.3M 1s
194100K .......... .......... .......... .......... .......... 81% 7.27M 1s
194150K .......... .......... .......... .......... .......... 82% 62.9M 1s
194200K .......... .......... .......... .......... .......... 82% 75.9M 1s
194250K .......... .......... .......... .......... .......... 82% 55.5M 1s
194300K .......... .......... .......... .......... .......... 82% 15.0M 1s
194350K .......... .......... .......... .......... .......... 82% 11.6M 1s
194400K .......... .......... .......... .......... .......... 82% 18.4M 1s
194450K .......... .......... .......... .......... .......... 82% 8.60M 1s
194500K .......... .......... .......... .......... .......... 82% 24.3M 1s
194550K .......... .......... .......... .......... .......... 82% 54.8M 1s
194600K .......... .......... .......... .......... .......... 82% 8.50M 1s
194650K .......... .......... .......... .......... .......... 82% 36.2M 1s
194700K .......... .......... .......... .......... .......... 82% 44.6M 1s
194750K .......... .......... .......... .......... .......... 82% 56.3M 1s
194800K .......... .......... .......... .......... .......... 82% 55.1M 1s
194850K .......... .......... .......... .......... .......... 82% 47.3M 1s
194900K .......... .......... .......... .......... .......... 82% 52.2M 1s
194950K .......... .......... .......... .......... .......... 82% 60.8M 1s
195000K .......... .......... .......... .......... .......... 82% 6.71M 1s
195050K .......... .......... .......... .......... .......... 82% 60.0M 1s
195100K .......... .......... .......... .......... .......... 82% 69.8M 1s
195150K .......... .......... .......... .......... .......... 82% 74.0M 1s
195200K .......... .......... .......... .......... .......... 82% 77.1M 1s
195250K .......... .......... .......... .......... .......... 82% 67.5M 1s
195300K .......... .......... .......... .......... .......... 82% 65.8M 1s
195350K .......... .......... .......... .......... .......... 82% 69.5M 1s
195400K .......... .......... .......... .......... .......... 82% 46.5M 1s
195450K .......... .......... .......... .......... .......... 82% 44.8M 1s
195500K .......... .......... .......... .......... .......... 82% 49.7M 1s
195550K .......... .......... .......... .......... .......... 82% 4.54M 1s
195600K .......... .......... .......... .......... .......... 82% 42.7M 1s
195650K .......... .......... .......... .......... .......... 82% 58.2M 1s
195700K .......... .......... .......... .......... .......... 82% 77.4M 1s
195750K .......... .......... .......... .......... .......... 82% 42.5M 1s
195800K .......... .......... .......... .......... .......... 82% 6.39M 1s
195850K .......... .......... .......... .......... .......... 82% 58.0M 1s
195900K .......... .......... .......... .......... .......... 82% 71.1M 1s
195950K .......... .......... .......... .......... .......... 82% 73.4M 1s
196000K .......... .......... .......... .......... .......... 82% 72.1M 1s
196050K .......... .......... .......... .......... .......... 82% 55.3M 1s
196100K .......... .......... .......... .......... .......... 82% 63.6M 1s
196150K .......... .......... .......... .......... .......... 82% 77.8M 1s
196200K .......... .......... .......... .......... .......... 82% 61.0M 1s
196250K .......... .......... .......... .......... .......... 82% 62.3M 1s
196300K .......... .......... .......... .......... .......... 82% 80.5M 1s
196350K .......... .......... .......... .......... .......... 82% 74.8M 1s
196400K .......... .......... .......... .......... .......... 82% 38.3M 1s
196450K .......... .......... .......... .......... .......... 82% 57.6M 1s
196500K .......... .......... .......... .......... .......... 82% 62.8M 1s
196550K .......... .......... .......... .......... .......... 83% 74.1M 1s
196600K .......... .......... .......... .......... .......... 83% 78.1M 1s
196650K .......... .......... .......... .......... .......... 83% 25.3M 1s
196700K .......... .......... .......... .......... .......... 83% 70.4M 1s
196750K .......... .......... .......... .......... .......... 83% 74.1M 1s
196800K .......... .......... .......... .......... .......... 83% 55.3M 1s
196850K .......... .......... .......... .......... .......... 83% 65.6M 1s
196900K .......... .......... .......... .......... .......... 83% 71.0M 1s
196950K .......... .......... .......... .......... .......... 83% 74.0M 1s
197000K .......... .......... .......... .......... .......... 83% 60.8M 1s
197050K .......... .......... .......... .......... .......... 83% 46.6M 1s
197100K .......... .......... .......... .......... .......... 83% 70.2M 1s
197150K .......... .......... .......... .......... .......... 83% 6.50M 1s
197200K .......... .......... .......... .......... .......... 83% 67.3M 1s
197250K .......... .......... .......... .......... .......... 83% 61.3M 1s
197300K .......... .......... .......... .......... .......... 83% 74.1M 1s
197350K .......... .......... .......... .......... .......... 83% 75.8M 1s
197400K .......... .......... .......... .......... .......... 83% 73.8M 1s
197450K .......... .......... .......... .......... .......... 83% 65.9M 1s
197500K .......... .......... .......... .......... .......... 83% 70.3M 1s
197550K .......... .......... .......... .......... .......... 83% 68.4M 1s
197600K .......... .......... .......... .......... .......... 83% 70.9M 1s
197650K .......... .......... .......... .......... .......... 83% 65.8M 1s
197700K .......... .......... .......... .......... .......... 83% 72.1M 1s
197750K .......... .......... .......... .......... .......... 83% 47.9M 1s
197800K .......... .......... .......... .......... .......... 83% 48.1M 1s
197850K .......... .......... .......... .......... .......... 83% 51.0M 1s
197900K .......... .......... .......... .......... .......... 83% 59.7M 1s
197950K .......... .......... .......... .......... .......... 83% 59.4M 1s
198000K .......... .......... .......... .......... .......... 83% 69.7M 1s
198050K .......... .......... .......... .......... .......... 83% 67.6M 1s
198100K .......... .......... .......... .......... .......... 83% 76.4M 1s
198150K .......... .......... .......... .......... .......... 83% 70.8M 1s
198200K .......... .......... .......... .......... .......... 83% 75.0M 1s
198250K .......... .......... .......... .......... .......... 83% 64.1M 1s
198300K .......... .......... .......... .......... .......... 83% 54.4M 1s
198350K .......... .......... .......... .......... .......... 83% 65.6M 1s
198400K .......... .......... .......... .......... .......... 83% 69.4M 1s
198450K .......... .......... .......... .......... .......... 83% 27.3M 1s
198500K .......... .......... .......... .......... .......... 83% 66.7M 1s
198550K .......... .......... .......... .......... .......... 83% 68.9M 1s
198600K .......... .......... .......... .......... .......... 83% 76.7M 1s
198650K .......... .......... .......... .......... .......... 83% 66.2M 1s
198700K .......... .......... .......... .......... .......... 83% 76.4M 1s
198750K .......... .......... .......... .......... .......... 83% 31.5M 1s
198800K .......... .......... .......... .......... .......... 83% 21.0M 1s
198850K .......... .......... .......... .......... .......... 83%  245M 1s
198900K .......... .......... .......... .......... .......... 84% 6.19M 1s
198950K .......... .......... .......... .......... .......... 84%  268M 1s
199000K .......... .......... .......... .......... .......... 84%  240M 1s
199050K .......... .......... .......... .......... .......... 84%  217M 1s
199100K .......... .......... .......... .......... .......... 84%  221M 1s
199150K .......... .......... .......... .......... .......... 84%  243M 1s
199200K .......... .......... .......... .......... .......... 84%  260M 1s
199250K .......... .......... .......... .......... .......... 84%  216M 1s
199300K .......... .......... .......... .......... .......... 84% 14.3M 1s
199350K .......... .......... .......... .......... .......... 84% 8.45M 1s
199400K .......... .......... .......... .......... .......... 84%  158M 1s
199450K .......... .......... .......... .......... .......... 84%  224M 1s
199500K .......... .......... .......... .......... .......... 84%  239M 1s
199550K .......... .......... .......... .......... .......... 84% 87.5M 1s
199600K .......... .......... .......... .......... .......... 84%  129M 1s
199650K .......... .......... .......... .......... .......... 84% 45.6M 1s
199700K .......... .......... .......... .......... .......... 84% 47.5M 1s
199750K .......... .......... .......... .......... .......... 84% 65.1M 1s
199800K .......... .......... .......... .......... .......... 84% 6.58M 1s
199850K .......... .......... .......... .......... .......... 84% 51.5M 1s
199900K .......... .......... .......... .......... .......... 84% 42.2M 1s
199950K .......... .......... .......... .......... .......... 84% 63.9M 1s
200000K .......... .......... .......... .......... .......... 84% 48.4M 1s
200050K .......... .......... .......... .......... .......... 84% 82.5M 1s
200100K .......... .......... .......... .......... .......... 84% 7.47M 1s
200150K .......... .......... .......... .......... .......... 84% 11.4M 1s
200200K .......... .......... .......... .......... .......... 84%  101M 1s
200250K .......... .......... .......... .......... .......... 84% 82.6M 1s
200300K .......... .......... .......... .......... .......... 84% 78.7M 1s
200350K .......... .......... .......... .......... .......... 84% 47.4M 1s
200400K .......... .......... .......... .......... .......... 84% 79.4M 1s
200450K .......... .......... .......... .......... .......... 84% 63.9M 1s
200500K .......... .......... .......... .......... .......... 84% 40.0M 1s
200550K .......... .......... .......... .......... .......... 84% 56.0M 1s
200600K .......... .......... .......... .......... .......... 84%  152M 1s
200650K .......... .......... .......... .......... .......... 84% 85.1M 1s
200700K .......... .......... .......... .......... .......... 84% 78.8M 1s
200750K .......... .......... .......... .......... .......... 84% 11.4M 1s
200800K .......... .......... .......... .......... .......... 84% 56.7M 1s
200850K .......... .......... .......... .......... .......... 84%  123M 1s
200900K .......... .......... .......... .......... .......... 84% 66.5M 1s
200950K .......... .......... .......... .......... .......... 84%  109M 1s
201000K .......... .......... .......... .......... .......... 84% 61.5M 1s
201050K .......... .......... .......... .......... .......... 84% 50.5M 1s
201100K .......... .......... .......... .......... .......... 84%  180M 1s
201150K .......... .......... .......... .......... .......... 84%  109M 1s
201200K .......... .......... .......... .......... .......... 84% 67.0M 1s
201250K .......... .......... .......... .......... .......... 85% 69.4M 1s
201300K .......... .......... .......... .......... .......... 85% 10.0M 1s
201350K .......... .......... .......... .......... .......... 85% 78.0M 1s
201400K .......... .......... .......... .......... .......... 85% 57.7M 1s
201450K .......... .......... .......... .......... .......... 85% 6.11M 1s
201500K .......... .......... .......... .......... .......... 85% 75.8M 1s
201550K .......... .......... .......... .......... .......... 85% 71.4M 1s
201600K .......... .......... .......... .......... .......... 85% 79.6M 1s
201650K .......... .......... .......... .......... .......... 85%  117M 1s
201700K .......... .......... .......... .......... .......... 85% 82.9M 1s
201750K .......... .......... .......... .......... .......... 85% 53.3M 1s
201800K .......... .......... .......... .......... .......... 85%  105M 1s
201850K .......... .......... .......... .......... .......... 85% 63.5M 1s
201900K .......... .......... .......... .......... .......... 85% 57.6M 1s
201950K .......... .......... .......... .......... .......... 85%  104M 1s
202000K .......... .......... .......... .......... .......... 85% 25.6M 1s
202050K .......... .......... .......... .......... .......... 85% 99.3M 1s
202100K .......... .......... .......... .......... .......... 85% 56.8M 1s
202150K .......... .......... .......... .......... .......... 85% 79.3M 1s
202200K .......... .......... .......... .......... .......... 85%  104M 1s
202250K .......... .......... .......... .......... .......... 85%  126M 1s
202300K .......... .......... .......... .......... .......... 85% 95.9M 1s
202350K .......... .......... .......... .......... .......... 85% 8.30M 1s
202400K .......... .......... .......... .......... .......... 85%  172M 1s
202450K .......... .......... .......... .......... .......... 85%  153M 1s
202500K .......... .......... .......... .......... .......... 85%  109M 1s
202550K .......... .......... .......... .......... .......... 85%  206M 1s
202600K .......... .......... .......... .......... .......... 85%  176M 1s
202650K .......... .......... .......... .......... .......... 85% 97.8M 1s
202700K .......... .......... .......... .......... .......... 85%  191M 1s
202750K .......... .......... .......... .......... .......... 85%  225M 1s
202800K .......... .......... .......... .......... .......... 85%  159M 1s
202850K .......... .......... .......... .......... .......... 85%  129M 1s
202900K .......... .......... .......... .......... .......... 85% 94.3M 1s
202950K .......... .......... .......... .......... .......... 85% 65.4M 1s
203000K .......... .......... .......... .......... .......... 85% 7.58M 1s
203050K .......... .......... .......... .......... .......... 85% 16.7M 1s
203100K .......... .......... .......... .......... .......... 85% 81.7M 1s
203150K .......... .......... .......... .......... .......... 85%  120M 1s
203200K .......... .......... .......... .......... .......... 85% 88.9M 1s
203250K .......... .......... .......... .......... .......... 85% 68.6M 1s
203300K .......... .......... .......... .......... .......... 85% 50.8M 1s
203350K .......... .......... .......... .......... .......... 85%  191M 1s
203400K .......... .......... .......... .......... .......... 85% 7.57M 1s
203450K .......... .......... .......... .......... .......... 85%  168M 1s
203500K .......... .......... .......... .......... .......... 85%  158M 1s
203550K .......... .......... .......... .......... .......... 85%  232M 1s
203600K .......... .......... .......... .......... .......... 85%  155M 1s
203650K .......... .......... .......... .......... .......... 86% 55.8M 1s
203700K .......... .......... .......... .......... .......... 86%  170M 1s
203750K .......... .......... .......... .......... .......... 86% 7.10M 1s
203800K .......... .......... .......... .......... .......... 86% 70.5M 1s
203850K .......... .......... .......... .......... .......... 86% 73.5M 1s
203900K .......... .......... .......... .......... .......... 86% 60.7M 1s
203950K .......... .......... .......... .......... .......... 86% 60.1M 1s
204000K .......... .......... .......... .......... .......... 86% 11.2M 1s
204050K .......... .......... .......... .......... .......... 86%  284M 1s
204100K .......... .......... .......... .......... .......... 86%  279M 1s
204150K .......... .......... .......... .......... .......... 86%  288M 1s
204200K .......... .......... .......... .......... .......... 86% 5.51M 1s
204250K .......... .......... .......... .......... .......... 86%  205M 1s
204300K .......... .......... .......... .......... .......... 86%  118M 1s
204350K .......... .......... .......... .......... .......... 86% 59.4M 1s
204400K .......... .......... .......... .......... .......... 86%  126M 1s
204450K .......... .......... .......... .......... .......... 86% 13.8M 1s
204500K .......... .......... .......... .......... .......... 86%  124M 1s
204550K .......... .......... .......... .......... .......... 86%  235M 1s
204600K .......... .......... .......... .......... .......... 86%  130M 1s
204650K .......... .......... .......... .......... .......... 86%  183M 1s
204700K .......... .......... .......... .......... .......... 86%  238M 1s
204750K .......... .......... .......... .......... .......... 86% 14.7M 1s
204800K .......... .......... .......... .......... .......... 86% 67.5M 1s
204850K .......... .......... .......... .......... .......... 86% 65.3M 1s
204900K .......... .......... .......... .......... .......... 86% 46.2M 1s
204950K .......... .......... .......... .......... .......... 86% 62.0M 1s
205000K .......... .......... .......... .......... .......... 86%  155M 1s
205050K .......... .......... .......... .......... .......... 86% 64.0M 1s
205100K .......... .......... .......... .......... .......... 86% 40.7M 1s
205150K .......... .......... .......... .......... .......... 86% 81.2M 1s
205200K .......... .......... .......... .......... .......... 86% 85.9M 1s
205250K .......... .......... .......... .......... .......... 86% 89.2M 1s
205300K .......... .......... .......... .......... .......... 86% 78.2M 1s
205350K .......... .......... .......... .......... .......... 86% 11.2M 1s
205400K .......... .......... .......... .......... .......... 86% 64.8M 1s
205450K .......... .......... .......... .......... .......... 86% 74.9M 1s
205500K .......... .......... .......... .......... .......... 86% 6.23M 1s
205550K .......... .......... .......... .......... .......... 86% 70.4M 1s
205600K .......... .......... .......... .......... .......... 86% 83.5M 1s
205650K .......... .......... .......... .......... .......... 86% 51.3M 1s
205700K .......... .......... .......... .......... .......... 86% 85.8M 1s
205750K .......... .......... .......... .......... .......... 86% 58.3M 1s
205800K .......... .......... .......... .......... .......... 86% 59.3M 1s
205850K .......... .......... .......... .......... .......... 86% 66.8M 1s
205900K .......... .......... .......... .......... .......... 86% 56.4M 1s
205950K .......... .......... .......... .......... .......... 86% 63.3M 1s
206000K .......... .......... .......... .......... .......... 87% 73.7M 1s
206050K .......... .......... .......... .......... .......... 87% 71.7M 1s
206100K .......... .......... .......... .......... .......... 87% 51.2M 1s
206150K .......... .......... .......... .......... .......... 87% 70.6M 1s
206200K .......... .......... .......... .......... .......... 87% 64.2M 1s
206250K .......... .......... .......... .......... .......... 87% 57.7M 1s
206300K .......... .......... .......... .......... .......... 87% 68.9M 1s
206350K .......... .......... .......... .......... .......... 87% 60.1M 1s
206400K .......... .......... .......... .......... .......... 87% 12.3M 1s
206450K .......... .......... .......... .......... .......... 87% 65.1M 1s
206500K .......... .......... .......... .......... .......... 87% 48.3M 1s
206550K .......... .......... .......... .......... .......... 87% 60.5M 1s
206600K .......... .......... .......... .......... .......... 87% 71.9M 1s
206650K .......... .......... .......... .......... .......... 87% 60.8M 1s
206700K .......... .......... .......... .......... .......... 87% 62.8M 1s
206750K .......... .......... .......... .......... .......... 87% 63.1M 1s
206800K .......... .......... .......... .......... .......... 87% 65.9M 1s
206850K .......... .......... .......... .......... .......... 87% 58.8M 1s
206900K .......... .......... .......... .......... .......... 87% 41.3M 1s
206950K .......... .......... .......... .......... .......... 87% 75.8M 1s
207000K .......... .......... .......... .......... .......... 87% 23.0M 1s
207050K .......... .......... .......... .......... .......... 87% 66.5M 1s
207100K .......... .......... .......... .......... .......... 87% 29.6M 1s
207150K .......... .......... .......... .......... .......... 87% 32.3M 1s
207200K .......... .......... .......... .......... .......... 87% 48.9M 1s
207250K .......... .......... .......... .......... .......... 87% 66.7M 1s
207300K .......... .......... .......... .......... .......... 87% 50.5M 1s
207350K .......... .......... .......... .......... .......... 87% 63.2M 1s
207400K .......... .......... .......... .......... .......... 87% 71.1M 1s
207450K .......... .......... .......... .......... .......... 87% 68.4M 1s
207500K .......... .......... .......... .......... .......... 87% 10.8M 1s
207550K .......... .......... .......... .......... .......... 87% 50.1M 1s
207600K .......... .......... .......... .......... .......... 87% 55.2M 1s
207650K .......... .......... .......... .......... .......... 87% 66.0M 1s
207700K .......... .......... .......... .......... .......... 87% 8.07M 1s
207750K .......... .......... .......... .......... .......... 87% 62.9M 1s
207800K .......... .......... .......... .......... .......... 87% 55.8M 1s
207850K .......... .......... .......... .......... .......... 87% 61.1M 1s
207900K .......... .......... .......... .......... .......... 87% 70.6M 1s
207950K .......... .......... .......... .......... .......... 87% 66.2M 1s
208000K .......... .......... .......... .......... .......... 87% 61.1M 1s
208050K .......... .......... .......... .......... .......... 87% 4.05M 1s
208100K .......... .......... .......... .......... .......... 87% 20.2M 1s
208150K .......... .......... .......... .......... .......... 87% 57.6M 1s
208200K .......... .......... .......... .......... .......... 87% 68.4M 1s
208250K .......... .......... .......... .......... .......... 87% 77.8M 1s
208300K .......... .......... .......... .......... .......... 87% 73.9M 1s
208350K .......... .......... .......... .......... .......... 88% 58.8M 1s
208400K .......... .......... .......... .......... .......... 88% 68.5M 1s
208450K .......... .......... .......... .......... .......... 88% 71.2M 1s
208500K .......... .......... .......... .......... .......... 88% 69.1M 1s
208550K .......... .......... .......... .......... .......... 88% 36.1M 1s
208600K .......... .......... .......... .......... .......... 88% 71.4M 1s
208650K .......... .......... .......... .......... .......... 88% 67.8M 1s
208700K .......... .......... .......... .......... .......... 88% 70.8M 1s
208750K .......... .......... .......... .......... .......... 88% 62.7M 1s
208800K .......... .......... .......... .......... .......... 88% 70.6M 1s
208850K .......... .......... .......... .......... .......... 88% 68.5M 1s
208900K .......... .......... .......... .......... .......... 88% 75.2M 1s
208950K .......... .......... .......... .......... .......... 88% 62.3M 1s
209000K .......... .......... .......... .......... .......... 88% 63.0M 1s
209050K .......... .......... .......... .......... .......... 88% 36.7M 1s
209100K .......... .......... .......... .......... .......... 88% 71.9M 1s
209150K .......... .......... .......... .......... .......... 88% 61.7M 1s
209200K .......... .......... .......... .......... .......... 88% 67.4M 1s
209250K .......... .......... .......... .......... .......... 88% 73.4M 1s
209300K .......... .......... .......... .......... .......... 88% 76.3M 1s
209350K .......... .......... .......... .......... .......... 88% 74.8M 1s
209400K .......... .......... .......... .......... .......... 88% 17.5M 1s
209450K .......... .......... .......... .......... .......... 88% 61.5M 1s
209500K .......... .......... .......... .......... .......... 88% 58.1M 1s
209550K .......... .......... .......... .......... .......... 88% 9.98M 1s
209600K .......... .......... .......... .......... .......... 88% 30.4M 1s
209650K .......... .......... .......... .......... .......... 88% 33.9M 1s
209700K .......... .......... .......... .......... .......... 88% 67.3M 1s
209750K .......... .......... .......... .......... .......... 88% 67.4M 1s
209800K .......... .......... .......... .......... .......... 88% 69.3M 1s
209850K .......... .......... .......... .......... .......... 88% 55.3M 1s
209900K .......... .......... .......... .......... .......... 88% 63.7M 1s
209950K .......... .......... .......... .......... .......... 88% 51.9M 1s
210000K .......... .......... .......... .......... .......... 88% 61.9M 1s
210050K .......... .......... .......... .......... .......... 88% 37.8M 1s
210100K .......... .......... .......... .......... .......... 88% 65.3M 1s
210150K .......... .......... .......... .......... .......... 88% 66.4M 1s
210200K .......... .......... .......... .......... .......... 88% 45.4M 1s
210250K .......... .......... .......... .......... .......... 88% 68.3M 1s
210300K .......... .......... .......... .......... .......... 88% 63.6M 1s
210350K .......... .......... .......... .......... .......... 88% 12.1M 1s
210400K .......... .......... .......... .......... .......... 88% 47.2M 1s
210450K .......... .......... .......... .......... .......... 88% 66.2M 1s
210500K .......... .......... .......... .......... .......... 88% 68.0M 1s
210550K .......... .......... .......... .......... .......... 88% 71.6M 1s
210600K .......... .......... .......... .......... .......... 88% 36.8M 1s
210650K .......... .......... .......... .......... .......... 88% 71.3M 1s
210700K .......... .......... .......... .......... .......... 88% 65.5M 1s
210750K .......... .......... .......... .......... .......... 89% 74.3M 1s
210800K .......... .......... .......... .......... .......... 89% 57.4M 1s
210850K .......... .......... .......... .......... .......... 89% 67.5M 1s
210900K .......... .......... .......... .......... .......... 89% 79.6M 1s
210950K .......... .......... .......... .......... .......... 89% 22.2M 1s
211000K .......... .......... .......... .......... .......... 89% 73.1M 1s
211050K .......... .......... .......... .......... .......... 89% 75.3M 1s
211100K .......... .......... .......... .......... .......... 89% 41.3M 1s
211150K .......... .......... .......... .......... .......... 89% 69.4M 1s
211200K .......... .......... .......... .......... .......... 89% 32.6M 1s
211250K .......... .......... .......... .......... .......... 89% 20.1M 1s
211300K .......... .......... .......... .......... .......... 89% 69.2M 1s
211350K .......... .......... .......... .......... .......... 89% 64.3M 1s
211400K .......... .......... .......... .......... .......... 89% 75.4M 1s
211450K .......... .......... .......... .......... .......... 89% 78.1M 1s
211500K .......... .......... .......... .......... .......... 89% 74.5M 1s
211550K .......... .......... .......... .......... .......... 89% 18.9M 1s
211600K .......... .......... .......... .......... .......... 89% 33.4M 1s
211650K .......... .......... .......... .......... .......... 89% 6.62M 1s
211700K .......... .......... .......... .......... .......... 89% 67.4M 1s
211750K .......... .......... .......... .......... .......... 89% 41.2M 1s
211800K .......... .......... .......... .......... .......... 89% 64.4M 1s
211850K .......... .......... .......... .......... .......... 89% 64.4M 1s
211900K .......... .......... .......... .......... .......... 89% 65.3M 1s
211950K .......... .......... .......... .......... .......... 89% 58.0M 1s
212000K .......... .......... .......... .......... .......... 89% 19.9M 1s
212050K .......... .......... .......... .......... .......... 89% 66.7M 1s
212100K .......... .......... .......... .......... .......... 89% 80.2M 1s
212150K .......... .......... .......... .......... .......... 89% 6.49M 1s
212200K .......... .......... .......... .......... .......... 89% 13.1M 1s
212250K .......... .......... .......... .......... .......... 89% 4.70M 1s
212300K .......... .......... .......... .......... .......... 89% 46.7M 1s
212350K .......... .......... .......... .......... .......... 89% 59.4M 1s
212400K .......... .......... .......... .......... .......... 89%  205M 1s
212450K .......... .......... .......... .......... .......... 89%  245M 1s
212500K .......... .......... .......... .......... .......... 89%  182M 1s
212550K .......... .......... .......... .......... .......... 89%  215M 1s
212600K .......... .......... .......... .......... .......... 89%  258M 1s
212650K .......... .......... .......... .......... .......... 89%  214M 1s
212700K .......... .......... .......... .......... .......... 89%  195M 1s
212750K .......... .......... .......... .......... .......... 89%  264M 1s
212800K .......... .......... .......... .......... .......... 89%  255M 1s
212850K .......... .......... .......... .......... .......... 89% 63.0M 1s
212900K .......... .......... .......... .......... .......... 89%  224M 1s
212950K .......... .......... .......... .......... .......... 89%  256M 1s
213000K .......... .......... .......... .......... .......... 89%  327M 1s
213050K .......... .......... .......... .......... .......... 89%  255M 1s
213100K .......... .......... .......... .......... .......... 90%  203M 1s
213150K .......... .......... .......... .......... .......... 90%  308M 1s
213200K .......... .......... .......... .......... .......... 90% 52.9M 1s
213250K .......... .......... .......... .......... .......... 90%  275M 1s
213300K .......... .......... .......... .......... .......... 90% 17.5M 1s
213350K .......... .......... .......... .......... .......... 90% 72.7M 1s
213400K .......... .......... .......... .......... .......... 90% 60.1M 1s
213450K .......... .......... .......... .......... .......... 90% 72.4M 1s
213500K .......... .......... .......... .......... .......... 90% 39.8M 1s
213550K .......... .......... .......... .......... .......... 90% 10.3M 1s
213600K .......... .......... .......... .......... .......... 90% 56.5M 1s
213650K .......... .......... .......... .......... .......... 90% 66.5M 1s
213700K .......... .......... .......... .......... .......... 90% 65.7M 1s
213750K .......... .......... .......... .......... .......... 90% 32.8M 1s
213800K .......... .......... .......... .......... .......... 90% 62.2M 1s
213850K .......... .......... .......... .......... .......... 90% 60.8M 1s
213900K .......... .......... .......... .......... .......... 90% 56.1M 1s
213950K .......... .......... .......... .......... .......... 90% 45.6M 1s
214000K .......... .......... .......... .......... .......... 90% 54.8M 1s
214050K .......... .......... .......... .......... .......... 90% 45.7M 1s
214100K .......... .......... .......... .......... .......... 90% 63.2M 1s
214150K .......... .......... .......... .......... .......... 90% 5.52M 1s
214200K .......... .......... .......... .......... .......... 90% 4.65M 1s
214250K .......... .......... .......... .......... .......... 90% 9.27M 1s
214300K .......... .......... .......... .......... .......... 90% 67.1M 1s
214350K .......... .......... .......... .......... .......... 90% 64.2M 1s
214400K .......... .......... .......... .......... .......... 90% 71.2M 1s
214450K .......... .......... .......... .......... .......... 90% 71.1M 1s
214500K .......... .......... .......... .......... .......... 90% 64.9M 1s
214550K .......... .......... .......... .......... .......... 90% 54.6M 1s
214600K .......... .......... .......... .......... .......... 90% 72.9M 1s
214650K .......... .......... .......... .......... .......... 90% 75.9M 1s
214700K .......... .......... .......... .......... .......... 90% 72.5M 1s
214750K .......... .......... .......... .......... .......... 90% 60.9M 1s
214800K .......... .......... .......... .......... .......... 90% 71.3M 1s
214850K .......... .......... .......... .......... .......... 90% 11.2M 1s
214900K .......... .......... .......... .......... .......... 90% 76.2M 1s
214950K .......... .......... .......... .......... .......... 90% 66.6M 1s
215000K .......... .......... .......... .......... .......... 90% 74.7M 1s
215050K .......... .......... .......... .......... .......... 90% 76.2M 1s
215100K .......... .......... .......... .......... .......... 90% 71.5M 1s
215150K .......... .......... .......... .......... .......... 90% 64.6M 1s
215200K .......... .......... .......... .......... .......... 90% 65.4M 1s
215250K .......... .......... .......... .......... .......... 90% 66.4M 1s
215300K .......... .......... .......... .......... .......... 90% 61.9M 1s
215350K .......... .......... .......... .......... .......... 90% 63.8M 1s
215400K .......... .......... .......... .......... .......... 90% 62.9M 1s
215450K .......... .......... .......... .......... .......... 91% 70.9M 1s
215500K .......... .......... .......... .......... .......... 91% 67.2M 1s
215550K .......... .......... .......... .......... .......... 91% 66.9M 1s
215600K .......... .......... .......... .......... .......... 91% 82.7M 1s
215650K .......... .......... .......... .......... .......... 91% 72.4M 1s
215700K .......... .......... .......... .......... .......... 91% 73.0M 1s
215750K .......... .......... .......... .......... .......... 91% 66.4M 1s
215800K .......... .......... .......... .......... .......... 91% 77.8M 1s
215850K .......... .......... .......... .......... .......... 91% 74.6M 1s
215900K .......... .......... .......... .......... .......... 91% 75.7M 1s
215950K .......... .......... .......... .......... .......... 91% 61.4M 1s
216000K .......... .......... .......... .......... .......... 91% 73.1M 1s
216050K .......... .......... .......... .......... .......... 91% 75.2M 1s
216100K .......... .......... .......... .......... .......... 91% 75.2M 1s
216150K .......... .......... .......... .......... .......... 91% 22.5M 1s
216200K .......... .......... .......... .......... .......... 91% 36.8M 1s
216250K .......... .......... .......... .......... .......... 91% 31.2M 1s
216300K .......... .......... .......... .......... .......... 91% 4.51M 1s
216350K .......... .......... .......... .......... .......... 91% 58.3M 1s
216400K .......... .......... .......... .......... .......... 91% 35.8M 1s
216450K .......... .......... .......... .......... .......... 91% 52.2M 1s
216500K .......... .......... .......... .......... .......... 91% 62.9M 1s
216550K .......... .......... .......... .......... .......... 91% 74.4M 1s
216600K .......... .......... .......... .......... .......... 91% 70.4M 1s
216650K .......... .......... .......... .......... .......... 91% 67.0M 1s
216700K .......... .......... .......... .......... .......... 91% 63.1M 1s
216750K .......... .......... .......... .......... .......... 91% 61.2M 1s
216800K .......... .......... .......... .......... .......... 91% 69.7M 1s
216850K .......... .......... .......... .......... .......... 91% 66.3M 1s
216900K .......... .......... .......... .......... .......... 91% 67.0M 1s
216950K .......... .......... .......... .......... .......... 91% 57.5M 1s
217000K .......... .......... .......... .......... .......... 91% 69.2M 1s
217050K .......... .......... .......... .......... .......... 91% 67.6M 1s
217100K .......... .......... .......... .......... .......... 91% 62.2M 1s
217150K .......... .......... .......... .......... .......... 91% 57.3M 1s
217200K .......... .......... .......... .......... .......... 91% 63.1M 1s
217250K .......... .......... .......... .......... .......... 91% 75.4M 1s
217300K .......... .......... .......... .......... .......... 91% 67.7M 1s
217350K .......... .......... .......... .......... .......... 91% 63.4M 1s
217400K .......... .......... .......... .......... .......... 91% 60.8M 1s
217450K .......... .......... .......... .......... .......... 91% 68.8M 1s
217500K .......... .......... .......... .......... .......... 91% 69.7M 1s
217550K .......... .......... .......... .......... .......... 91% 62.2M 1s
217600K .......... .......... .......... .......... .......... 91% 63.7M 1s
217650K .......... .......... .......... .......... .......... 91% 69.5M 1s
217700K .......... .......... .......... .......... .......... 91% 53.1M 1s
217750K .......... .......... .......... .......... .......... 91% 52.3M 1s
217800K .......... .......... .......... .......... .......... 91% 66.6M 1s
217850K .......... .......... .......... .......... .......... 92% 73.7M 1s
217900K .......... .......... .......... .......... .......... 92% 71.5M 1s
217950K .......... .......... .......... .......... .......... 92% 65.4M 1s
218000K .......... .......... .......... .......... .......... 92% 69.7M 1s
218050K .......... .......... .......... .......... .......... 92% 70.5M 1s
218100K .......... .......... .......... .......... .......... 92% 40.8M 1s
218150K .......... .......... .......... .......... .......... 92% 63.5M 1s
218200K .......... .......... .......... .......... .......... 92% 58.4M 1s
218250K .......... .......... .......... .......... .......... 92% 2.65M 1s
218300K .......... .......... .......... .......... .......... 92%  185M 1s
218350K .......... .......... .......... .......... .......... 92%  276M 1s
218400K .......... .......... .......... .......... .......... 92% 12.0M 1s
218450K .......... .......... .......... .......... .......... 92% 65.8M 1s
218500K .......... .......... .......... .......... .......... 92% 45.3M 1s
218550K .......... .......... .......... .......... .......... 92%  187M 1s
218600K .......... .......... .......... .......... .......... 92% 51.8M 1s
218650K .......... .......... .......... .......... .......... 92% 68.1M 1s
218700K .......... .......... .......... .......... .......... 92% 53.8M 1s
218750K .......... .......... .......... .......... .......... 92% 26.6M 1s
218800K .......... .......... .......... .......... .......... 92% 80.5M 1s
218850K .......... .......... .......... .......... .......... 92% 64.2M 1s
218900K .......... .......... .......... .......... .......... 92% 51.9M 1s
218950K .......... .......... .......... .......... .......... 92%  103M 1s
219000K .......... .......... .......... .......... .......... 92%  110M 1s
219050K .......... .......... .......... .......... .......... 92% 21.3M 1s
219100K .......... .......... .......... .......... .......... 92% 12.2M 1s
219150K .......... .......... .......... .......... .......... 92% 88.8M 1s
219200K .......... .......... .......... .......... .......... 92% 52.6M 1s
219250K .......... .......... .......... .......... .......... 92% 79.5M 1s
219300K .......... .......... .......... .......... .......... 92% 59.9M 1s
219350K .......... .......... .......... .......... .......... 92% 54.7M 1s
219400K .......... .......... .......... .......... .......... 92% 84.0M 1s
219450K .......... .......... .......... .......... .......... 92% 85.2M 1s
219500K .......... .......... .......... .......... .......... 92%  111M 1s
219550K .......... .......... .......... .......... .......... 92% 86.1M 1s
219600K .......... .......... .......... .......... .......... 92% 50.0M 1s
219650K .......... .......... .......... .......... .......... 92% 47.3M 1s
219700K .......... .......... .......... .......... .......... 92%  183M 1s
219750K .......... .......... .......... .......... .......... 92% 7.10M 1s
219800K .......... .......... .......... .......... .......... 92% 83.7M 1s
219850K .......... .......... .......... .......... .......... 92% 84.6M 1s
219900K .......... .......... .......... .......... .......... 92% 95.8M 1s
219950K .......... .......... .......... .......... .......... 92% 87.3M 1s
220000K .......... .......... .......... .......... .......... 92% 93.4M 1s
220050K .......... .......... .......... .......... .......... 92% 83.6M 1s
220100K .......... .......... .......... .......... .......... 92% 85.6M 1s
220150K .......... .......... .......... .......... .......... 92% 39.4M 1s
220200K .......... .......... .......... .......... .......... 93%  103M 1s
220250K .......... .......... .......... .......... .......... 93% 98.8M 1s
220300K .......... .......... .......... .......... .......... 93% 41.4M 1s
220350K .......... .......... .......... .......... .......... 93% 6.80M 1s
220400K .......... .......... .......... .......... .......... 93% 17.1M 1s
220450K .......... .......... .......... .......... .......... 93% 47.4M 1s
220500K .......... .......... .......... .......... .......... 93%  221M 1s
220550K .......... .......... .......... .......... .......... 93% 78.0M 0s
220600K .......... .......... .......... .......... .......... 93% 34.8M 0s
220650K .......... .......... .......... .......... .......... 93% 77.4M 0s
220700K .......... .......... .......... .......... .......... 93% 63.2M 0s
220750K .......... .......... .......... .......... .......... 93% 35.0M 0s
220800K .......... .......... .......... .......... .......... 93% 90.2M 0s
220850K .......... .......... .......... .......... .......... 93%  109M 0s
220900K .......... .......... .......... .......... .......... 93% 87.6M 0s
220950K .......... .......... .......... .......... .......... 93% 5.95M 0s
221000K .......... .......... .......... .......... .......... 93% 52.4M 0s
221050K .......... .......... .......... .......... .......... 93% 55.6M 0s
221100K .......... .......... .......... .......... .......... 93% 10.3M 0s
221150K .......... .......... .......... .......... .......... 93% 58.9M 0s
221200K .......... .......... .......... .......... .......... 93% 74.7M 0s
221250K .......... .......... .......... .......... .......... 93% 31.4M 0s
221300K .......... .......... .......... .......... .......... 93% 15.2M 0s
221350K .......... .......... .......... .......... .......... 93% 74.3M 0s
221400K .......... .......... .......... .......... .......... 93% 74.7M 0s
221450K .......... .......... .......... .......... .......... 93% 11.9M 0s
221500K .......... .......... .......... .......... .......... 93% 5.59M 0s
221550K .......... .......... .......... .......... .......... 93% 16.4M 0s
221600K .......... .......... .......... .......... .......... 93% 71.4M 0s
221650K .......... .......... .......... .......... .......... 93% 66.0M 0s
221700K .......... .......... .......... .......... .......... 93% 76.6M 0s
221750K .......... .......... .......... .......... .......... 93% 8.76M 0s
221800K .......... .......... .......... .......... .......... 93% 65.5M 0s
221850K .......... .......... .......... .......... .......... 93% 61.8M 0s
221900K .......... .......... .......... .......... .......... 93% 66.7M 0s
221950K .......... .......... .......... .......... .......... 93% 74.4M 0s
222000K .......... .......... .......... .......... .......... 93% 36.8M 0s
222050K .......... .......... .......... .......... .......... 93% 26.7M 0s
222100K .......... .......... .......... .......... .......... 93% 17.4M 0s
222150K .......... .......... .......... .......... .......... 93% 60.1M 0s
222200K .......... .......... .......... .......... .......... 93% 22.4M 0s
222250K .......... .......... .......... .......... .......... 93% 30.8M 0s
222300K .......... .......... .......... .......... .......... 93% 34.7M 0s
222350K .......... .......... .......... .......... .......... 93% 18.1M 0s
222400K .......... .......... .......... .......... .......... 93% 61.5M 0s
222450K .......... .......... .......... .......... .......... 93% 59.5M 0s
222500K .......... .......... .......... .......... .......... 93% 65.9M 0s
222550K .......... .......... .......... .......... .......... 93% 23.0M 0s
222600K .......... .......... .......... .......... .......... 94% 74.4M 0s
222650K .......... .......... .......... .......... .......... 94% 65.6M 0s
222700K .......... .......... .......... .......... .......... 94% 70.5M 0s
222750K .......... .......... .......... .......... .......... 94% 25.4M 0s
222800K .......... .......... .......... .......... .......... 94% 77.7M 0s
222850K .......... .......... .......... .......... .......... 94% 62.5M 0s
222900K .......... .......... .......... .......... .......... 94% 21.1M 0s
222950K .......... .......... .......... .......... .......... 94% 67.1M 0s
223000K .......... .......... .......... .......... .......... 94% 65.3M 0s
223050K .......... .......... .......... .......... .......... 94% 58.2M 0s
223100K .......... .......... .......... .......... .......... 94% 10.5M 0s
223150K .......... .......... .......... .......... .......... 94% 69.4M 0s
223200K .......... .......... .......... .......... .......... 94% 77.4M 0s
223250K .......... .......... .......... .......... .......... 94% 64.7M 0s
223300K .......... .......... .......... .......... .......... 94% 74.7M 0s
223350K .......... .......... .......... .......... .......... 94% 73.4M 0s
223400K .......... .......... .......... .......... .......... 94% 75.7M 0s
223450K .......... .......... .......... .......... .......... 94% 67.6M 0s
223500K .......... .......... .......... .......... .......... 94% 74.6M 0s
223550K .......... .......... .......... .......... .......... 94% 74.6M 0s
223600K .......... .......... .......... .......... .......... 94% 78.4M 0s
223650K .......... .......... .......... .......... .......... 94% 69.4M 0s
223700K .......... .......... .......... .......... .......... 94% 73.5M 0s
223750K .......... .......... .......... .......... .......... 94% 77.8M 0s
223800K .......... .......... .......... .......... .......... 94% 76.0M 0s
223850K .......... .......... .......... .......... .......... 94% 63.0M 0s
223900K .......... .......... .......... .......... .......... 94% 62.4M 0s
223950K .......... .......... .......... .......... .......... 94% 70.3M 0s
224000K .......... .......... .......... .......... .......... 94% 67.3M 0s
224050K .......... .......... .......... .......... .......... 94% 10.7M 0s
224100K .......... .......... .......... .......... .......... 94% 7.00M 0s
224150K .......... .......... .......... .......... .......... 94%  204M 0s
224200K .......... .......... .......... .......... .......... 94%  329M 0s
224250K .......... .......... .......... .......... .......... 94%  189M 0s
224300K .......... .......... .......... .......... .......... 94%  268M 0s
224350K .......... .......... .......... .......... .......... 94% 96.3M 0s
224400K .......... .......... .......... .......... .......... 94%  325M 0s
224450K .......... .......... .......... .......... .......... 94%  217M 0s
224500K .......... .......... .......... .......... .......... 94%  296M 0s
224550K .......... .......... .......... .......... .......... 94%  305M 0s
224600K .......... .......... .......... .......... .......... 94% 69.5M 0s
224650K .......... .......... .......... .......... .......... 94%  226M 0s
224700K .......... .......... .......... .......... .......... 94%  256M 0s
224750K .......... .......... .......... .......... .......... 94%  277M 0s
224800K .......... .......... .......... .......... .......... 94%  298M 0s
224850K .......... .......... .......... .......... .......... 94%  272M 0s
224900K .......... .......... .......... .......... .......... 94%  302M 0s
224950K .......... .......... .......... .......... .......... 95%  317M 0s
225000K .......... .......... .......... .......... .......... 95%  262M 0s
225050K .......... .......... .......... .......... .......... 95%  263M 0s
225100K .......... .......... .......... .......... .......... 95%  306M 0s
225150K .......... .......... .......... .......... .......... 95%  297M 0s
225200K .......... .......... .......... .......... .......... 95% 19.8M 0s
225250K .......... .......... .......... .......... .......... 95% 46.6M 0s
225300K .......... .......... .......... .......... .......... 95% 44.8M 0s
225350K .......... .......... .......... .......... .......... 95% 54.0M 0s
225400K .......... .......... .......... .......... .......... 95% 18.8M 0s
225450K .......... .......... .......... .......... .......... 95% 8.51M 0s
225500K .......... .......... .......... .......... .......... 95% 61.1M 0s
225550K .......... .......... .......... .......... .......... 95% 62.4M 0s
225600K .......... .......... .......... .......... .......... 95% 58.5M 0s
225650K .......... .......... .......... .......... .......... 95% 6.55M 0s
225700K .......... .......... .......... .......... .......... 95% 61.4M 0s
225750K .......... .......... .......... .......... .......... 95% 67.3M 0s
225800K .......... .......... .......... .......... .......... 95% 9.97M 0s
225850K .......... .......... .......... .......... .......... 95% 30.2M 0s
225900K .......... .......... .......... .......... .......... 95% 29.1M 0s
225950K .......... .......... .......... .......... .......... 95% 63.1M 0s
226000K .......... .......... .......... .......... .......... 95% 64.4M 0s
226050K .......... .......... .......... .......... .......... 95% 56.9M 0s
226100K .......... .......... .......... .......... .......... 95% 60.4M 0s
226150K .......... .......... .......... .......... .......... 95% 70.5M 0s
226200K .......... .......... .......... .......... .......... 95% 70.8M 0s
226250K .......... .......... .......... .......... .......... 95% 67.8M 0s
226300K .......... .......... .......... .......... .......... 95% 12.1M 0s
226350K .......... .......... .......... .......... .......... 95% 59.2M 0s
226400K .......... .......... .......... .......... .......... 95% 26.2M 0s
226450K .......... .......... .......... .......... .......... 95% 21.0M 0s
226500K .......... .......... .......... .......... .......... 95% 16.7M 0s
226550K .......... .......... .......... .......... .......... 95% 64.6M 0s
226600K .......... .......... .......... .......... .......... 95% 68.8M 0s
226650K .......... .......... .......... .......... .......... 95% 55.0M 0s
226700K .......... .......... .......... .......... .......... 95% 57.8M 0s
226750K .......... .......... .......... .......... .......... 95% 64.3M 0s
226800K .......... .......... .......... .......... .......... 95% 67.1M 0s
226850K .......... .......... .......... .......... .......... 95% 7.75M 0s
226900K .......... .......... .......... .......... .......... 95%  146M 0s
226950K .......... .......... .......... .......... .......... 95%  143M 0s
227000K .......... .......... .......... .......... .......... 95%  140M 0s
227050K .......... .......... .......... .......... .......... 95%  149M 0s
227100K .......... .......... .......... .......... .......... 95% 7.31M 0s
227150K .......... .......... .......... .......... .......... 95%  164M 0s
227200K .......... .......... .......... .......... .......... 95%  248M 0s
227250K .......... .......... .......... .......... .......... 95%  242M 0s
227300K .......... .......... .......... .......... .......... 96%  186M 0s
227350K .......... .......... .......... .......... .......... 96% 11.1M 0s
227400K .......... .......... .......... .......... .......... 96% 82.8M 0s
227450K .......... .......... .......... .......... .......... 96% 77.1M 0s
227500K .......... .......... .......... .......... .......... 96% 66.2M 0s
227550K .......... .......... .......... .......... .......... 96% 90.0M 0s
227600K .......... .......... .......... .......... .......... 96% 85.2M 0s
227650K .......... .......... .......... .......... .......... 96%  112M 0s
227700K .......... .......... .......... .......... .......... 96% 44.5M 0s
227750K .......... .......... .......... .......... .......... 96%  122M 0s
227800K .......... .......... .......... .......... .......... 96% 74.1M 0s
227850K .......... .......... .......... .......... .......... 96% 83.3M 0s
227900K .......... .......... .......... .......... .......... 96% 81.8M 0s
227950K .......... .......... .......... .......... .......... 96% 63.0M 0s
228000K .......... .......... .......... .......... .......... 96% 92.9M 0s
228050K .......... .......... .......... .......... .......... 96% 47.2M 0s
228100K .......... .......... .......... .......... .......... 96%  179M 0s
228150K .......... .......... .......... .......... .......... 96% 21.6M 0s
228200K .......... .......... .......... .......... .......... 96%  125M 0s
228250K .......... .......... .......... .......... .......... 96% 56.1M 0s
228300K .......... .......... .......... .......... .......... 96% 8.03M 0s
228350K .......... .......... .......... .......... .......... 96% 79.5M 0s
228400K .......... .......... .......... .......... .......... 96% 83.4M 0s
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228500K .......... .......... .......... .......... .......... 96% 58.6M 0s
228550K .......... .......... .......... .......... .......... 96% 13.6M 0s
228600K .......... .......... .......... .......... .......... 96%  229M 0s
228650K .......... .......... .......... .......... .......... 96%  292M 0s
228700K .......... .......... .......... .......... .......... 96%  157M 0s
228750K .......... .......... .......... .......... .......... 96%  182M 0s
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228850K .......... .......... .......... .......... .......... 96%  218M 0s
228900K .......... .......... .......... .......... .......... 96%  101M 0s
228950K .......... .......... .......... .......... .......... 96%  150M 0s
229000K .......... .......... .......... .......... .......... 96%  159M 0s
229050K .......... .......... .......... .......... .......... 96% 57.8M 0s
229100K .......... .......... .......... .......... .......... 96%  169M 0s
229150K .......... .......... .......... .......... .......... 96%  103M 0s
229200K .......... .......... .......... .......... .......... 96% 78.7M 0s
229250K .......... .......... .......... .......... .......... 96% 75.6M 0s
229300K .......... .......... .......... .......... .......... 96% 76.0M 0s
229350K .......... .......... .......... .......... .......... 96% 50.0M 0s
229400K .......... .......... .......... .......... .......... 96% 68.0M 0s
229450K .......... .......... .......... .......... .......... 96% 8.64M 0s
229500K .......... .......... .......... .......... .......... 96%  232M 0s
229550K .......... .......... .......... .......... .......... 96%  227M 0s
229600K .......... .......... .......... .......... .......... 96% 84.9M 0s
229650K .......... .......... .......... .......... .......... 96% 6.66M 0s
229700K .......... .......... .......... .......... .......... 97% 60.3M 0s
229750K .......... .......... .......... .......... .......... 97% 41.5M 0s
229800K .......... .......... .......... .......... .......... 97%  105M 0s
229850K .......... .......... .......... .......... .......... 97% 14.4M 0s
229900K .......... .......... .......... .......... .......... 97% 7.37M 0s
229950K .......... .......... .......... .......... .......... 97%  176M 0s
230000K .......... .......... .......... .......... .......... 97%  255M 0s
230050K .......... .......... .......... .......... .......... 97%  172M 0s
230100K .......... .......... .......... .......... .......... 97%  170M 0s
230150K .......... .......... .......... .......... .......... 97% 93.1M 0s
230200K .......... .......... .......... .......... .......... 97% 10.5M 0s
230250K .......... .......... .......... .......... .......... 97%  170M 0s
230300K .......... .......... .......... .......... .......... 97% 80.5M 0s
230350K .......... .......... .......... .......... .......... 97% 62.9M 0s
230400K .......... .......... .......... .......... .......... 97% 16.0M 0s
230450K .......... .......... .......... .......... .......... 97%  242M 0s
230500K .......... .......... .......... .......... .......... 97% 17.1M 0s
230550K .......... .......... .......... .......... .......... 97%  169M 0s
230600K .......... .......... .......... .......... .......... 97% 9.90M 0s
230650K .......... .......... .......... .......... .......... 97%  225M 0s
230700K .......... .......... .......... .......... .......... 97%  242M 0s
230750K .......... .......... .......... .......... .......... 97%  189M 0s
230800K .......... .......... .......... .......... .......... 97% 9.72M 0s
230850K .......... .......... .......... .......... .......... 97% 17.2M 0s
230900K .......... .......... .......... .......... .......... 97%  105M 0s
230950K .......... .......... .......... .......... .......... 97%  179M 0s
231000K .......... .......... .......... .......... .......... 97%  129M 0s
231050K .......... .......... .......... .......... .......... 97%  175M 0s
231100K .......... .......... .......... .......... .......... 97%  241M 0s
231150K .......... .......... .......... .......... .......... 97% 12.0M 0s
231200K .......... .......... .......... .......... .......... 97% 8.47M 0s
231250K .......... .......... .......... .......... .......... 97%  118M 0s
231300K .......... .......... .......... .......... .......... 97% 69.6M 0s
231350K .......... .......... .......... .......... .......... 97%  109M 0s
231400K .......... .......... .......... .......... .......... 97% 85.1M 0s
231450K .......... .......... .......... .......... .......... 97% 75.6M 0s
231500K .......... .......... .......... .......... .......... 97% 62.9M 0s
231550K .......... .......... .......... .......... .......... 97%  107M 0s
231600K .......... .......... .......... .......... .......... 97% 94.2M 0s
231650K .......... .......... .......... .......... .......... 97% 95.4M 0s
231700K .......... .......... .......... .......... .......... 97% 61.5M 0s
231750K .......... .......... .......... .......... .......... 97% 45.7M 0s
231800K .......... .......... .......... .......... .......... 97% 71.3M 0s
231850K .......... .......... .......... .......... .......... 97% 95.3M 0s
231900K .......... .......... .......... .......... .......... 97%  112M 0s
231950K .......... .......... .......... .......... .......... 97% 55.6M 0s
232000K .......... .......... .......... .......... .......... 97% 61.1M 0s
232050K .......... .......... .......... .......... .......... 98% 63.7M 0s
232100K .......... .......... .......... .......... .......... 98%  117M 0s
232150K .......... .......... .......... .......... .......... 98% 70.8M 0s
232200K .......... .......... .......... .......... .......... 98% 46.6M 0s
232250K .......... .......... .......... .......... .......... 98%  198M 0s
232300K .......... .......... .......... .......... .......... 98% 87.8M 0s
232350K .......... .......... .......... .......... .......... 98% 10.0M 0s
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2025-03-02 23:01:07 (32.0 MB/s) - ‘./tmp_data/ntuple_merged_90.root’ saved [242496200/242496200]
from DeepSetsDataset import DeepSetsDataset

train_files = ["./tmp_data/ntuple_merged_90.root"]

train_generator = DeepSetsDataset(
    features,
    labels,
    spectators,
    start_event=0,
    stop_event=10000,
    npad=ntracks,
    file_names=train_files,
)
train_generator.process()

test_generator = DeepSetsDataset(
    features,
    labels,
    spectators,
    start_event=10001,
    stop_event=14001,
    npad=ntracks,
    file_names=train_files,
)
test_generator.process()

Deep Sets Network#

Deep Sets models are designed to be explicitly permutation invariant. At their core they are composed of two networks, \(\phi\) and \(\rho\), such that the total network \(f\) is given by

\[ \Large \begin{align} f &= \rho\left(\Sigma_{\mathbf{x}_i\in\mathcal{X}} ~ \phi(\mathbf{x}_i)\right) \end{align} \]

where \(\mathbf{x}_i\) are the features for the \(i\)-th element in the input sequence \(\mathcal{X}\).

We will define a DeepSets model that will take as input up to 60 of the tracks (with 48 features) with zero-padding.

import torch.nn as nn
import torch.nn.functional as F
from torch.nn import (
    Sequential as Seq,
    Linear as Lin,
    ReLU,
    BatchNorm1d,
    AvgPool1d,
    Sigmoid,
    Conv1d,
)
from torch_scatter import scatter_mean

# ntracks = 60
inputs = 6
hidden1 = 64
hidden2 = 32
hidden3 = 16
classify1 = 50
outputs = 2

class DeepSets(torch.nn.Module):
    def __init__(self):
        super(DeepSets, self).__init__()
        self.phi = Seq(
            Conv1d(inputs, hidden1, 1),
            BatchNorm1d(hidden1),
            ReLU(),
            Conv1d(hidden1, hidden2, 1),
            BatchNorm1d(hidden2),
            ReLU(),
            Conv1d(hidden2, hidden3, 1),
            BatchNorm1d(hidden3),
            ReLU(),
        )
        self.rho = Seq(
            Lin(hidden3, classify1),
            BatchNorm1d(classify1),
            ReLU(),
            Lin(classify1, outputs),
            Sigmoid(),
        )

    def forward(self, x):
        out = self.phi(x)
        out = scatter_mean(out, torch.LongTensor(np.zeros(ntracks)), dim=-1)
        return self.rho(torch.squeeze(out))


model = DeepSets()
print(model)
print("----------")
print({l: model.state_dict()[l].shape for l in model.state_dict()})

model = DeepSets().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-2)
DeepSets(
  (phi): Sequential(
    (0): Conv1d(6, 64, kernel_size=(1,), stride=(1,))
    (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
    (3): Conv1d(64, 32, kernel_size=(1,), stride=(1,))
    (4): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU()
    (6): Conv1d(32, 16, kernel_size=(1,), stride=(1,))
    (7): BatchNorm1d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (8): ReLU()
  )
  (rho): Sequential(
    (0): Linear(in_features=16, out_features=50, bias=True)
    (1): BatchNorm1d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
    (3): Linear(in_features=50, out_features=2, bias=True)
    (4): Sigmoid()
  )
)
----------
{'phi.0.weight': torch.Size([64, 6, 1]), 'phi.0.bias': torch.Size([64]), 'phi.1.weight': torch.Size([64]), 'phi.1.bias': torch.Size([64]), 'phi.1.running_mean': torch.Size([64]), 'phi.1.running_var': torch.Size([64]), 'phi.1.num_batches_tracked': torch.Size([]), 'phi.3.weight': torch.Size([32, 64, 1]), 'phi.3.bias': torch.Size([32]), 'phi.4.weight': torch.Size([32]), 'phi.4.bias': torch.Size([32]), 'phi.4.running_mean': torch.Size([32]), 'phi.4.running_var': torch.Size([32]), 'phi.4.num_batches_tracked': torch.Size([]), 'phi.6.weight': torch.Size([16, 32, 1]), 'phi.6.bias': torch.Size([16]), 'phi.7.weight': torch.Size([16]), 'phi.7.bias': torch.Size([16]), 'phi.7.running_mean': torch.Size([16]), 'phi.7.running_var': torch.Size([16]), 'phi.7.num_batches_tracked': torch.Size([]), 'rho.0.weight': torch.Size([50, 16]), 'rho.0.bias': torch.Size([50]), 'rho.1.weight': torch.Size([50]), 'rho.1.bias': torch.Size([50]), 'rho.1.running_mean': torch.Size([50]), 'rho.1.running_var': torch.Size([50]), 'rho.1.num_batches_tracked': torch.Size([]), 'rho.3.weight': torch.Size([2, 50]), 'rho.3.bias': torch.Size([2])}

Define the training loop#

@torch.no_grad()
def test(model, loader, total, batch_size, leave=False):
    model.eval()

    xentropy = nn.CrossEntropyLoss(reduction="mean")

    sum_loss = 0.0
    t = tqdm(enumerate(loader), total=total / batch_size, leave=leave)
    for i, data in t:
        x = data[0].to(device)
        y = data[1].to(device)
        y = torch.argmax(y, dim=1)
        batch_output = model(x)
        batch_loss_item = xentropy(batch_output, y).item()
        sum_loss += batch_loss_item
        t.set_description("loss = %.5f" % (batch_loss_item))
        t.refresh()  # to show immediately the update

    return sum_loss / (i + 1)

def train(model, optimizer, loader, total, batch_size, leave=False):
    model.train()

    xentropy = nn.CrossEntropyLoss(reduction="mean")

    sum_loss = 0.0
    t = tqdm(enumerate(loader), total=total / batch_size, leave=leave)
    for i, data in t:
        x = data[0].to(device)
        y = data[1].to(device)
        y = torch.argmax(y, dim=1)
        optimizer.zero_grad()
        batch_output = model(x)
        batch_loss = xentropy(batch_output, y)
        batch_loss.backward()
        batch_loss_item = batch_loss.item()
        t.set_description("loss = %.5f" % batch_loss_item)
        t.refresh()  # to show immediately the update
        sum_loss += batch_loss_item
        optimizer.step()

    return sum_loss / (i + 1)

Define training, validation, testing data generators#

from torch.utils.data import ConcatDataset

train_generator_data = ConcatDataset(train_generator.datas)
test_generator_data = ConcatDataset(test_generator.datas)
from torch.utils.data import random_split, DataLoader

torch.manual_seed(0)
valid_frac = 0.20
train_length = len(train_generator_data)
valid_num = int(valid_frac * train_length)
batch_size = 32

train_dataset, valid_dataset = random_split(
    train_generator_data, [train_length - valid_num, valid_num]
)

def collate(items):
    l = sum(items, [])
    return Batch.from_data_list(l)


train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
# train_loader.collate_fn = collate
valid_loader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False)
# valid_loader.collate_fn = collate
test_loader = DataLoader(test_generator_data, batch_size=batch_size, shuffle=False)
# test_loader.collate_fn = collate

train_samples = len(train_dataset)
valid_samples = len(valid_dataset)
test_samples = len(test_generator_data)
print(train_length)
print(train_samples)
print(valid_samples)
print(test_samples)
9387
7510
1877
3751

Train#

import os.path as osp

n_epochs = 30
stale_epochs = 0
best_valid_loss = 99999
patience = 5
t = tqdm(range(0, n_epochs))

for epoch in t:
    loss = train(
        model,
        optimizer,
        train_loader,
        train_samples,
        batch_size,
        leave=bool(epoch == n_epochs - 1),
    )
    valid_loss = test(
        model,
        valid_loader,
        valid_samples,
        batch_size,
        leave=bool(epoch == n_epochs - 1),
    )
    print("Epoch: {:02d}, Training Loss:   {:.4f}".format(epoch, loss))
    print("           Validation Loss: {:.4f}".format(valid_loss))

    if valid_loss < best_valid_loss:
        best_valid_loss = valid_loss
        modpath = osp.join("./tmp_data/deepsets_best.pth")
        print("New best model saved to:", modpath)
        torch.save(model.state_dict(), modpath)
        stale_epochs = 0
    else:
        print("Stale epoch")
        stale_epochs += 1
    if stale_epochs >= patience:
        print("Early stopping after %i stale epochs" % patience)
        break
Epoch: 00, Training Loss:   0.4483
           Validation Loss: 0.4470
New best model saved to: ./tmp_data/deepsets_best.pth
Epoch: 01, Training Loss:   0.4401
           Validation Loss: 0.4497
Stale epoch
Epoch: 02, Training Loss:   0.4416
           Validation Loss: 0.4489
Stale epoch
Epoch: 03, Training Loss:   0.4401
           Validation Loss: 0.4489
Stale epoch
Epoch: 04, Training Loss:   0.4404
           Validation Loss: 0.4487
Stale epoch
Epoch: 05, Training Loss:   0.4400
           Validation Loss: 0.4459
New best model saved to: ./tmp_data/deepsets_best.pth
Epoch: 06, Training Loss:   0.4394
           Validation Loss: 0.4467
Stale epoch
Epoch: 07, Training Loss:   0.4390
           Validation Loss: 0.4455
New best model saved to: ./tmp_data/deepsets_best.pth
Epoch: 08, Training Loss:   0.4385
           Validation Loss: 0.4476
Stale epoch
Epoch: 09, Training Loss:   0.4390
           Validation Loss: 0.4489
Stale epoch
Epoch: 10, Training Loss:   0.4382
           Validation Loss: 0.4488
Stale epoch
Epoch: 11, Training Loss:   0.4391
           Validation Loss: 0.4450
New best model saved to: ./tmp_data/deepsets_best.pth
Epoch: 12, Training Loss:   0.4384
           Validation Loss: 0.4428
New best model saved to: ./tmp_data/deepsets_best.pth
Epoch: 13, Training Loss:   0.4385
           Validation Loss: 0.4489
Stale epoch
Epoch: 14, Training Loss:   0.4393
           Validation Loss: 0.4489
Stale epoch
Epoch: 15, Training Loss:   0.4384
           Validation Loss: 0.4436
Stale epoch
Epoch: 16, Training Loss:   0.4388
           Validation Loss: 0.4489
Stale epoch
Epoch: 17, Training Loss:   0.4380
           Validation Loss: 0.4489
Stale epoch
Early stopping after 5 stale epochs

Evaluate on Test Data#

# In case you need to load the model from a pth file
# Trained on 4 vectors (as above in notebook)
# urlPTH = "https://raw.githubusercontent.com/jmduarte/iaifi-summer-school/main/book/deepsets_best_4vec.pth"
# pthFile = wget.download(urlPTH)
# model.load_state_dict(torch.load("deepsets_best_4vec.pth"))
# Trained on all possible inputs (a different configuration)
# urlPTH = "https://raw.githubusercontent.com/jmduarte/iaifi-summer-school/main/book/deepsets_best_AllTraining.pth"
# pthFile = wget.download(urlPTH)
# model.load_state_dict(torch.load("deepsets_best_AllTraining.pth"))

model.eval()
t = tqdm(enumerate(test_loader), total=test_samples / batch_size)
y_test = []
y_predict = []
track_pt = []
for i, data in t:
    x = data[0].to(device)
    y = data[1].to(device)
    track_pt.append(x[:, 0, 0].numpy())
    batch_output = model(x)
    y_predict.append(batch_output.detach().cpu().numpy())
    y_test.append(y.cpu().numpy())
track_pt = np.concatenate(track_pt)
y_test = np.concatenate(y_test)
y_predict = np.concatenate(y_predict)
_, bins, _ = plt.hist(track_pt[y_test[:, 1] == 1], bins=50, label="sig", histtype="step")
_, bins, _ = plt.hist(track_pt[y_test[:, 1] == 0], bins=bins, label="bkg", histtype="step")
plt.legend()
plt.semilogy()
[]
../../_images/b44ff480cf16fae4cae51b793110118a5291e2842c5c4f20c229cd4cbdfa2844.png
from sklearn.metrics import roc_curve, auc
import mplhep as hep

plt.style.use(hep.style.ROOT)
# create ROC curves
fpr_deepset, tpr_deepset, threshold_deepset = roc_curve(y_test[:, 1], y_predict[:, 1])
with open("./tmp_data/deepset_roc.npy", "wb") as f:
    np.save(f, fpr_deepset)
    np.save(f, tpr_deepset)
    np.save(f, threshold_deepset)

# plot ROC curves
plt.figure()
plt.plot(
    fpr_deepset,
    tpr_deepset,
    lw=2.5,
    label="DeepSet, AUC = {:.1f}%".format(auc(fpr_deepset, tpr_deepset) * 100),
)
plt.xlabel(r"False positive rate")
plt.ylabel(r"True positive rate")
plt.ylim(0.001, 1)
plt.xlim(0, 1)
plt.grid(True)
plt.legend(loc="upper left")
plt.show()
../../_images/546bcf1663fb51df3c8b276ba939784aaecafd4c72fd53bc37d689902a339255.png

AUC and ROC Curves#

To better understand this curve, let define the confusion matrix:

https://raw.githubusercontent.com/illinois-ipaml/MachineLearningForPhysics/main/img/GraphNeuralNetworks-ConfusionMatrix.png

True Positive Rate (TPR) is a synonym for recall/sensitivity and is therefore defined as follows: $\( \Large TPR = \frac{TP}{TP + FN} \)$ TPR tells us what proportion of the positive class got correctly classified. A simple example would be determining what proportion of the actual sick people were correctly detected by the model.

False Negative Rate (FNR) is defined as follows: $\( \Large FNR = \frac{FN}{TP + FN} \)$ FNR tells us what proportion of the positive class got incorrectly classified by the classifier. A higher TPR and a lower FNR are desirable since we want to classify the positive class correctly.

True Negative Rate (TNR) is a synonym for specificity and is defined as follows: $\( \Large TNR = \frac{TN}{TN + FP} \)$ Specificity tells us what proportion of the negative class got correctly classified. Taking the same example as in Sensitivity, Specificity would mean determining the proportion of healthy people who were correctly identified by the model.

False Positive Rate (FPR) is defined as follows: $\( \Large TPR = \frac{FP}{FP + TN} \)$ FPR tells us what proportion of the negative class got incorrectly classified by the classifier. A higher TNR and a lower FPR are desirable since we want to classify the negative class correctly.

A ROC curve (receiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds. This curve plots two parameters:

  • True Positive Rate

  • False Positive Rate

An ROC curve plots TPR vs. FPR at different classification thresholds. Lowering the classification threshold classifies more items as positive, thus increasing both False Positives and True Positives. The following figure shows a typical ROC curve.

https://raw.githubusercontent.com/illinois-ipaml/MachineLearningForPhysics/main/img/GraphNeuralNetworks-ROC.png

AUC stands for “Area under the ROC Curve.” That is, AUC measures the entire two-dimensional area underneath the entire ROC curve (think integral calculus) from (0,0) to (1,1). AUC provides an aggregate measure of performance across all possible classification thresholds.

https://raw.githubusercontent.com/illinois-ipaml/MachineLearningForPhysics/main/img/GraphNeuralNetworks-AUC.png


Interaction Network#

Now we will look at graph neural networks using the PyTorch Geometric library: https://pytorch-geometric.readthedocs.io/. See [] for more details.

wget_data("https://raw.githubusercontent.com/jmduarte/iaifi-summer-school/main/book/definitions.yml")
--2025-03-02 23:16:29--  https://raw.githubusercontent.com/jmduarte/iaifi-summer-school/main/book/definitions.yml
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 1227 (1.2K) [text/plain]
Saving to: ‘./tmp_data/definitions.yml’
     0K .                                                     100%  104M=0s
2025-03-02 23:16:29 (104 MB/s) - ‘./tmp_data/definitions.yml’ saved [1227/1227]
import yaml

with open("./tmp_data/definitions.yml") as file:
    # The FullLoader parameter handles the conversion from YAML
    # scalar values to Python the dictionary format
    definitions = yaml.load(file, Loader=yaml.FullLoader)

# You can test with using only 4-vectors by using:
# if not os.path.exists("definitions_lorentz.yml"):
#    url = "https://raw.githubusercontent.com/jmduarte/iaifi-summer-school/main/book/definitions_lorentz.yml"
#    definitionsFile = wget.download(url)
# with open('definitions_lorentz.yml') as file:
#    # The FullLoader parameter handles the conversion from YAML
#    # scalar values to Python the dictionary format
#    definitions = yaml.load(file, Loader=yaml.FullLoader)

features   = definitions["features"]
spectators = definitions["spectators"]
labels     = definitions["labels"]

nfeatures   = definitions["nfeatures"]
nspectators = definitions["nspectators"]
nlabels     = definitions["nlabels"]
ntracks     = definitions["ntracks"]

Graph Datasets#

Here we have to define the graph dataset. We do this in a separate class following this example: https://pytorch-geometric.readthedocs.io/en/latest/notes/create_dataset.html#creating-larger-datasets

Formally, a graph is represented by a triplet \(\mathcal G = (\mathbf{u}, V, E)\), consisting of a graph-level, or global, feature vector \(\mathbf{u}\), a set of \(N^v\) nodes \(V\), and a set of \(N^e\) edges \(E\). The nodes are given by \(V = \{\mathbf{v}_i\}_{i=1:N^v}\), where \(\mathbf{v}_i\) represents the \(i\)th node’s attributes. The edges connect pairs of nodes and are given by \(E = \{\left(\mathbf{e}_k, r_k, s_k\right)\}_{k=1:N^e}\), where \(\mathbf{e}_k\) represents the \(k\)th edge’s attributes, and \(r_k\) and \(s_k\) are the indices of the “receiver” and “sender” nodes, respectively, connected by the \(k\)th edge (from the sender node to the receiver node). The receiver and sender index vectors are an alternative way of encoding the directed adjacency matrix.

attributes
wget_data("https://raw.githubusercontent.com/jmduarte/iaifi-summer-school/main/book/GraphDataset.py", './')
--2025-03-02 23:16:44--  https://raw.githubusercontent.com/jmduarte/iaifi-summer-school/main/book/GraphDataset.py
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 6953 (6.8K) [text/plain]
Saving to: ‘./GraphDataset.py’
     0K ......                                                100% 28.5M=0s
2025-03-02 23:16:45 (28.5 MB/s) - ‘./GraphDataset.py’ saved [6953/6953]
from GraphDataset import GraphDataset

file_names = ["./tmp_data/ntuple_merged_90.root"]

graph_dataset = GraphDataset(
    "./tmp_data/gdata_train",
    features,
    labels,
    spectators,
    start_event=0,
    stop_event=8000,
    n_events_merge=1,
    file_names=file_names,
)

test_dataset = GraphDataset(
    "./tmp_data/gdata_test",
    features,
    labels,
    spectators,
    start_event=8001,
    stop_event=10001,
    n_events_merge=1,
    file_names=file_names,
)
Processing...
Done!
Processing...
Done!

Graph Neural Network#

Here, we recapitulate the “graph network” (GN) formalism described in this paper, which generalizes various GNNs and other similar methods.

GNs are graph-to-graph mappings, whose output graphs have the same node and edge structure as the input. Formally, a GN block contains three “update” functions, \(\phi\), and three “aggregation” functions, \(\rho\). The stages of processing in a single GN block are:

\[\begin{split} \begin{align} \mathbf{e}'_k &= \phi^e\left(\mathbf{e}_k, \mathbf{v}_{r_k}, \mathbf{v}_{s_k}, \mathbf{u} \right) & \mathbf{\bar{e}}'_i &= \rho^{e \rightarrow v}\left(E'_i\right) & \text{(Edge block),} \\ \mathbf{v}'_i &= \phi^v\left(\mathbf{\bar{e}}'_i, \mathbf{v}_i, \mathbf{u}\right) & \mathbf{\bar{e}}' &= \rho^{e \rightarrow u}\left(E'\right) & \text{(Node block),} \\ \mathbf{u}' &= \phi^u\left(\mathbf{\bar{e}}', \mathbf{\bar{v}}', \mathbf{u}\right) & \mathbf{\bar{v}}' &= \rho^{v \rightarrow u}\left(V'\right) &\text{(Global block).} \end{align} \end{split}\]

where \(E'_i = \left\{\left(\mathbf{e}'_k, r_k, s_k \right)\right\}_{r_k=i,\; k=1:N^e}\) contains the updated edge features for edges whose receiver node is the \(i^\text{th}\) node, \(E' = \bigcup_i E_i' = \left\{\left(\mathbf{e}'_k, r_k, s_k \right)\right\}_{k=1:N^e}\) is the set of updated edges, and \(V'=\left\{\mathbf{v}'_i\right\}_{i=1:N^v}\) is the set of updated nodes.

GN full block

We will define an interaction network model similar to this paper, but just modeling the particle-particle interactions. It will take as input all of the tracks (with 48 features) without truncating or zero-padding. Another modification is the use of batch normalization [] layers to improve the stability of the training.

import torch.nn as nn
import torch.nn.functional as F
import torch_geometric.transforms as T
from torch_geometric.nn import EdgeConv, global_mean_pool
from torch.nn import Sequential as Seq, Linear as Lin, ReLU, BatchNorm1d
from torch_scatter import scatter_mean
from torch_geometric.nn import MetaLayer

inputs = 48
hidden = 128
outputs = 2

class EdgeBlock(torch.nn.Module):
    def __init__(self):
        super(EdgeBlock, self).__init__()
        self.edge_mlp = Seq(
            Lin(inputs * 2, hidden), BatchNorm1d(hidden), ReLU(), Lin(hidden, hidden)
        )

    def forward(self, src, dest, edge_attr, u, batch):
        out = torch.cat([src, dest], 1)
        return self.edge_mlp(out)


class NodeBlock(torch.nn.Module):
    def __init__(self):
        super(NodeBlock, self).__init__()
        self.node_mlp_1 = Seq(
            Lin(inputs + hidden, hidden),
            BatchNorm1d(hidden),
            ReLU(),
            Lin(hidden, hidden),
        )
        self.node_mlp_2 = Seq(
            Lin(inputs + hidden, hidden),
            BatchNorm1d(hidden),
            ReLU(),
            Lin(hidden, hidden),
        )

    def forward(self, x, edge_index, edge_attr, u, batch):
        row, col = edge_index
        out = torch.cat([x[row], edge_attr], dim=1)
        out = self.node_mlp_1(out)
        out = scatter_mean(out, col, dim=0, dim_size=x.size(0))
        out = torch.cat([x, out], dim=1)
        return self.node_mlp_2(out)

class GlobalBlock(torch.nn.Module):
    def __init__(self):
        super(GlobalBlock, self).__init__()
        self.global_mlp = Seq(
            Lin(hidden, hidden), BatchNorm1d(hidden), ReLU(), Lin(hidden, outputs)
        )

    def forward(self, x, edge_index, edge_attr, u, batch):
        out = scatter_mean(x, batch, dim=0)
        return self.global_mlp(out)

class InteractionNetwork(torch.nn.Module):
    def __init__(self):
        super(InteractionNetwork, self).__init__()
        self.interactionnetwork = MetaLayer(EdgeBlock(), NodeBlock(), GlobalBlock())
        self.bn = BatchNorm1d(inputs)

    def forward(self, x, edge_index, batch):

        x = self.bn(x)
        x, edge_attr, u = self.interactionnetwork(x, edge_index, None, None, batch)
        return u


model = InteractionNetwork().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-2)

Define the training loop#

@torch.no_grad()
def test(model, loader, total, batch_size, leave=False):
    model.eval()

    xentropy = nn.CrossEntropyLoss(reduction="mean")

    sum_loss = 0.0
    t = tqdm(enumerate(loader), total=total / batch_size, leave=leave)
    for i, data in t:
        data = data.to(device)
        y = torch.argmax(data.y, dim=1)
        batch_output = model(data.x, data.edge_index, data.batch)
        batch_loss_item = xentropy(batch_output, y).item()
        sum_loss += batch_loss_item
        t.set_description("loss = %.5f" % (batch_loss_item))
        t.refresh()  # to show immediately the update

    return sum_loss / (i + 1)

def train(model, optimizer, loader, total, batch_size, leave=False):
    model.train()

    xentropy = nn.CrossEntropyLoss(reduction="mean")

    sum_loss = 0.0
    t = tqdm(enumerate(loader), total=total / batch_size, leave=leave)
    for i, data in t:
        data = data.to(device)
        y = torch.argmax(data.y, dim=1)
        optimizer.zero_grad()
        batch_output = model(data.x, data.edge_index, data.batch)
        batch_loss = xentropy(batch_output, y)
        batch_loss.backward()
        batch_loss_item = batch_loss.item()
        t.set_description("loss = %.5f" % batch_loss_item)
        t.refresh()  # to show immediately the update
        sum_loss += batch_loss_item
        optimizer.step()

    return sum_loss / (i + 1)

Define training, validation, testing data generators#

from torch_geometric.data import Data, DataListLoader, Batch
from torch.utils.data import random_split

def collate(items):
    l = sum(items, [])
    return Batch.from_data_list(l)

torch.manual_seed(0)
valid_frac = 0.20
full_length = len(graph_dataset)
valid_num = int(valid_frac * full_length)
batch_size = 32

train_dataset, valid_dataset = random_split(
    graph_dataset, [full_length - valid_num, valid_num]
)

train_loader = DataListLoader(
    train_dataset, batch_size=batch_size, pin_memory=True, shuffle=True
)
train_loader.collate_fn = collate
valid_loader = DataListLoader(
    valid_dataset, batch_size=batch_size, pin_memory=True, shuffle=False
)
valid_loader.collate_fn = collate
test_loader = DataListLoader(
    test_dataset, batch_size=batch_size, pin_memory=True, shuffle=False
)
test_loader.collate_fn = collate

train_samples = len(train_dataset)
valid_samples = len(valid_dataset)
test_samples = len(test_dataset)
print(full_length)
print(train_samples)
print(valid_samples)
print(test_samples)
7501
6001
1500
1886

Train#

import os.path as osp

n_epochs = 10
stale_epochs = 0
best_valid_loss = 99999
patience = 5
t = tqdm(range(0, n_epochs))

for epoch in t:
    loss = train(
        model,
        optimizer,
        train_loader,
        train_samples,
        batch_size,
        leave=bool(epoch == n_epochs - 1),
    )
    valid_loss = test(
        model,
        valid_loader,
        valid_samples,
        batch_size,
        leave=bool(epoch == n_epochs - 1),
    )
    print("Epoch: {:02d}, Training Loss:   {:.4f}".format(epoch, loss))
    print("           Validation Loss: {:.4f}".format(valid_loss))

    if valid_loss < best_valid_loss:
        best_valid_loss = valid_loss
        modpath = osp.join("./tmp_data/interactionnetwork_best.pth")
        print("New best model saved to:", modpath)
        torch.save(model.state_dict(), modpath)
        stale_epochs = 0
    else:
        print("Stale epoch")
        stale_epochs += 1
    if stale_epochs >= patience:
        print("Early stopping after %i stale epochs" % patience)
        break
Epoch: 00, Training Loss:   0.2126
           Validation Loss: 0.1896
New best model saved to: ./tmp_data/interactionnetwork_best.pth
Epoch: 01, Training Loss:   0.1745
           Validation Loss: 0.1402
New best model saved to: ./tmp_data/interactionnetwork_best.pth
Epoch: 02, Training Loss:   0.1544
           Validation Loss: 0.1920
Stale epoch
Epoch: 03, Training Loss:   0.1475
           Validation Loss: 0.1350
New best model saved to: ./tmp_data/interactionnetwork_best.pth
Epoch: 04, Training Loss:   0.1414
           Validation Loss: 0.1166
New best model saved to: ./tmp_data/interactionnetwork_best.pth
Epoch: 05, Training Loss:   0.1402
           Validation Loss: 0.1520
Stale epoch
Epoch: 06, Training Loss:   0.1351
           Validation Loss: 0.1213
Stale epoch
Epoch: 07, Training Loss:   0.1258
           Validation Loss: 0.1332
Stale epoch
Epoch: 08, Training Loss:   0.1288
           Validation Loss: 0.1260
Stale epoch
Epoch: 09, Training Loss:   0.1264
           Validation Loss: 0.1419
Stale epoch
Early stopping after 5 stale epochs

Evaluate on Test Data#

model.eval()
t = tqdm(enumerate(test_loader), total=test_samples / batch_size)
y_test = []
y_predict = []
for i, data in t:
    data = data.to(device)
    batch_output = model(data.x, data.edge_index, data.batch)
    y_predict.append(batch_output.detach().cpu().numpy())
    y_test.append(data.y.cpu().numpy())
y_test = np.concatenate(y_test)
y_predict = np.concatenate(y_predict)
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
import mplhep as hep

plt.style.use(hep.style.ROOT)
# create ROC curves
fpr_gnn, tpr_gnn, threshold_gnn = roc_curve(y_test[:, 1], y_predict[:, 1])
with open("./tmp_data/gnn_roc.npy", "wb") as f:
    np.save(f, fpr_gnn)
    np.save(f, tpr_gnn)
    np.save(f, threshold_gnn)

with open("./tmp_data/deepset_roc.npy", "rb") as f:
    fpr_deepset = np.load(f)
    tpr_deepset = np.load(f)
    threshold_deepset = np.load(f)

# plot ROC curves
plt.figure()
plt.plot(
    fpr_deepset,
    tpr_deepset,
    lw=2.5,
    label="DeepSet, AUC = {:.1f}%".format(auc(fpr_deepset, tpr_deepset) * 100),
)
plt.plot(
    fpr_gnn,
    tpr_gnn,
    lw=2.5,
    label="GNN, AUC = {:.1f}%".format(auc(fpr_gnn, tpr_gnn) * 100),
)
plt.xlabel(r"False positive rate")
plt.ylabel(r"True positive rate")
#plt.semilogx()
plt.ylim(0.001, 1)
plt.xlim(0, 1)
plt.grid(True)
#plt.legend(loc="upper left")
plt.legend(loc="lower right")
plt.show()
../../_images/b768a3df14f8d9d0dc482bcd0819b1ab0060158120f1f742539f28898b751341.png

Acknowledgments#

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