keyword stringclasses 7 values | repo_name stringlengths 8 98 | file_path stringlengths 4 244 | file_extension stringclasses 29 values | file_size int64 0 84.1M | line_count int64 0 1.6M | content stringlengths 1 84.1M ⌀ | language stringclasses 14 values |
|---|---|---|---|---|---|---|---|
3D | lvqiujie/Mol2Context-vec | tasks/lipop/test.py | .py | 1,506 | 40 | import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from tasks.lipop.train import LSTM, MyDataset
import torch.nn.functional as F
import torch.utils.data as data
import pandas as pd
from sklearn.externals import joblib
import numpy as np
# 设置超参数
input_size = 512
num_layers = 2 #定义超参数rnn的层数，层数为1层
hidden_size = 512 #定义超参数rnn的循环神经元个数，个数为32个
learning_rate = 0.02 #定义超参数学习率
epoch_num = 1000
batch_size = 1
best_loss = 10000
test_best_loss = 1000
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# filepath="esol/delaney.csv"
# df = pd.read_csv(filepath, header=0, encoding="gbk")
y = joblib.load('lipop/label.pkl')
all_smi = np.array(joblib.load('lipop/smi.pkl'))
x = joblib.load('lipop/lipop_embed.pkl')
data_test = MyDataset(x, y,all_smi)
dataset_test = data.DataLoader(dataset=data_test, batch_size=batch_size, shuffle=True, drop_last=True)
rnn = LSTM().to(device)
rnn_dict = torch.load('lipop/lstm_net.pth')
task_matrix = torch.load('lipop/task_matrix.pth')
# print(task_matrix[0], task_matrix[1], task_matrix[2])
rnn.load_state_dict(rnn_dict)
rnn.cuda()
rnn.eval()
for tmp_x, tmp_y, tmp_smi in dataset_test:
out, alpha_n, att_n = rnn(tmp_x, task_matrix)
print(tmp_smi[0],out.cpu().detach().numpy()[0], tmp_y.cpu().detach().numpy()[0], ) | Python |
3D | lvqiujie/Mol2Context-vec | tasks/muv/get_muv_data.py | .py | 5,651 | 185 | import sys
sys.path.append('./')
import pandas as pd
import numpy as np
from rdkit import Chem
import os
from rdkit.Chem import Descriptors
from sklearn.externals import joblib
# step 1
filepath="muv/muv.csv"
df = pd.read_csv(filepath, header=0, encoding="gbk")
w_file = open("muv/muv.smi", mode='w', encoding="utf-8")
all_label = []
all_smi = []
for line in df.values:
if len(line[18]) <= 0:
continue
smi = Chem.MolToSmiles(Chem.MolFromSmiles(line[-1].strip()), isomericSmiles=True)
if len(smi) <= 0:
break
mol = Chem.MolFromSmiles(smi)
all_smi.append(smi)
all_label.append(line[:17])
w_file.write(smi + "\n")
# smi = line[-1].strip()
# mol = Chem.MolFromSmiles(smi)
# try:
# if 12 <= Descriptors.MolWt(mol) <= 600:
# if -5 <= Descriptors.MolLogP(mol) <= 7:
# flag = True
# for t in mol.GetAtoms():
# if t.GetSymbol() not in ["H", "B", "C", "N", "O", "F", "P", "S", "Cl", "Br", "I", "Si"]:
# flag = False
# print(t.GetSymbol())
# break
# if flag:
# all_label.append(line[:17])
# all_smi.append(smi)
# w_file.write(smi + "\n")
# except:
# print("error "+smi)
w_file.close()
# step 2
adb = "mol2vec corpus -i muv/muv.smi -o muv/muv.cp -r 1 -j 4 --uncommon UNK --threshold 3"
d = os.popen(adb)
f = d.read()
print(f)
# step 3
vocab_path = "data/datasets/my_smi/smi_tran.vocab"
vocab = {line.split()[0]: int(line.split()[1]) for line in open(vocab_path).readlines()}
sentence_maxlen = 80
w_file = open("muv/muv_tran.cp_UNK", mode='w', encoding="utf-8")
label = []
smi = []
index = -1
mols_path = "muv/muv.cp_UNK"
mols_file = open(mols_path, mode='r',encoding="utf-8")
while True:
line = mols_file.readline().strip()
index += 1
if "None".__eq__(line.strip()) or "UNK".__eq__(line.strip()):
continue
if not line:
break
token_ids = np.zeros((sentence_maxlen,), dtype=np.int64)
# Add begin of sentence index
token_ids[0] = vocab['<bos>']
for j, token in enumerate(line.split()[:sentence_maxlen - 2]):
# print(token)
if token.lower() in vocab:
token_ids[j + 1] = vocab[token.lower()]
else:
token_ids[j + 1] = vocab['<unk>']
# Add end of sentence index
if token_ids[1]:
token_ids[j + 2] = vocab['<eos>']
# print(token_ids)
label.append(all_label[index])
smi.append(all_smi[index])
w_file.write(" ".join(str(i) for i in token_ids).strip()+"\n")
w_file.close()
joblib.dump(label, 'muv/label.pkl')
joblib.dump(smi, 'muv/smi.pkl')
# step 4
import os
import keras.backend as K
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
# from data import DATA_SET_DIR
from context_vec.smi_generator import SMIDataGenerator
from context_vec.smi_model import context_vec
import tensorflow as tf
from tensorflow import keras
from sklearn.externals import joblib
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
keras.backend.set_session(sess)
parameters = {
'multi_processing': False,
'n_threads': 4,
'cuDNN': True if len(K.tensorflow_backend._get_available_gpus()) else False,
'test_dataset': 'muv/muv_tran.cp_UNK',
'vocab': 'my_smi/smi_tran.vocab',
'model_dir': "smi_context_vec_512",
'vocab_flag': False,
'uncommon_threshold': 3,
# 'vocab_size': 28914,
# 'vocab_size': 748,
'vocab_size': 13576,
'num_sampled': 100,
# 'charset_size': 262,
'sentence_maxlen': 80,
'token_maxlen': 50,
'token_encoding': 'word',
'epochs': 1000,
'patience': 2,
'batch_size': 512,
'test_batch_size': 512,
'clip_value': 1,
'cell_clip': 5,
'proj_clip': 5,
'lr': 0.2,
'shuffle': False,
'n_lstm_layers': 2,
'n_highway_layers': 2,
'cnn_filters': [[1, 32],
[2, 32],
[3, 64],
[4, 128],
[5, 256],
[6, 512],
[7, 512]
],
'lstm_units_size': 512,
'hidden_units_size': 300,
'char_embedding_size': 16,
'dropout_rate': 0.1,
'word_dropout_rate': 0.05,
'weight_tying': True,
}
test_generator = SMIDataGenerator(parameters['test_dataset'],
os.path.join("data/datasets", parameters['vocab']),
sentence_maxlen=parameters['sentence_maxlen'],
token_maxlen=parameters['token_maxlen'],
batch_size=parameters['test_batch_size'],
shuffle=parameters['shuffle'],
token_encoding=parameters['token_encoding'])
# Compile context_vec
context_vec_model = context_vec(parameters)
context_vec_model.compile_context_vec()
# context_vec_model.load(sampled_softmax=False)
#
# # Evaluate Bidirectional Language Model
# context_vec_model.evaluate(test_generator, parameters['test_batch_size'])
#
# # Build context_vec meta-model to deploy for production and persist in disk
# context_vec_model.wrap_multi_context_vec_encoder(print_summary=True)
# Load context_vec encoder
context_vec_model.load_context_vec_encoder()
# Get context_vec embeddings to feed as inputs for downstream tasks
context_vec_embeddings = context_vec_model.get_outputs(test_generator, output_type='word', state='all')
print(context_vec_embeddings.shape)
# 保存x
joblib.dump(context_vec_embeddings, 'muv/muv_embed.pkl')
| Python |
3D | lvqiujie/Mol2Context-vec | tasks/muv/muv_train.py | .py | 21,864 | 461 | import os
import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.nn.functional as F
import torch.utils.data as data
import pandas as pd
from sklearn.externals import joblib
import numpy as np
import math
import random
from sklearn import metrics
from sklearn.metrics import precision_recall_curve
# from utils.util import *
# from utils.model import *
import matplotlib.pyplot as plt
from sklearn.model_selection import KFold
torch.cuda.set_device(0)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class LSTM(nn.Module):
"""搭建rnn网络"""
def __init__(self, out_num, input_size=300, task_type='sing', att=False):
super(LSTM, self).__init__()
self.matrix = nn.Parameter(torch.tensor([0.33, 0.33, 0.33]), requires_grad=True)
self.input_size = input_size
self.out_num = out_num * 2 if "muti".__eq__(task_type) else out_num
self.att = att
self.fc = nn.Linear(self.input_size, 1024)
self.lstm = nn.LSTM(
input_size=1024,
hidden_size=1024,
num_layers=2,
batch_first=True,)
# bidirectional=True)
# self.fc1 = nn.Linear(512, 1024)
# self.fc2 = nn.Linear(1024, 512)
self.fc3 = nn.Linear(1024, 512)
self.fc4 = nn.Linear(512, self.out_num)
self.dropout = nn.Dropout(p=0.3)
# self.sig = nn.Sigmoid()
# self.bn1 = nn.BatchNorm1d(1024)
# self.bn2 = nn.BatchNorm1d(512)
# self.bn3 = nn.BatchNorm1d(128)
def attention_net(self, x, query, mask=None):
d_k = query.size(-1) # d_k为query的维度
# query:[batch, seq_len, hidden_dim*2], x.t:[batch, hidden_dim*2, seq_len]
# print("query: ", query.shape, x.transpose(1, 2).shape) # torch.Size([128, 38, 128]) torch.Size([128, 128, 38])
# 打分机制 scores: [batch, seq_len, seq_len]
scores = torch.matmul(query, x.transpose(1, 2)) / math.sqrt(d_k)
# print("score: ", scores.shape) # torch.Size([128, 38, 38])
# 对最后一个维度 归一化得分
alpha_n = F.softmax(scores, dim=-1)
# print("alpha_n: ", alpha_n.shape) # torch.Size([128, 38, 38])
# 对权重化的x求和
# [batch, seq_len, seq_len]·[batch,seq_len, hidden_dim*2] = [batch,seq_len,hidden_dim*2] -> [batch, hidden_dim*2]
context = torch.matmul(alpha_n, x).sum(1)
return context, alpha_n
def forward(self, x):
# bs = len(x)
# length = np.array([t.shape[0] for t in x])
#
# x, orderD = pack_sequences(x)
# print(self.matrix[0],self.matrix[1],self.matrix[2])
x = x.to(device)
x = self.matrix[0] * x[:, 0, :, :] + self.matrix[1] * x[:, 1, :, :] + self.matrix[2] * x[:, 2, :, :]
x = self.fc(x.to(device)).to(device)
# changed_length1 = length[orderD]
# x = pack_padded_sequence(x, changed_length1, batch_first=True)
out,(h_n, c_n) = self.lstm(x.to(device)) #h_state是之前的隐层状态
# out = torch.cat((h_n[-1, :, :], h_n[-2, :, :]), dim=-1)
# out1 = unpack_sequences(rnn_out, orderD)
# for i in range(bs):
# out1[i,length[i]:-1,:] = 0
if self.att:
query = self.dropout(out)
# 加入attention机制
out, alpha_n = self.attention_net(out, query)
else:
out = torch.mean(out,dim=1).squeeze().cuda()
# out = out[:,-1,:]
#进行全连接
# out = self.fc1(out[:,-1,:])
# out = F.relu(out)
# out = self.bn1(F.dropout(out, p=0.3))
# out = self.fc2(out)
# out = F.relu(out)
# out = self.bn2(F.dropout(out, p=0.3))
out = self.fc3(out)
out = F.relu(out)
out = self.dropout(out)
out = self.fc4(out)
# return F.softmax(out,dim=-1)
return out
class MyDataset(data.Dataset):
def __init__(self, compound, y, smi):
super(MyDataset, self).__init__()
self.compound = compound
# self.compound = torch.FloatTensor(compound)
# self.y = torch.FloatTensor(y)
self.y = y
self.smi = smi
def __getitem__(self, item):
return self.compound[item], self.y[item], self.smi[item]
def __len__(self):
return len(self.compound)
def split_multi_label(x, y, smi, k_fold, name):
y = np.array(y).astype(float)
# y[np.where(np.isnan(y))] = 6
all_smi = np.array(smi)
# save_path = 'tox/'+str(k_fold)+'-fold-index.pkl'
# if os.path.isfile(save_path):
# index = joblib.load(save_path)
# train_split_x = x[index["train_index"]]
# train_split_y = y[index["train_index"]]
# val_split_x = x[index["val_index"]]
# val_split_y = y[index["val_index"]]
# test_split_x = x[index["test_index"]]
# test_split_y = y[index["test_index"]]
# train_weights = joblib.load('tox/train_weights.pkl')
# return train_split_x, train_split_y, val_split_x, val_split_y, test_split_x, test_split_y, train_weights
kf = KFold(5, False, 100)
all_train_index = [[],[],[],[],[]]
all_train_index_weights = [[] for i in range(y.shape[1])]
all_val_index = [[],[],[],[],[]]
all_test_index = [[],[],[],[],[]]
for task_index in range(y.shape[-1]):
negative_index = np.where(y[:, task_index] == 0)[0]
positive_index = np.where(y[:, task_index] == 1)[0]
train_index = [[],[],[],[],[]]
val_index = [[],[],[],[],[]]
test_index = [[],[],[],[],[]]
for k, tmp in enumerate(kf.split(negative_index)):
# train_tmp is the index ofnegative_index
train_tmp, test_tmp = tmp
train_index[k].extend(negative_index[train_tmp])
num_t = int(len(test_tmp)/2)
val_index[k].extend(negative_index[test_tmp[:num_t]])
test_index[k].extend(negative_index[test_tmp[num_t:]])
for k, tmp in enumerate(kf.split(positive_index)):
train_tmp, test_tmp = tmp
train_index[k].extend(positive_index[train_tmp])
num_t = int(len(test_tmp)/2)
val_index[k].extend(positive_index[test_tmp[:num_t]])
test_index[k].extend(positive_index[test_tmp[num_t:]])
all_train_index_weights[task_index] = [(len(negative_index) + len(positive_index)) / len(negative_index),
(len(negative_index) + len(positive_index)) / len(positive_index)]
if task_index == 0:
all_train_index = train_index
all_val_index = val_index
all_test_index = test_index
else:
all_train_index = [list(set(all_train_index[i]).union(set(t))) for i, t in enumerate(train_index)]
all_val_index = [list(set(all_val_index[i]).union(set(t))) for i, t in enumerate(val_index)]
all_test_index = [list(set(all_test_index[i]).union(set(t))) for i, t in enumerate(test_index)]
for i in range(5):
joblib.dump({"train_index":all_train_index[i],
"val_index": all_val_index[i],
"test_index": all_test_index[i],
}, name+'/'+str(i+1)+'-fold-index.pkl')
joblib.dump(all_train_index_weights, name+'/weights.pkl')
train_split_x = x[all_train_index[k_fold]]
train_split_y = y[all_train_index[k_fold]]
train_split_smi = all_smi[all_train_index[k_fold]]
val_split_x = x[all_val_index[k_fold]]
val_split_y = y[all_val_index[k_fold]]
val_split_smi = all_smi[all_val_index[k_fold]]
test_split_x = x[all_test_index[k_fold]]
test_split_y = y[all_test_index[k_fold]]
test_split_smi = all_smi[all_test_index[k_fold]]
return train_split_x, train_split_y, train_split_smi,\
val_split_x, val_split_y, val_split_smi,\
test_split_x, test_split_y, test_split_smi, all_train_index_weights
if __name__ == '__main__':
# 设置超参数
input_size = 512
hidden_size = 512 # 定义超参数rnn的循环神经元个数，个数为32个
learning_rate = 0.01 # 定义超参数学习率
epoch_num = 2000
batch_size = 512
best_loss = 10000
test_best_loss = 10000
weight_decay = 1e-5
momentum = 0.9
b = 0.3
tasks = ["MUV-466", "MUV-548", "MUV-600", "MUV-644", "MUV-652", "MUV-689", "MUV-692", "MUV-712", "MUV-713",
"MUV-733", "MUV-737", "MUV-810", "MUV-832", "MUV-846", "MUV-852", "MUV-858", "MUV-859"]
y = joblib.load('muv/label.pkl')
all_smi = joblib.load('muv/smi.pkl')
x = joblib.load('muv/muv_embed.pkl')
print("data len is ",x.shape[0])
seed = 188
torch.manual_seed(seed)
random.seed(seed)
np.random.seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# 5-Fold
train_split_x, train_split_y, train_split_smi, \
val_split_x, val_split_y, val_split_smi, \
test_split_x, test_split_y, test_split_smi, weights = split_multi_label(x, y, all_smi, 3, 'muv')
del x
data_train = MyDataset(train_split_x, train_split_y, train_split_smi)
dataset_train = data.DataLoader(dataset=data_train, batch_size=batch_size, shuffle=True)
data_val = MyDataset(val_split_x, val_split_y, val_split_smi)
dataset_val = data.DataLoader(dataset=data_val, batch_size=batch_size, shuffle=True)
data_test = MyDataset(test_split_x, test_split_y, test_split_smi)
dataset_test = data.DataLoader(dataset=data_test, batch_size=batch_size, shuffle=True)
rnn = LSTM(len(tasks), task_type="muti", input_size=300, att=True).to(device)
# 设置优化器和损失函数
#使用adam优化器进行优化，输入待优化参数rnn.parameters，优化学习率为learning_rate
optimizer = torch.optim.SGD(rnn.parameters(), lr=learning_rate, weight_decay = weight_decay,
momentum = momentum)
# optimizer = torch.optim.Adam(rnn.parameters(), lr=learning_rate, weight_decay=weight_decay)
# loss_function = F.cross_entropy
# loss_function = F.nll_loss
# loss_function = nn.CrossEntropyLoss()
# loss_function = nn.BCELoss()
# loss_function = [FocalLoss(alpha=1 / w[0]) for w in train_weights]
loss_function = [torch.nn.CrossEntropyLoss(torch.Tensor(w).to(device), reduction='mean')
for w in weights]
# 按照以下的过程进行参数的训练
for epoch in range(epoch_num):
avg_loss = 0
sum_loss = 0
rnn.train()
y_true_task = {}
y_pred_task = {}
y_pred_task_score = {}
for index, tmp in enumerate(dataset_train):
tmp_compound, tmp_y, tmp_smi = tmp
optimizer.zero_grad()
outputs = rnn(tmp_compound.to(device))
loss = 0
# tmp_y = F.one_hot(tmp_y, 2).float().to(device)
# print(label_one_hot)
# aa = tmp_y.type(torch.FloatTensor).to(device)
# bb = outputs
# print(outputs.flatten())
# out_label = F.softmax(outputs, dim=1)
# pred = out_label.data.max(1, keepdim=True)[1].view(-1).cpu().numpy()
# pred_score = [x[tmp_y.cpu().detach().numpy()[i]] for i,x in enumerate(out_label.cpu().detach().numpy())]
# y_pred.extend(pred)
# y_pred_score.extend(pred_score)
for i in range(len(tasks)):
validId = np.where((tmp_y[:,i].cpu().numpy() == 0) | (tmp_y[:,i].cpu().numpy() == 1))[0]
if len(validId) == 0:
continue
y_pred = outputs[:,i * 2:(i + 1) * 2][torch.tensor(validId).to(device)]
y_label = tmp_y[:,i][torch.tensor(validId).to(device)]
# y_pred = torch.sigmoid(y_pred).view(-1)
# y_label = F.one_hot(y_label, 2).float().to(device)
loss += loss_function[i](y_pred.to(device), y_label.long().to(device))
pred_lable = F.softmax(y_pred.detach().cpu(), dim=-1)[:, 1].view(-1).numpy()
# pred_lable = np.zeros_like(y_pred.cpu().detach().numpy(), dtype=int)
# pred_lable[np.where(np.asarray(y_pred.cpu().detach().numpy()) > 0.5)] = 1
try:
y_true_task[i].extend(y_label.cpu().numpy())
y_pred_task[i].extend(pred_lable)
# y_pred_task_score[i].extend(y_pred)
except:
y_true_task[i] = []
y_pred_task[i] = []
# y_pred_task_score[i] = []
y_true_task[i].extend(y_label.cpu().numpy())
y_pred_task[i].extend(pred_lable)
# y_pred_task_score[i].extend(y_pred.cpu().detach().numpy())
# flood = (loss - b).abs() + b
loss.backward()
optimizer.step()
sum_loss += loss
# print("epoch:", epoch, "index: ", index,"loss:", loss.item())
avg_loss = sum_loss / (index + 1)
# cm = [metrics.confusion_matrix(y_true_task[i], y_pred_task[i]) for i in range(len(tasks))]
trn_roc = [metrics.roc_auc_score(y_true_task[i], y_pred_task[i]) for i in range(len(tasks))]
trn_prc = [metrics.auc(precision_recall_curve(y_true_task[i], y_pred_task[i])[1],
precision_recall_curve(y_true_task[i], y_pred_task[i])[0]) for i in range(len(tasks))]
# acc = [metrics.accuracy_score(y_true_task[i], y_pred_task[i]) for i in range(len(tasks))]
# recall = [metrics.recall_score(y_true_task[i], y_pred_task[i]) for i in range(len(tasks))]
# specificity = [cm[i][0, 0] / (cm[i][0, 0] + cm[i][0, 1]) for i in range(len(tasks))]
print("epoch:", epoch," train " "avg_loss:", avg_loss.item(),
# "acc: ", np.array(acc).mean(),
# "recall: ", np.array(recall).mean(),
# "specificity: ", np.array(specificity).mean(),
# " train_auc: ",trn_roc,
" train_auc: ", np.array(trn_roc).mean(),
# " train_pr: ", trn_prc,
" train_pr: ", np.array(trn_prc).mean())
with torch.no_grad():
rnn.eval()
val_avg_loss = 0
val_sum_loss = 0
y_true_task = {}
y_pred_task = {}
y_pred_task_score = {}
for index, tmp in enumerate(dataset_val):
tmp_compound, tmp_y, tmp_smi = tmp
loss = 0
outputs = rnn(tmp_compound)
# out_label = F.softmax(outputs, dim=1)
# pred = out_label.data.max(1, keepdim=True)[1].view(-1).cpu().numpy()
# pred_score = [x[tmp_y.cpu().detach().numpy()[i]] for i, x in enumerate(out_label.cpu().detach().numpy())]
# y_pred.extend(pred)
# y_pred_score.extend(pred_score)
for i in range(len(tasks)):
validId = np.where((tmp_y[:, i].cpu().numpy() == 0) | (tmp_y[:, i].cpu().numpy() == 1))[0]
if len(validId) == 0:
continue
y_pred = outputs[:, i * 2:(i + 1) * 2][torch.tensor(validId).to(device)]
y_label = tmp_y[:, i][torch.tensor(validId).to(device)]
# y_pred = torch.sigmoid(y_pred).view(-1)
# y_label = F.one_hot(y_label, 2).float().to(device)
loss += loss_function[i](y_pred.to(device), y_label.long().to(device))
pred_lable = F.softmax(y_pred.detach().cpu(), dim=-1)[:, 1].view(-1).numpy()
# pred_lable = np.zeros_like(y_pred.cpu().detach().numpy(), dtype=int)
# pred_lable[np.where(np.asarray(y_pred.cpu().detach().numpy()) > 0.5)] = 1
try:
y_true_task[i].extend(y_label.cpu().numpy())
y_pred_task[i].extend(pred_lable)
# y_pred_task_score[i].extend(y_pred)
except:
y_true_task[i] = []
y_pred_task[i] = []
# y_pred_task_score[i] = []
y_true_task[i].extend(y_label.cpu().numpy())
y_pred_task[i].extend(pred_lable)
# y_pred_task_score[i].extend(y_pred.cpu().detach().numpy())
val_sum_loss += loss.item()
val_avg_loss = val_sum_loss / (index + 1)
# cm = [metrics.confusion_matrix(y_true_task[i], y_pred_task[i]) for i in range(len(tasks))]
trn_roc = [metrics.roc_auc_score(y_true_task[i], y_pred_task[i]) for i in range(len(tasks))]
trn_prc = [metrics.auc(precision_recall_curve(y_true_task[i], y_pred_task[i])[1],
precision_recall_curve(y_true_task[i], y_pred_task[i])[0]) for i in
range(len(tasks))]
# acc = [metrics.accuracy_score(y_true_task[i], y_pred_task[i]) for i in range(len(tasks))]
# recall = [metrics.recall_score(y_true_task[i], y_pred_task[i]) for i in range(len(tasks))]
# specificity = [cm[i][0, 0] / (cm[i][0, 0] + cm[i][0, 1]) for i in range(len(tasks))]
print("epoch:", epoch, " val avg_loss:", val_avg_loss,
# "acc: ", np.array(acc).mean(),
# "recall: ", np.array(recall).mean(),
# "specificity: ", np.array(specificity).mean(),
"val_auc: ", np.array(trn_roc).mean(),
"val_pr: ", np.array(trn_prc).mean())
# 保存模型
if val_avg_loss < test_best_loss:
test_best_loss = val_avg_loss
PATH = 'muv/lstm_net.pth'
print("test save model")
torch.save(rnn.state_dict(), PATH)
with torch.no_grad():
rnn.eval()
test_sum_loss = []
y_true_task = {}
y_pred_task = {}
y_pred_task_score = {}
for index, tmp in enumerate(dataset_test):
tmp_compound, tmp_y, tmp_smi = tmp
loss = 0
outputs = rnn(tmp_compound)
# out_label = F.softmax(outputs, dim=1)
# pred = out_label.data.max(1, keepdim=True)[1].view(-1).cpu().numpy()
# pred_score = [x[tmp_y.cpu().detach().numpy()[i]] for i, x in enumerate(out_label.cpu().detach().numpy())]
# y_pred.extend(pred)
# y_pred_score.extend(pred_score)
for i in range(len(tasks)):
validId = np.where((tmp_y[:, i].cpu().numpy() == 0) | (tmp_y[:, i].cpu().numpy() == 1))[0]
if len(validId) == 0:
continue
y_pred = outputs[:, i * 2:(i + 1) * 2][torch.tensor(validId)].to(device)
y_label = tmp_y[:, i][torch.tensor(validId)].long().to(device)
# y_pred = torch.sigmoid(y_pred).view(-1)
# y_label = F.one_hot(y_label, 2).float().to(device)
loss += loss_function[i](y_pred, y_label)
y_pred_s = F.softmax(y_pred.detach().cpu(), dim=-1)[:, 1].view(-1).numpy()
pred_lable = np.zeros_like(y_pred_s, dtype=int)
pred_lable[np.where(np.asarray(y_pred_s) > 0.5)] = 1
try:
y_true_task[i].extend(y_label.cpu().numpy())
y_pred_task[i].extend(pred_lable)
y_pred_task_score[i].extend(y_pred_s)
except:
y_true_task[i] = []
y_pred_task[i] = []
y_pred_task_score[i] = []
y_true_task[i].extend(y_label.cpu().numpy())
y_pred_task[i].extend(pred_lable)
y_pred_task_score[i].extend(y_pred_s)
test_sum_loss.append(loss.cpu().detach().numpy())
trn_roc = [metrics.roc_auc_score(y_true_task[i], y_pred_task_score[i]) for i in range(len(tasks))]
trn_prc = [metrics.auc(precision_recall_curve(y_true_task[i], y_pred_task_score[i])[1],
precision_recall_curve(y_true_task[i], y_pred_task_score[i])[0]) for i in
range(len(tasks))]
# print(len(trn_roc))
# print(sum(y_true_task[0]))
# print(sum(y_pred_task[0]))
acc = [metrics.accuracy_score(y_true_task[i], y_pred_task[i]) for i in range(len(tasks))]
# recall = [metrics.recall_score(y_true_task[i], y_pred_task[i]) for i in range(tasks_num)]
# specificity = [cm[i][0, 0] / (cm[i][0, 0] + cm[i][0, 1]) for i in range(tasks_num)]
print("epoch:", epoch, " test " "avg_loss:", np.array(test_sum_loss).mean(),
"acc: ", np.array(acc).mean(),
# "recall: ", np.array(recall).mean(),
# "specificity: ", np.array(specificity).mean(),
# " test_auc: ", trn_roc,
" test_auc: ", np.array(trn_roc).mean(),
# " test_pr: ", trn_prc,
" test_pr: ", np.array(trn_prc).mean())
| Python |
3D | lvqiujie/Mol2Context-vec | tasks/sider/sider_train.py | .py | 12,877 | 262 | import os
import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.nn.functional as F
import torch.utils.data as data
import pandas as pd
from sklearn.externals import joblib
import numpy as np
import random
from sklearn import metrics
from sklearn.metrics import precision_recall_curve
import matplotlib.pyplot as plt
from tasks.utils.util import *
from tasks.utils.model import *
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if __name__ == '__main__':
# 设置超参数
input_size = 512
hidden_size = 512 # 定义超参数rnn的循环神经元个数，个数为32个
learning_rate = 0.001 # 定义超参数学习率
epoch_num = 200
batch_size = 32
best_loss = 10000
test_best_loss = 10000
weight_decay = 1e-5
momentum = 0.9
# b = 0.6
seed = 199
torch.manual_seed(seed)
random.seed(seed)
np.random.seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
y = joblib.load('sider/label.pkl')
all_smi = joblib.load('sider/smi.pkl')
x = joblib.load('sider/sider_embed.pkl')
print("data len is ", x.shape[0])
tasks_num = 27
# 5-Fold
train_split_x, train_split_y, train_split_smi, \
val_split_x, val_split_y, val_split_smi, \
test_split_x, test_split_y, test_split_smi, weights = split_multi_label(x, y, all_smi, 1, 'sider')
data_train = MyDataset(train_split_x, train_split_y, train_split_smi)
dataset_train = data.DataLoader(dataset=data_train, batch_size=batch_size, shuffle=True)
data_val = MyDataset(val_split_x, val_split_y, val_split_smi)
dataset_val = data.DataLoader(dataset=data_val, batch_size=batch_size, shuffle=True)
data_test = MyDataset(test_split_x, test_split_y, test_split_smi)
dataset_test = data.DataLoader(dataset=data_test, batch_size=batch_size, shuffle=True)
rnn = LSTM(tasks_num, task_type="muti", input_size=600).to(device)
# 设置优化器和损失函数
#使用adam优化器进行优化，输入待优化参数rnn.parameters，优化学习率为learning_rate
# optimizer = torch.optim.SGD(rnn.parameters(), lr=learning_rate, weight_decay = weight_decay,
# momentum = momentum)
optimizer = torch.optim.Adam(rnn.parameters(), lr=learning_rate, weight_decay=weight_decay)
# optimizer = torch.optim.RMSprop(rnn.parameters(), lr=learning_rate, weight_decay = weight_decay)
# loss_function = F.cross_entropy
# loss_function = F.nll_loss
# loss_function = nn.CrossEntropyLoss()
loss_function = [nn.CrossEntropyLoss(torch.Tensor(weight).to(device), reduction='mean') for weight in weights]
# loss_function = nn.BCELoss()
# loss_function = nn.BCEWithLogitsLoss()
# loss_function = FocalLoss(alpha=1 / train_weights[0])
# 按照以下的过程进行参数的训练
for epoch in range(epoch_num):
sum_loss = []
rnn.train()
y_true_task = {}
y_pred_task = {}
y_pred_task_score = {}
for index, tmp in enumerate(dataset_train):
tmp_compound, tmp_y, tmp_smi = tmp
optimizer.zero_grad()
outputs = rnn(tmp_compound.to(device))
loss = 0
for i in range(tasks_num):
validId = np.where((tmp_y[:,i].cpu().numpy() == 0) | (tmp_y[:,i].cpu().numpy() == 1))[0]
if len(validId) == 0:
continue
# print(outputs.shape)
y_pred = outputs[:, i * 2:(i + 1) * 2][torch.tensor(validId)].to(device)
y_label = tmp_y[:,i][torch.tensor(validId)].long().to(device)
# y_pred = torch.sigmoid(y_pred).view(-1)
# y_label = F.one_hot(y_label, 2).float().to(device)
loss += loss_function[i](y_pred, y_label)
pred_lable = F.softmax(y_pred.detach().cpu(), dim=-1)[:, 1].view(-1).numpy()
# pred_lable = np.zeros_like(y_pred.cpu().detach().numpy(), dtype=int)
# pred_lable[np.where(np.asarray(y_pred.cpu().detach().numpy()) > 0.5)] = 1
try:
y_true_task[i].extend(y_label.cpu().numpy())
y_pred_task[i].extend(pred_lable)
# y_pred_task_score[i].extend(y_pred)
except:
y_true_task[i] = []
y_pred_task[i] = []
# y_pred_task_score[i] = []
y_true_task[i].extend(y_label.cpu().numpy())
y_pred_task[i].extend(pred_lable)
# y_pred_task_score[i].extend(y_pred.cpu().detach().numpy())
# loss = (loss - b).abs() + b
loss.backward()
optimizer.step()
sum_loss.append(loss.cpu().detach().numpy())
# print("epoch:", epoch, "index: ", index,"loss:", loss.item())
# acc = [metrics.accuracy_score(y_true_task[i], y_pred_task[i]) for i in range(tasks_num)]
# recall = [metrics.recall_score(y_true_task[i], y_pred_task[i]) for i in range(tasks_num)]
# specificity = [cm[i][0, 0] / (cm[i][0, 0] + cm[i][0, 1]) for i in range(tasks_num)]
print("epoch:", epoch," train " "avg_loss:", np.array(sum_loss).mean())
with torch.no_grad():
rnn.eval()
val_sum_loss = []
y_true_task = {}
y_pred_task = {}
y_pred_task_score = {}
for index, tmp in enumerate(dataset_val):
tmp_compound, tmp_y, tmp_smi = tmp
loss = 0
outputs = rnn(tmp_compound)
# out_label = F.softmax(outputs, dim=1)
# pred = out_label.data.max(1, keepdim=True)[1].view(-1).cpu().numpy()
# pred_score = [x[tmp_y.cpu().detach().numpy()[i]] for i, x in enumerate(out_label.cpu().detach().numpy())]
# y_pred.extend(pred)
# y_pred_score.extend(pred_score)
for i in range(tasks_num):
validId = np.where((tmp_y[:, i].cpu().numpy() == 0) | (tmp_y[:, i].cpu().numpy() == 1))[0]
if len(validId) == 0:
continue
y_pred = outputs[:, i * 2:(i + 1) * 2][torch.tensor(validId)].to(device)
y_label = tmp_y[:, i][torch.tensor(validId)].long().to(device)
# y_pred = torch.sigmoid(y_pred).view(-1)
# y_label = F.one_hot(y_label, 2).float().to(device)
loss += loss_function[i](y_pred, y_label)
pred_lable = F.softmax(y_pred.detach().cpu(), dim=-1)[:, 1].view(-1).numpy()
# pred_lable = np.zeros_like(y_pred.cpu().detach().numpy(), dtype=int)
# pred_lable[np.where(np.asarray(y_pred.cpu().detach().numpy()) > 0.5)] = 1
try:
y_true_task[i].extend(y_label.cpu().numpy())
y_pred_task[i].extend(pred_lable)
# y_pred_task_score[i].extend(y_pred)
except:
y_true_task[i] = []
y_pred_task[i] = []
# y_pred_task_score[i] = []
y_true_task[i].extend(y_label.cpu().numpy())
y_pred_task[i].extend(pred_lable)
# y_pred_task_score[i].extend(y_pred.cpu().detach().numpy())
val_sum_loss.append(loss.cpu().detach().numpy())
val_avg_loss = np.array(val_sum_loss).mean()
trn_roc = [metrics.roc_auc_score(y_true_task[i], y_pred_task[i]) for i in range(tasks_num)]
trn_prc = [metrics.auc(precision_recall_curve(y_true_task[i], y_pred_task[i])[1],
precision_recall_curve(y_true_task[i], y_pred_task[i])[0]) for i in
range(tasks_num)]
# acc = [metrics.accuracy_score(y_true_task[i], y_pred_task[i]) for i in range(tasks_num)]
# recall = [metrics.recall_score(y_true_task[i], y_pred_task[i]) for i in range(tasks_num)]
# specificity = [cm[i][0, 0] / (cm[i][0, 0] + cm[i][0, 1]) for i in range(tasks_num)]
print("epoch:", epoch, " val " "avg_loss:", val_avg_loss,
# "acc: ", np.array(acc).mean(),
# "recall: ", np.array(recall).mean(),
# "specificity: ", np.array(specificity).mean(),
# " val_auc: ", trn_roc,
" val_auc: ", np.array(trn_roc).mean(),
# " val_pr: ", trn_prc,
" val_pr: ", np.array(trn_prc).mean())
# 保存模型
if val_avg_loss < test_best_loss:
test_best_loss = val_avg_loss
PATH = 'sider/lstm_net.pth'
print("test save model")
torch.save(rnn.state_dict(), PATH)
with torch.no_grad():
rnn.eval()
test_sum_loss = []
y_true_task = {}
y_pred_task = {}
y_pred_task_score = {}
for index, tmp in enumerate(dataset_test):
tmp_compound, tmp_y, tmp_smi = tmp
loss = 0
outputs = rnn(tmp_compound)
# out_label = F.softmax(outputs, dim=1)
# pred = out_label.data.max(1, keepdim=True)[1].view(-1).cpu().numpy()
# pred_score = [x[tmp_y.cpu().detach().numpy()[i]] for i, x in enumerate(out_label.cpu().detach().numpy())]
# y_pred.extend(pred)
# y_pred_score.extend(pred_score)
for i in range(tasks_num):
validId = np.where((tmp_y[:, i].cpu().numpy() == 0) | (tmp_y[:, i].cpu().numpy() == 1))[0]
if len(validId) == 0:
continue
y_pred = outputs[:, i * 2:(i + 1) * 2][torch.tensor(validId)].to(device)
y_label = tmp_y[:, i][torch.tensor(validId)].long().to(device)
# y_pred = torch.sigmoid(y_pred).view(-1)
# y_label = F.one_hot(y_label, 2).float().to(device)
loss += loss_function[i](y_pred, y_label)
y_pred_s = F.softmax(y_pred.detach().cpu(), dim=-1)[:, 1].view(-1).numpy()
pred_lable = np.zeros_like(y_pred_s, dtype=int)
pred_lable[np.where(np.asarray(y_pred_s) > 0.5)] = 1
try:
y_true_task[i].extend(y_label.cpu().numpy())
y_pred_task[i].extend(pred_lable)
y_pred_task_score[i].extend(y_pred_s)
except:
y_true_task[i] = []
y_pred_task[i] = []
y_pred_task_score[i] = []
y_true_task[i].extend(y_label.cpu().numpy())
y_pred_task[i].extend(pred_lable)
y_pred_task_score[i].extend(y_pred_s)
test_sum_loss.append(loss.cpu().detach().numpy())
trn_roc = [metrics.roc_auc_score(y_true_task[i], y_pred_task_score[i]) for i in range(tasks_num)]
trn_prc = [metrics.auc(precision_recall_curve(y_true_task[i], y_pred_task_score[i])[1],
precision_recall_curve(y_true_task[i], y_pred_task_score[i])[0]) for i in
range(tasks_num)]
# print(len(trn_roc))
# print(sum(y_true_task[0]))
# print(sum(y_pred_task[0]))
acc = [metrics.accuracy_score(y_true_task[i], y_pred_task[i]) for i in range(tasks_num)]
# recall = [metrics.recall_score(y_true_task[i], y_pred_task[i]) for i in range(tasks_num)]
# specificity = [cm[i][0, 0] / (cm[i][0, 0] + cm[i][0, 1]) for i in range(tasks_num)]
print("epoch:", epoch, " test " "avg_loss:", np.array(test_sum_loss).mean(),
"acc: ", np.array(acc).mean(),
# "recall: ", np.array(recall).mean(),
# "specificity: ", np.array(specificity).mean(),
# " test_auc: ", trn_roc,
" test_auc: ", np.array(trn_roc).mean(),
# " test_pr: ", trn_prc,
" test_pr: ", np.array(trn_prc).mean())
| Python |
3D | lvqiujie/Mol2Context-vec | tasks/sider/get_sider_data.py | .py | 5,522 | 179 | import pandas as pd
import numpy as np
from rdkit import Chem
import os
from rdkit.Chem import Descriptors
from sklearn.externals import joblib
# step 1
filepath="sider/sider.csv"
df = pd.read_csv(filepath, header=0, encoding="gbk")
w_file = open("sider/sider.smi", mode='w', encoding="utf-8")
all_label = []
all_smi = []
for line in df.values:
# aa = np.array(line[:17], dtype = np.float64)
# a =np.isnan(aa)
smi = line[27].strip()
all_label.append(line[0:27])
all_smi.append(smi)
w_file.write(smi + "\n")
# mol = Chem.MolFromSmiles(smi)
# try:
# if 12 <= Descriptors.MolWt(mol) <= 600:
# if -5 <= Descriptors.MolLogP(mol) <= 7:
# flag = True
# for t in mol.GetAtoms():
# if t.GetSymbol() not in ["H", "B", "C", "N", "O", "F", "P", "S", "Cl", "Br", "I", "Si"]:
# flag = False
# print(t.GetSymbol())
# break
# if flag:
# all_label.append(line[0:27])
# all_smi.append(smi)
# w_file.write(smi + "\n")
# except:
# print("error "+smi)
w_file.close()
# step 2
adb = "mol2vec corpus -i sider/sider.smi -o sider/sider.cp -r 1 -j 4 --uncommon UNK --threshold 3"
d = os.popen(adb)
f = d.read()
print(f)
# step 3
vocab_path = "data/datasets/my_smi/smi_tran.vocab"
vocab = {line.split()[0]: int(line.split()[1]) for line in open(vocab_path).readlines()}
sentence_maxlen = 80
w_file = open("sider/sider_tran.cp_UNK", mode='w', encoding="utf-8")
label = []
smi = []
index = -1
mols_path = "sider/sider.cp_UNK"
mols_file = open(mols_path, mode='r',encoding="utf-8")
while True:
line = mols_file.readline().strip()
index += 1
if "None".__eq__(line.strip()) or "UNK".__eq__(line.strip()):
continue
if not line:
break
token_ids = np.zeros((sentence_maxlen,), dtype=np.int64)
# Add begin of sentence index
token_ids[0] = vocab['<bos>']
for j, token in enumerate(line.split()[:sentence_maxlen - 2]):
# print(token)
if token.lower() in vocab:
token_ids[j + 1] = vocab[token.lower()]
else:
token_ids[j + 1] = vocab['<unk>']
# Add end of sentence index
if token_ids[1]:
token_ids[j + 2] = vocab['<eos>']
# print(token_ids)
label.append(all_label[index])
smi.append(all_smi[index])
w_file.write(" ".join(str(i) for i in token_ids).strip()+"\n")
w_file.close()
joblib.dump(label, 'sider/label.pkl')
joblib.dump(smi, 'sider/smi.pkl')
# step 4
import os
import keras.backend as K
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
from data import DATA_SET_DIR
from context_vec.smi_generator import SMIDataGenerator
from context_vec.smi_model import context_vec
import tensorflow as tf
from tensorflow import keras
from sklearn.externals import joblib
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
keras.backend.set_session(sess)
parameters = {
'multi_processing': False,
'n_threads': 4,
'cuDNN': True if len(K.tensorflow_backend._get_available_gpus()) else False,
'test_dataset': 'sider/sider_tran.cp_UNK',
'vocab': 'my_smi/smi_tran.vocab',
'model_dir': "smi_context_vec_512",
'vocab_flag': False,
'uncommon_threshold': 3,
# 'vocab_size': 28914,
# 'vocab_size': 748,
'vocab_size': 13576,
'num_sampled': 100,
# 'charset_size': 262,
'sentence_maxlen': 80,
'token_maxlen': 50,
'token_encoding': 'word',
'epochs': 1000,
'patience': 2,
'batch_size': 512,
'test_batch_size': 512,
'clip_value': 1,
'cell_clip': 5,
'proj_clip': 5,
'lr': 0.2,
'shuffle': False,
'n_lstm_layers': 2,
'n_highway_layers': 2,
'cnn_filters': [[1, 32],
[2, 32],
[3, 64],
[4, 128],
[5, 256],
[6, 512],
[7, 512]
],
'lstm_units_size': 512,
'hidden_units_size': 300,
'char_embedding_size': 16,
'dropout_rate': 0.1,
'word_dropout_rate': 0.05,
'weight_tying': True,
}
test_generator = SMIDataGenerator(parameters['test_dataset'],
os.path.join(DATA_SET_DIR, parameters['vocab']),
sentence_maxlen=parameters['sentence_maxlen'],
token_maxlen=parameters['token_maxlen'],
batch_size=parameters['test_batch_size'],
shuffle=parameters['shuffle'],
token_encoding=parameters['token_encoding'])
# Compile context_vec
context_vec_model = context_vec(parameters)
context_vec_model.compile_context_vec()
# context_vec_model.load(sampled_softmax=False)
#
# # Evaluate Bidirectional Language Model
# context_vec_model.evaluate(test_generator, parameters['test_batch_size'])
#
# # Build context_vec meta-model to deploy for production and persist in disk
# context_vec_model.wrap_multi_context_vec_encoder(print_summary=True)
# Load context_vec encoder
context_vec_model.load_context_vec_encoder()
# Get context_vec embeddings to feed as inputs for downstream tasks
context_vec_embeddings = context_vec_model.get_outputs(test_generator, output_type='word', state='all')
print(context_vec_embeddings.shape)
# 保存x
joblib.dump(context_vec_embeddings, 'sider/sider_embed.pkl')
| Python |
3D | lvqiujie/Mol2Context-vec | tasks/BBBP/get_BBBP_data.py | .py | 4,709 | 162 | import sys
sys.path.append('./')
import pandas as pd
import joblib
import numpy as np
import os
# step 1
filepath="BBBP/BBBP.csv"
df = pd.read_csv(filepath, header=0, encoding="gbk")
w_file = open("BBBP/BBBP.smi", mode='w', encoding="utf-8")
all_label = []
all_smi = []
for line in df.values:
smi = line[1]
if len(smi) <= 0:
continue
# aa = np.array(line[:17], dtype = np.float64)
# a =np.isnan(aa)
all_label.append(line[0])
all_smi.append(smi)
w_file.write(smi+"\n")
w_file.close()
# step 2
adb = "mol2vec corpus -i BBBP/BBBP.smi -o BBBP/BBBP.cp -r 1 -j 4 --uncommon UNK --threshold 3"
d = os.popen(adb)
f = d.read()
print(f)
# step 3
vocab_path = "data/datasets/my_smi/smi_tran.vocab"
vocab = {line.split()[0]: int(line.split()[1]) for line in open(vocab_path).readlines()}
sentence_maxlen = 80
w_file = open("BBBP/BBBP_tran.cp_UNK", mode='w', encoding="utf-8")
label = []
smi = []
index = -1
mols_path = "BBBP/BBBP.cp_UNK"
mols_file = open(mols_path, mode='r',encoding="utf-8")
while True:
line = mols_file.readline().strip()
index += 1
if "None".__eq__(line.strip()) or "UNK".__eq__(line.strip()):
continue
if not line:
break
token_ids = np.zeros((sentence_maxlen,), dtype=np.int64)
# Add begin of sentence index
token_ids[0] = vocab['<bos>']
for j, token in enumerate(line.split()[:sentence_maxlen - 2]):
# print(token)
if token.lower() in vocab:
token_ids[j + 1] = vocab[token.lower()]
else:
token_ids[j + 1] = vocab['<unk>']
# Add end of sentence index
if token_ids[1]:
token_ids[j + 2] = vocab['<eos>']
# print(token_ids)
label.append(all_label[index])
smi.append(all_smi[index])
w_file.write(" ".join(str(i) for i in token_ids).strip()+"\n")
w_file.close()
joblib.dump(label, 'BBBP/label.pkl')
joblib.dump(smi, 'BBBP/smi.pkl')
# step 4
import keras.backend as K
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
from context_vec.smi_generator import SMIDataGenerator
from context_vec.smi_model import Context_vec
import tensorflow as tf
from tensorflow import keras
import joblib
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
keras.backend.set_session(sess)
parameters = {
'multi_processing': False,
'n_threads': 4,
'cuDNN': True if len(K.tensorflow_backend._get_available_gpus()) else False,
'test_dataset': 'BBBP/BBBP_tran.cp_UNK',
'vocab': 'my_smi/smi_tran.vocab',
'model_dir': "smi_context_vec_300_0",
'vocab_flag': False,
'uncommon_threshold': 3,
# 'vocab_size': 28914,
# 'vocab_size': 748,
'vocab_size': 121,
'num_sampled': 100,
# 'charset_size': 262,
'sentence_maxlen': 80,
'token_maxlen': 50,
'token_encoding': 'word',
'epochs': 1000,
'patience': 2,
'batch_size': 512,
'test_batch_size': 512,
'clip_value': 1,
'cell_clip': 5,
'proj_clip': 5,
'lr': 0.2,
'shuffle': False,
'n_lstm_layers': 2,
'n_highway_layers': 2,
'cnn_filters': [[1, 32],
[2, 32],
[3, 64],
[4, 128],
[5, 256],
[6, 512],
[7, 512]
],
'lstm_units_size': 300,
'hidden_units_size': 150,
'char_embedding_size': 16,
'dropout_rate': 0.1,
'word_dropout_rate': 0.05,
'weight_tying': True,
}
test_generator = SMIDataGenerator(parameters['test_dataset'],
os.path.join("data/datasets", parameters['vocab']),
sentence_maxlen=parameters['sentence_maxlen'],
token_maxlen=parameters['token_maxlen'],
batch_size=parameters['test_batch_size'],
shuffle=parameters['shuffle'],
token_encoding=parameters['token_encoding'])
# Compile
context_vec_model = Context_vec(parameters)
context_vec_model.compile_context_vec()
# context_vec_model.load(sampled_softmax=False)
#
# # Evaluate Bidirectional Language Model
# context_vec_model.evaluate(test_generator, parameters['test_batch_size'])
#
# # Build meta-model to deploy for production and persist in disk
# context_vec_model.wrap_multi_context_vec_encoder(print_summary=True)
# Load encoder
context_vec_model.load_context_vec_encoder()
# Get embeddings to feed as inputs for downstream tasks
context_vec_embeddings = context_vec_model.get_outputs(test_generator, output_type='word', state='all')
print(context_vec_embeddings.shape)
# 保存x
joblib.dump(context_vec_embeddings, 'BBBP/BBBP_embed.pkl')
| Python |
3D | lvqiujie/Mol2Context-vec | tasks/BBBP/__init__.py | .py | 0 | 0 | null | Python |
3D | lvqiujie/Mol2Context-vec | tasks/BBBP/BBBP_train.py | .py | 9,523 | 215 | import os
import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.nn.functional as F
import torch.utils.data as data
import pandas as pd
from sklearn.externals import joblib
import numpy as np
import math
import random
from sklearn import metrics
from sklearn.metrics import precision_recall_curve
import matplotlib.pyplot as plt
from sklearn.model_selection import KFold
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
from tasks.utils.util import *
from tasks.utils.model import *
if __name__ == '__main__':
# 设置超参数
input_size = 512
hidden_size = 512 # 定义超参数rnn的循环神经元个数，个数为32个
learning_rate = 0.01 # 定义超参数学习率
epoch_num = 2000
batch_size = 512
best_loss = 10000
test_best_loss = 10000
weight_decay = 1e-5
momentum = 0.9
b = 0.3
y = joblib.load('BBBP/label.pkl')
x = joblib.load('BBBP/BBBP_embed.pkl')
all_smi = joblib.load('BBBP/smi.pkl')
print("data len is ",x.shape[0])
seed = 188
torch.manual_seed(seed)
random.seed(seed)
np.random.seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# 5-Fold
# 5-Fold
train_split_x, train_split_y, train_split_smi, \
val_split_x, val_split_y, val_split_smi, \
test_split_x, test_split_y, test_split_smi, weights = split_data(x, y, all_smi, 4, "BBBP")
data_train = MyDataset(train_split_x, train_split_y, train_split_smi)
dataset_train = data.DataLoader(dataset=data_train, batch_size=batch_size, shuffle=True)
data_val = MyDataset(val_split_x, val_split_y, val_split_smi)
dataset_val = data.DataLoader(dataset=data_val, batch_size=batch_size, shuffle=True)
data_test = MyDataset(test_split_x, test_split_y, test_split_smi)
dataset_test = data.DataLoader(dataset=data_test, batch_size=batch_size, shuffle=True)
rnn = LSTM(1, task_type="sing", input_size=300, att=True).to(device)
# 设置优化器和损失函数
#使用adam优化器进行优化，输入待优化参数rnn.parameters，优化学习率为learning_rate
optimizer = torch.optim.SGD(rnn.parameters(), lr=learning_rate, weight_decay = weight_decay,
momentum = momentum)
# optimizer = torch.optim.Adam(rnn.parameters(), lr=learning_rate, weight_decay = weight_decay)
# loss_function = F.cross_entropy
# loss_function = F.nll_loss
# loss_function = nn.CrossEntropyLoss()
# loss_function = nn.BCELoss()
loss_function = nn.BCEWithLogitsLoss().to(device)
# loss_function = [FocalLoss(alpha=1 / w[0]) for w in train_weights]
# loss_function = [torch.nn.CrossEntropyLoss(torch.Tensor(w).to(device), reduction='mean')
# for w in train_weights]
# 按照以下的过程进行参数的训练
for epoch in range(epoch_num):
avg_loss = 0
sum_loss = 0
rnn.train()
y_true = []
y_pred = []
y_pred_score = []
for index, tmp in enumerate(dataset_train):
tmp_compound, tmp_y, tmp_smi = tmp
optimizer.zero_grad()
outputs = rnn(tmp_compound.to(device))
# tmp_y = F.one_hot(tmp_y, 2).float().to(device)
# print(label_one_hot)
# aa = tmp_y.type(torch.FloatTensor).to(device)
# bb = outputs
# print(outputs.flatten())
y_true.extend(tmp_y.cpu().numpy())
# out_label = F.softmax(outputs, dim=1)
# pred = out_label.data.max(1, keepdim=True)[1].view(-1).cpu().numpy()
# pred_score = [x[tmp_y.cpu().detach().numpy()[i]] for i,x in enumerate(out_label.cpu().detach().numpy())]
# y_pred.extend(pred)
# y_pred_score.extend(pred_score)
# tmp_y = F.one_hot(tmp_y, 2).float().to(device)
loss = loss_function(outputs.to(device), tmp_y.float().to(device))
outputs = torch.sigmoid(outputs).view(-1)
pred = np.zeros_like(outputs.cpu().detach().numpy(), dtype=int)
pred[np.where(np.asarray(outputs.cpu().detach().numpy()) > 0.5)] = 1
y_pred.extend(pred)
y_pred_score.extend(outputs.cpu().detach().numpy())
# flood = (loss - b).abs() + b
loss.backward()
optimizer.step()
sum_loss += loss
# print("epoch:", epoch, "index: ", index,"loss:", loss.item())
avg_loss = sum_loss / (index + 1)
cm = metrics.confusion_matrix(y_true, y_pred)
print("epoch:", epoch," train " "avg_loss:", avg_loss.item(),
"acc: ", metrics.accuracy_score(y_true, y_pred),
# "recall: ", metrics.recall_score(y_true, y_pred),
# "specificity: ", cm[0, 0] / (cm[0, 0] + cm[0, 1]),
" train_auc: ", metrics.roc_auc_score(y_true, y_pred_score))
# # 保存模型
# if avg_loss < best_loss:
# best_loss = avg_loss
# PATH = 'BBBP/lstm_net.pth'
# print("train save model")
# torch.save(rnn.state_dict(), PATH)
# 测试集部分换机器后再调试
with torch.no_grad():
rnn.eval()
test_avg_loss = 0
test_sum_loss = 0
y_true = []
y_pred = []
y_pred_score = []
for index, tmp in enumerate(dataset_val):
tmp_compound, tmp_y, tmp_sm = tmp
y_true.extend(tmp_y.cpu().numpy())
outputs = rnn(tmp_compound)
# out_label = F.softmax(outputs, dim=1)
# pred = out_label.data.max(1, keepdim=True)[1].view(-1).cpu().numpy()
# pred_score = [x[tmp_y.cpu().detach().numpy()[i]] for i, x in enumerate(out_label.cpu().detach().numpy())]
# y_pred.extend(pred)
# y_pred_score.extend(pred_score)
outputs = torch.sigmoid(outputs).view(-1)
# tmp_y = F.one_hot(tmp_y, 2).float().to(device)
loss = loss_function(outputs, tmp_y.float().to(device))
# print(outputs.flatten())
pred = np.zeros_like(outputs.cpu().detach().numpy(), dtype=int)
pred[np.where(np.asarray(outputs.cpu().detach().numpy()) > 0.5)] = 1
y_pred.extend(pred)
y_pred_score.extend(outputs.cpu().detach().numpy())
test_sum_loss += loss.item()
test_avg_loss = test_sum_loss / (index + 1)
cm = metrics.confusion_matrix(y_true, y_pred)
print("epoch:", epoch," val ", "avg_loss: ", test_avg_loss,
# "acc: ", metrics.accuracy_score(y_true, y_pred),
# "recall: ", metrics.recall_score(y_true, y_pred),
# "specificity: ", cm[0,0]/(cm[0,0]+cm[0,1]),
# "sensitivity: ", cm[1,1]/(cm[1,0]+cm[1,1]),
" test_auc: ", metrics.roc_auc_score(y_true, y_pred_score))
# 保存模型
if test_avg_loss < test_best_loss:
test_best_loss = test_avg_loss
PATH = 'BBBP/lstm_net.pth'
print("test save model")
torch.save(rnn.state_dict(), PATH)
rnn.eval()
test_avg_loss = 0
test_sum_loss = 0
y_true = []
y_pred = []
y_pred_score = []
for index, tmp in enumerate(dataset_test):
tmp_compound, tmp_y, tmp_smi = tmp
loss = 0
y_true.extend(tmp_y.cpu().numpy())
outputs = rnn(tmp_compound)
# out_label = F.softmax(outputs, dim=1)
# pred = out_label.data.max(1, keepdim=True)[1].view(-1).cpu().numpy()
# pred_score = [x[tmp_y.cpu().detach().numpy()[i]] for i, x in enumerate(out_label.cpu().detach().numpy())]
# y_pred.extend(pred)
# y_pred_score.extend(pred_score)
outputs = torch.sigmoid(outputs).view(-1)
# tmp_y = F.one_hot(tmp_y, 2).float().to(device)
loss = loss_function(outputs, tmp_y.float().to(device))
# print(outputs.flatten())
pred = np.zeros_like(outputs.cpu().detach().numpy(), dtype=int)
pred[np.where(np.asarray(outputs.cpu().detach().numpy()) > 0.5)] = 1
y_pred.extend(pred)
y_pred_score.extend(outputs.cpu().detach().numpy())
test_sum_loss += loss.item()
test_avg_loss = test_sum_loss / (index + 1)
cm = metrics.confusion_matrix(y_true, y_pred)
trn_prc = metrics.auc(precision_recall_curve(y_true, y_pred_score)[1],
precision_recall_curve(y_true, y_pred_score)[0])
print("epoch:", epoch, " test ", "avg_loss: ", test_avg_loss,
"acc: ", metrics.accuracy_score(y_true, y_pred),
# "recall: ", metrics.recall_score(y_true, y_pred),
# "specificity: ", cm[0, 0] / (cm[0, 0] + cm[0, 1]),
# "sensitivity: ", cm[1,1]/(cm[1,0]+cm[1,1]),
" test_auc: ", metrics.roc_auc_score(y_true, y_pred_score),
" test_pr: ", np.array(trn_prc).mean()) | Python |
3D | lvqiujie/Mol2Context-vec | context_vec/smi_model.py | .py | 20,741 | 414 | import sys
sys.path.append('./')
import os
import time
import gc
import numpy as np
from keras import backend as K
from keras.callbacks import ModelCheckpoint, EarlyStopping
from keras.layers import Dense, Input, SpatialDropout1D
from keras.layers import LSTM, CuDNNLSTM, Activation
from keras.layers import Lambda, Embedding, Conv2D, GlobalMaxPool1D
from keras.layers import add, concatenate
from keras.layers.wrappers import TimeDistributed
from keras.models import Model, load_model
from keras.optimizers import Adagrad
from keras.constraints import MinMaxNorm
from keras.utils import to_categorical
import shutil
# from data import MODELS_DIR
MODELS_DIR = 'context_vec/models'
from context_vec.custom_layers import TimestepDropout, Camouflage, Highway, SampledSoftmax
class Context_vec(object):
def __init__(self, parameters):
self._model = None
self._context_vec_model = None
self.parameters = parameters
self.model_dir = parameters['model_dir']
if not os.path.exists(os.path.join(MODELS_DIR, self.model_dir)):
os.mkdir(os.path.join(MODELS_DIR, self.model_dir))
self.compile_context_vec()
def __del__(self):
K.clear_session()
del self._model
def char_level_token_encoder(self):
charset_size = self.parameters['charset_size']
char_embedding_size = self.parameters['char_embedding_size']
token_embedding_size = self.parameters['hidden_units_size']
n_highway_layers = self.parameters['n_highway_layers']
filters = self.parameters['cnn_filters']
token_maxlen = self.parameters['token_maxlen']
# Input Layer, word characters (samples, words, character_indices)
inputs = Input(shape=(None, token_maxlen,), dtype='int32')
# Embed characters (samples, words, characters, character embedding)
embeds = Embedding(input_dim=charset_size, output_dim=char_embedding_size)(inputs)
token_embeds = []
# Apply multi-filter 2D convolutions + 1D MaxPooling + tanh
for (window_size, filters_size) in filters:
convs = Conv2D(filters=filters_size, kernel_size=[window_size, char_embedding_size], strides=(1, 1),
padding="same")(embeds)
convs = TimeDistributed(GlobalMaxPool1D())(convs)
convs = Activation('tanh')(convs)
convs = Camouflage(mask_value=0)(inputs=[convs, inputs])
token_embeds.append(convs)
token_embeds = concatenate(token_embeds)
# Apply highways networks
for i in range(n_highway_layers):
token_embeds = TimeDistributed(Highway())(token_embeds)
token_embeds = Camouflage(mask_value=0)(inputs=[token_embeds, inputs])
# Project to token embedding dimensionality
token_embeds = TimeDistributed(Dense(units=token_embedding_size, activation='linear'))(token_embeds)
token_embeds = Camouflage(mask_value=0)(inputs=[token_embeds, inputs])
token_encoder = Model(inputs=inputs, outputs=token_embeds, name='token_encoding')
return token_encoder
def compile_context_vec(self, print_summary=False):
"""
Compiles a Language Model RNN based on the given parameters
"""
if self.parameters['token_encoding'] == 'word':
# Train word embeddings from scratch
word_inputs = Input(shape=(None,), name='word_indices', dtype='int32')
embeddings = Embedding(self.parameters['vocab_size'], self.parameters['hidden_units_size'], trainable=True, name='token_encoding')
inputs = embeddings(word_inputs)
# Token embeddings for Input
drop_inputs = SpatialDropout1D(self.parameters['dropout_rate'])(inputs)
lstm_inputs = TimestepDropout(self.parameters['word_dropout_rate'])(drop_inputs)
# Pass outputs as inputs to apply sampled softmax
next_ids = Input(shape=(None, 1), name='next_ids', dtype='float32')
previous_ids = Input(shape=(None, 1), name='previous_ids', dtype='float32')
elif self.parameters['token_encoding'] == 'char':
# Train character-level representation
word_inputs = Input(shape=(None, self.parameters['token_maxlen'],), dtype='int32', name='char_indices')
inputs = self.char_level_token_encoder()(word_inputs)
# Token embeddings for Input
drop_inputs = SpatialDropout1D(self.parameters['dropout_rate'])(inputs)
lstm_inputs = TimestepDropout(self.parameters['word_dropout_rate'])(drop_inputs)
# Pass outputs as inputs to apply sampled softmax
next_ids = Input(shape=(None, 1), name='next_ids', dtype='float32')
previous_ids = Input(shape=(None, 1), name='previous_ids', dtype='float32')
# Reversed input for backward LSTMs
re_lstm_inputs = Lambda(function=Context_vec.reverse)(lstm_inputs)
mask = Lambda(function=Context_vec.reverse)(drop_inputs)
# Forward LSTMs
for i in range(self.parameters['n_lstm_layers']):
lstm = LSTM(units=self.parameters['lstm_units_size'], return_sequences=True, activation="tanh",
recurrent_activation='sigmoid',
kernel_constraint=MinMaxNorm(-1 * self.parameters['cell_clip'],
self.parameters['cell_clip']),
recurrent_constraint=MinMaxNorm(-1 * self.parameters['cell_clip'],
self.parameters['cell_clip'])
)(lstm_inputs)
lstm = Camouflage(mask_value=0)(inputs=[lstm, drop_inputs])
# Projection to hidden_units_size
proj = TimeDistributed(Dense(self.parameters['hidden_units_size'], activation='linear',
kernel_constraint=MinMaxNorm(-1 * self.parameters['proj_clip'],
self.parameters['proj_clip'])
))(lstm)
# Merge Bi-LSTMs feature vectors with the previous ones
lstm_inputs = add([proj, lstm_inputs], name='f_block_{}'.format(i + 1))
# Apply variational drop-out between BI-LSTM layers
lstm_inputs = SpatialDropout1D(self.parameters['dropout_rate'])(lstm_inputs)
# Backward LSTMs
for i in range(self.parameters['n_lstm_layers']):
re_lstm = LSTM(units=self.parameters['lstm_units_size'], return_sequences=True, activation='tanh',
recurrent_activation='sigmoid',
kernel_constraint=MinMaxNorm(-1 * self.parameters['cell_clip'],
self.parameters['cell_clip']),
recurrent_constraint=MinMaxNorm(-1 * self.parameters['cell_clip'],
self.parameters['cell_clip'])
)(re_lstm_inputs)
re_lstm = Camouflage(mask_value=0)(inputs=[re_lstm, mask])
# Projection to hidden_units_size
re_proj = TimeDistributed(Dense(self.parameters['hidden_units_size'], activation='linear',
kernel_constraint=MinMaxNorm(-1 * self.parameters['proj_clip'],
self.parameters['proj_clip'])
))(re_lstm)
# Merge Bi-LSTMs feature vectors with the previous ones
re_lstm_inputs = add([re_proj, re_lstm_inputs], name='b_block_{}'.format(i + 1))
# Apply variational drop-out between BI-LSTM layers
re_lstm_inputs = SpatialDropout1D(self.parameters['dropout_rate'])(re_lstm_inputs)
# Reverse backward LSTMs' outputs = Make it forward again
re_lstm_inputs = Lambda(function=Context_vec.reverse, name="reverse")(re_lstm_inputs)
# Project to Vocabulary with Sampled Softmax
sampled_softmax = SampledSoftmax(num_classes=self.parameters['vocab_size'],
num_sampled=int(self.parameters['num_sampled']),
tied_to=embeddings if self.parameters['weight_tying']
and self.parameters['token_encoding'] == 'word' else None)
outputs = sampled_softmax([lstm_inputs, next_ids])
re_outputs = sampled_softmax([re_lstm_inputs, previous_ids])
self._model = Model(inputs=[word_inputs, next_ids, previous_ids],
outputs=[outputs, re_outputs])
self._model.compile(optimizer=Adagrad(lr=self.parameters['lr'], clipvalue=self.parameters['clip_value']),
loss=None)
if print_summary:
self._model.summary()
def train(self, train_data, valid_data):
# Add callbacks (early stopping, model checkpoint)
weights_file = os.path.join(MODELS_DIR, self.model_dir, "context_vec_best_weights_{epoch:03d}_{val_loss:.2f}.hdf5")
save_best_model = ModelCheckpoint(filepath=weights_file, monitor='val_loss', verbose=1,
save_best_only=False, mode='auto')
# early_stopping = EarlyStopping(patience=self.parameters['patience'], restore_best_weights=True)
t_start = time.time()
# Fit Model
self._model.fit_generator(train_data,
validation_data=valid_data,
epochs=self.parameters['epochs'],
workers=self.parameters['n_threads']
if self.parameters['n_threads'] else os.cpu_count(),
use_multiprocessing=True
if self.parameters['multi_processing'] else False,
callbacks=[save_best_model])
print('Training took {0} sec'.format(str(time.time() - t_start)))
def evaluate(self, test_data, batch_size):
# 查找完整句子
def unpad(x, y_true, y_pred):
y_true_unpad = []
y_pred_unpad = []
for i, x_i in enumerate(x):
for j, x_ij in enumerate(x_i):
if x_ij == 0:
y_true_unpad.append(y_true[i][:j])
y_pred_unpad.append(y_pred[i][:j])
break
return np.asarray(y_true_unpad), np.asarray(y_pred_unpad)
# # Generate samples
# x, y_true_forward, y_true_backward = [], [], []
# for i in range(len(test_data)):
# test_batch = test_data[i][0]
# x.extend(test_batch[0])
# y_true_forward.extend(test_batch[1])
# y_true_backward.extend(test_batch[2])
# x = np.asarray(x)
# y_true_forward = np.asarray(y_true_forward)
# y_true_backward = np.asarray(y_true_backward)
# Generate samples
# x, y_true_forward, y_true_backward = [], [], []
# y_pred_forward, y_pred_backward = [], []
# for i in range(len(test_data)):
for i in range(488):
test_batch = test_data[i][0]
# Predict outputs
y_pred_forward_tmp, y_pred_backward_tmp = self._model.predict([test_batch[0], test_batch[1], test_batch[2]])
# Unpad sequences
y_true_forward, y_pred_forward = unpad(test_batch[0], test_batch[1], y_pred_forward_tmp)
y_true_backward, y_pred_backward = unpad(test_batch[0], test_batch[2], y_pred_backward_tmp)
# Compute and print perplexity
# print('{}， Forward Langauge Model Perplexity: {}'.format(i, context_vec.perplexity(y_pred_forward, y_true_forward)))
# print('{}， Backward Langauge Model Perplexity: {}'.format(i, context_vec.perplexity(y_pred_backward, y_true_backward)))
print('{}， avg {}'.format(i,
(Context_vec.perplexity(y_pred_backward, y_true_backward) + Context_vec.perplexity(y_pred_forward, y_true_forward)) / 2
))
del test_batch, y_true_backward, y_pred_backward, \
y_pred_forward_tmp, y_pred_backward_tmp, y_true_forward, y_pred_forward
gc.collect()
# x.extend(test_batch[0])
# y_true_forward.extend(test_batch[1])
# y_true_backward.extend(test_batch[2])
#
# y_pred_forward.extend(y_pred_forward_tmp)
# y_pred_backward.extend(y_pred_backward_tmp)
#
# x = np.asarray(x)
# y_true_forward = np.asarray(y_true_forward)
# y_true_backward = np.asarray(y_true_backward)
#
# y_pred_forward = np.asarray(y_pred_forward)
# y_pred_backward = np.asarray(y_pred_backward)
# # Unpad sequences
# y_true_forward, y_pred_forward = unpad(x, y_true_forward, y_pred_forward)
# y_true_backward, y_pred_backward = unpad(x, y_true_backward, y_pred_backward)
def wrap_multi_context_vec_encoder(self, print_summary=False, save=False):
"""
Wrap context_vec meta-model encoder, which returns an array of the 3 intermediate context_vec outputs
:param print_summary: print a summary of the new architecture
:param save: persist model
:return: None
"""
context_vec_embeddings = list()
context_vec_embeddings.append(concatenate([self._model.get_layer('token_encoding').output, self._model.get_layer('token_encoding').output],
name='context_vec_embeddings_level_0'))
for i in range(self.parameters['n_lstm_layers']):
context_vec_embeddings.append(concatenate([self._model.get_layer('f_block_{}'.format(i + 1)).output,
Lambda(function=Context_vec.reverse)
(self._model.get_layer('b_block_{}'.format(i + 1)).output)],
name='context_vec_embeddings_level_{}'.format(i + 1)))
camos = list()
for i, context_vec_embedding in enumerate(context_vec_embeddings):
camos.append(Camouflage(mask_value=0.0, name='camo_context_vec_embeddings_level_{}'.format(i + 1))([context_vec_embedding,
self._model.get_layer(
'token_encoding').output]))
self._context_vec_model = Model(inputs=[self._model.get_layer('word_indices').input], outputs=camos)
if print_summary:
self._context_vec_model.summary()
if save:
self._context_vec_model.save(os.path.join(MODELS_DIR, self.model_dir, 'context_vec_Encoder.hd5'))
print('context_vec Encoder saved successfully')
def save(self, sampled_softmax=True, model_dir="model"):
"""
Persist model in disk
:param sampled_softmax: reload model using the full softmax function
:return: None
"""
if not sampled_softmax:
self.parameters['num_sampled'] = self.parameters['vocab_size']
self.compile_context_vec()
best_name = ""
min_loss = 100000
for file_name in os.listdir(os.path.join(MODELS_DIR, self.model_dir)):
if "0." in file_name:
tmp_loss = float(file_name.split("_")[-1].split(".")[-2])
if tmp_loss < min_loss:
min_loss = tmp_loss
best_name = file_name
shutil.copyfile(os.path.join(MODELS_DIR, self.model_dir, best_name),
os.path.join(MODELS_DIR, self.model_dir, 'context_vec_best_weights.hdf5'))
print(" best model name is :" + best_name)
self._model.load_weights(os.path.join(MODELS_DIR, self.model_dir, 'context_vec_best_weights.hdf5'))
self._model.save(os.path.join(MODELS_DIR, model_dir, 'context_vec_LM_EVAL.hd5'))
print('context_vec Language Model saved successfully')
def load(self, sampled_softmax=False):
if not sampled_softmax:
self.parameters['num_sampled'] = self.parameters['vocab_size']
self.compile_context_vec()
self._model.load_weights(os.path.join(MODELS_DIR, self.model_dir, 'context_vec_best_weights.hdf5'))
# self._model = load_model(os.path.join(MODELS_DIR, self.model_dir, 'context_vec_Encoder.hd5'),
# custom_objects={'TimestepDropout': TimestepDropout,
# 'Camouflage': Camouflage})
def load_context_vec_encoder(self):
self._context_vec_model = load_model(os.path.join(MODELS_DIR, self.model_dir, 'context_vec_Encoder.hd5'),
custom_objects={'TimestepDropout': TimestepDropout,
'Camouflage': Camouflage})
def get_outputs(self, test_data, output_type='word', state='last'):
"""
Wrap context_vec meta-model encoder, which returns an array of the 3 intermediate context_vec outputs
:param test_data: data generator
:param output_type: "word" for word vectors or "sentence" for sentence vectors
:param state: 'last' for 2nd LSTMs outputs or 'mean' for mean-pooling over inputs, 1st LSTMs and 2nd LSTMs
:return: None
"""
# Generate samples
preds1 = []
# for i in range(len(test_data)):
for i in range(10):
x = test_data[i][0]
# print(x[0])
tmp = np.asarray(self._context_vec_model.predict(np.asarray(x[0]))).swapaxes(0,1)
preds1.extend(tmp)
del tmp
preds = np.array(preds1)
del preds1
if state == 'last':
context_vec_vectors = preds[:,-1,:,:]
elif state == 'all':
context_vec_vectors = preds
else:
context_vec_vectors = np.mean(preds, axis=1)
if output_type == 'word':
return context_vec_vectors
else:
return np.mean(context_vec_vectors, axis=2)
def get_outputs_Bylist(self, test_data, output_typeBy='word', state='last'):
"""
Wrap context_vec meta-model encoder, which returns an array of the 3 intermediate context_vec outputs
:param test_data: data generator
:param output_type: "word" for word vectors or "sentence" for sentence vectors
:param state: 'last' for 2nd LSTMs outputs or 'mean' for mean-pooling over inputs, 1st LSTMs and 2nd LSTMs
:return: None
"""
# Generate samples
preds = []
for i in range(len(test_data)):
x = test_data[i][0]
# print(x[0])
tmp = np.asarray(self._context_vec_model.predict(np.asarray(x[0]))).swapaxes(0,1)
preds.extend(tmp)
return preds
def get_predict(self, test_data, output_type='word', state='last'):
# x = []
# for i in range(len(test_data)):
# test_batch = test_data[0]
# x.extend(test_batch[0])
preds = np.asarray(self._context_vec_model.predict(np.asarray(test_data)))
if state == 'last':
context_vec_vectors = preds[-1]
else:
context_vec_vectors = np.mean(preds, axis=0)
if output_type == 'words':
return context_vec_vectors
else:
return np.mean(context_vec_vectors, axis=1)
@staticmethod
def reverse(inputs, axes=1):
return K.reverse(inputs, axes=axes)
@staticmethod
def perplexity(y_pred, y_true):
if len(y_pred) == 0 or len(y_true) == 0:
return -1
cross_entropies = []
for y_pred_seq, y_true_seq in zip(y_pred, y_true):
# Reshape targets to one-hot vectors
y_true_seq = to_categorical(y_true_seq, y_pred_seq.shape[-1])
# Compute cross_entropy for sentence words
cross_entropy = K.categorical_crossentropy(K.tf.convert_to_tensor(y_true_seq, dtype=K.tf.float32),
K.tf.convert_to_tensor(y_pred_seq, dtype=K.tf.float32))
cross_entropies.extend(cross_entropy.eval(session=K.get_session()))
# Compute mean cross_entropy and perplexity
cross_entropy = np.mean(np.asarray(cross_entropies), axis=-1)
return pow(2.0, cross_entropy)
| Python |
3D | lvqiujie/Mol2Context-vec | context_vec/train.py | .py | 5,463 | 143 | import os
import keras.backend as K
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
from context_vec.smi_generator import SMIDataGenerator
from context_vec.smi_model import Context_vec
import tensorflow as tf
from tensorflow import keras
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config = config)
keras.backend.set_session(sess)
DATA_SET_DIR = "./context_vec/data/"
my_smi = "my_smi_0"
parameters = {
'multi_processing': False,
'n_threads': 4,
'cuDNN': True if len(K.tensorflow_backend._get_available_gpus()) else False,
'train_dataset': my_smi+'/mols_train.cp_UNK',
'valid_dataset': my_smi+'/mols_val.cp_UNK',
'test_dataset': my_smi+'/mols_test.cp_UNK',
'vocab': my_smi+'/smi_tran.vocab',
'vocab_flag': False,
'model_dir': "smi_context_vec_0_300",
'uncommon_threshold': 3,
# 'vocab_size': 28914,
# 'vocab_size': 748,
# 'vocab_size': 121,
'vocab_size': 13576,
'num_sampled': 100,
# 'charset_size': 262,
'sentence_maxlen': 80,
'token_maxlen': 50,
'token_encoding': 'word',
'epochs': 50,
'patience': 2,
'batch_size': 512,
'test_batch_size': 512,
'clip_value': 1,
'cell_clip': 5,
'proj_clip': 5,
'lr': 0.2,
'shuffle': True,
'n_lstm_layers': 2,
'n_highway_layers': 2,
'cnn_filters': [[1, 32],
[2, 32],
[3, 64],
[4, 128],
[5, 256],
[6, 512],
[7, 512]
],
'lstm_units_size': 300,
'hidden_units_size': 300,
'char_embedding_size': 16,
'dropout_rate': 0.1,
'word_dropout_rate': 0.05,
'weight_tying': True,
}
def vocab_generator(corpus):
all_dict = {}
with open(corpus) as fp:
for i, line in enumerate(fp):
for j, token in enumerate(line.split()):
token = token.strip()
if token in all_dict:
all_dict[token] += 1
else:
all_dict[token] = 1
f = open(os.path.join(DATA_SET_DIR, my_smi+"/smi_tran.vocab"), 'w')
if "UNK" in all_dict:
all_dict.pop('UNK','404')
f.write('<pad> 0' + "\n")
f.write('<unk> 1' + "\n")
f.write('unk 1' + "\n")
f.write('<bos> 2' + "\n")
f.write('<eos> 3' + "\n")
index = 4
for k, v in all_dict.items():
if v <= parameters['uncommon_threshold']:
continue
f.write(k+' ' +str(index)+"\n")
index += 1
f.close()
print(parameters)
if not os.path.exists("context_vec/models/"+parameters['model_dir']):
os.mkdir("context_vec/models/"+parameters['model_dir'])
if parameters['vocab_flag']:
print("------------- vocab -------------")
vocab_generator(os.path.join(DATA_SET_DIR, my_smi+"/mols.cp_UNK"))
# Set-up Generators
train_generator = SMIDataGenerator(os.path.join(DATA_SET_DIR, parameters['train_dataset']),
os.path.join(DATA_SET_DIR, parameters['vocab']),
sentence_maxlen=parameters['sentence_maxlen'],
token_maxlen=parameters['token_maxlen'],
batch_size=parameters['batch_size'],
shuffle=parameters['shuffle'],
token_encoding=parameters['token_encoding'])
val_generator = SMIDataGenerator(os.path.join(DATA_SET_DIR, parameters['valid_dataset']),
os.path.join(DATA_SET_DIR, parameters['vocab']),
sentence_maxlen=parameters['sentence_maxlen'],
token_maxlen=parameters['token_maxlen'],
batch_size=parameters['batch_size'],
shuffle=parameters['shuffle'],
token_encoding=parameters['token_encoding'])
test_generator = SMIDataGenerator(os.path.join(DATA_SET_DIR, parameters['test_dataset']),
os.path.join(DATA_SET_DIR, parameters['vocab']),
sentence_maxlen=parameters['sentence_maxlen'],
token_maxlen=parameters['token_maxlen'],
batch_size=parameters['test_batch_size'],
shuffle=parameters['shuffle'],
token_encoding=parameters['token_encoding'])
# Compile context_vec
context_vec_model = Context_vec(parameters)
context_vec_model.compile_context_vec(print_summary=True)
# Train context_vec
# context_vec_model.train(train_data=train_generator, valid_data=val_generator)
# Persist context_vec Bidirectional Language Model in disk
context_vec_model.save(sampled_softmax=False, model_dir=parameters['model_dir'])
# Evaluate Bidirectional Language Model
context_vec_model.evaluate(test_generator, parameters['test_batch_size'])
# Build context_vec meta-model to deploy for production and persist in disk
context_vec_model.wrap_multi_context_vec_encoder(print_summary=True, save=True)
# Load context_vec encoder
context_vec_model.load_context_vec_encoder()
# Get context_vec embeddings to feed as inputs for downstream tasks
# context_vec_embeddings = context_vec_model.get_outputs(test_generator, output_type='word', state='mean')
# BUILD & TRAIN NEW KERAS MODEL FOR DOWNSTREAM TASK (E.G., TEXT CLASSIFICATION)
a = 1 | Python |
3D | lvqiujie/Mol2Context-vec | context_vec/__init__.py | .py | 0 | 0 | null | Python |
3D | lvqiujie/Mol2Context-vec | context_vec/smi_generator.py | .py | 6,425 | 140 | import numpy as np
import keras
class SMIDataGenerator(keras.utils.Sequence):
"""Generates data for Keras"""
def __len__(self):
"""Denotes the number of batches per epoch"""
return int(np.ceil(len(self.indices)/self.batch_size))
def __init__(self, corpus, vocab, sentence_maxlen=100, token_maxlen=50, batch_size=32, shuffle=True, token_encoding='word'):
"""Compiles a Language Model RNN based on the given parameters
:param corpus: filename of corpus
:param vocab: filename of vocabulary
:param sentence_maxlen: max size of sentence
:param token_maxlen: max size of token in characters
:param batch_size: number of steps at each batch
:param shuffle: True if shuffle at the end of each epoch
:param token_encoding: Encoding of token, either 'word' index or 'char' indices
:return: Nothing
"""
self.corpus = corpus
self.vocab = {line.split()[0]: int(line.split()[1]) for line in open(vocab).readlines()}
self.sent_ids = corpus
self.batch_size = batch_size
self.shuffle = shuffle
self.sentence_maxlen = sentence_maxlen
self.token_maxlen = token_maxlen
self.token_encoding = token_encoding
self.all_lines = open(corpus, mode='r', encoding="utf-8").readlines()
with open(self.corpus) as fp:
self.indices = np.arange(len(fp.readlines()))
# newlines = [index for index in range(0, len(self.indices), 2)]
# self.indices = np.delete(self.indices, newlines)
def __getitem__(self, index):
"""Generate one batch of data"""
# Generate indexes of the batch
batch_indices = self.indices[index * self.batch_size:(index + 1) * self.batch_size]
# Read sample sequences
word_indices_batch = np.zeros((len(batch_indices), self.sentence_maxlen), dtype=np.int32)
if self.token_encoding == 'char':
word_char_indices_batch = np.full((len(batch_indices), self.sentence_maxlen, self.token_maxlen), 260, dtype=np.int32)
for i, batch_id in enumerate(batch_indices):
# Read sentence (sample)
word_indices_batch[i] = self.all_lines[batch_id].split()
# word_indices_batch[i] = self.get_token_indices(sent_id=batch_id)
if self.token_encoding == 'char':
word_char_indices_batch[i] = self.get_token_char_indices(sent_id=batch_id)
# Build forward targets
for_word_indices_batch = np.zeros((len(batch_indices), self.sentence_maxlen), dtype=np.int32)
padding = np.zeros((1,), dtype=np.int32)
for i, word_seq in enumerate(word_indices_batch ):
for_word_indices_batch[i] = np.concatenate((word_seq[1:], padding), axis=0)
for_word_indices_batch = for_word_indices_batch[:, :, np.newaxis]
# Build backward targets
back_word_indices_batch = np.zeros((len(batch_indices), self.sentence_maxlen), dtype=np.int32)
for i, word_seq in enumerate(word_indices_batch):
back_word_indices_batch[i] = np.concatenate((padding, word_seq[:-1]), axis=0)
back_word_indices_batch = back_word_indices_batch[:, :, np.newaxis]
tmp = [word_indices_batch if 'word'.__eq__(self.token_encoding) else word_char_indices_batch, for_word_indices_batch, back_word_indices_batch]
return tmp, []
def on_epoch_end(self):
"""Updates indexes after each epoch"""
if self.shuffle:
np.random.shuffle(self.indices)
def get_token_indices(self, sent_id: int):
with open(self.corpus) as fp:
for i, line in enumerate(fp):
if i == sent_id:
token_ids = np.zeros((self.sentence_maxlen,), dtype=np.int64)
# Add begin of sentence index
token_ids[0] = self.vocab['<bos>']
for j, token in enumerate(line.split()[:self.sentence_maxlen - 2]):
# print(token)
if token.lower() in self.vocab:
token_ids[j + 1] = self.vocab[token.lower()]
else:
token_ids[j + 1] = self.vocab['<unk>']
# Add end of sentence index
if token_ids[1]:
token_ids[j + 2] = self.vocab['<eos>']
# print(token_ids)
return token_ids
def get_token_char_indices(self, sent_id: int):
def convert_token_to_char_ids(token, token_maxlen):
bos_char = 256 # <begin sentence>
eos_char = 257 # <end sentence>
bow_char = 258 # <begin word>
eow_char = 259 # <end word>
pad_char = 260 # <pad char>
char_indices = np.full([token_maxlen], pad_char, dtype=np.int32)
# Encode word to UTF-8 encoding
word_encoded = token.encode('utf-8', 'ignore')[:(token_maxlen - 2)]
# Set characters encodings
# Add begin of word char index
char_indices[0] = bow_char
if token == '<bos>':
char_indices[1] = bos_char
k = 1
elif token == '<eos>':
char_indices[1] = eos_char
k = 1
else:
# Add word char indices
for k, chr_id in enumerate(word_encoded, start=1):
char_indices[k] = chr_id + 1
# Add end of word char index
char_indices[k + 1] = eow_char
return char_indices
with open(self.corpus) as fp:
for i, line in enumerate(fp):
if i == sent_id:
token_ids = np.zeros((self.sentence_maxlen, self.token_maxlen), dtype=np.int32)
# Add begin of sentence char indices
token_ids[0] = convert_token_to_char_ids('<bos>', self.token_maxlen)
# Add tokens' char indices
for j, token in enumerate(line.split()[:self.sentence_maxlen - 2]):
token_ids[j + 1] = convert_token_to_char_ids(token, self.token_maxlen)
# Add end of sentence char indices
if token_ids[1]:
token_ids[j + 2] = convert_token_to_char_ids('<eos>', self.token_maxlen)
return token_ids
| Python |
3D | lvqiujie/Mol2Context-vec | context_vec/split.py | .py | 2,539 | 85 | import numpy as np
import os
import random
mols_src_all = []
all_dict = {}
with open("data/datasets/my_smi_0/mols.cp_UNK") as fp:
for i, line in enumerate(fp):
mols_src_all.append(line)
for j, token in enumerate(line.split()):
token = token.strip()
if token in all_dict:
all_dict[token] += 1
else:
all_dict[token] = 1
f = open("data/datasets/my_smi_0/smi_tran.vocab", 'w')
if "UNK" in all_dict:
all_dict.pop('UNK','404')
f.write('<pad> 0' + "\n")
f.write('<unk> 1' + "\n")
f.write('unk 1' + "\n")
f.write('<bos> 2' + "\n")
f.write('<eos> 3' + "\n")
index = 4
for k, v in all_dict.items():
if v <= 3:
continue
f.write(k+' ' +str(index)+"\n")
index += 1
f.close()
vocab_path = "data/datasets/my_smi_0/smi_tran.vocab"
vocab = {line.split()[0]: int(line.split()[1]) for line in open(vocab_path).readlines()}
w_file = open("data/datasets/my_smi_0/mols_tran.cp_UNK", mode='w', encoding="utf-8")
sentence_maxlen = 80
for line in mols_src_all:
if "None".__eq__(line.strip()) or "UNK".__eq__(line.strip()):
continue
if not line:
break
token_ids = np.zeros((sentence_maxlen,), dtype=np.int64)
# Add begin of sentence index
token_ids[0] = vocab['<bos>']
for j, token in enumerate(line.split()[:sentence_maxlen - 2]):
# print(token)
if token.lower() in vocab:
token_ids[j + 1] = vocab[token.lower()]
else:
token_ids[j + 1] = vocab['<unk>']
# Add end of sentence index
if token_ids[1]:
token_ids[j + 2] = vocab['<eos>']
w_file.write(" ".join(str(i) for i in token_ids).strip()+"\n")
w_file.close()
mols_path = "data/my_smi_0/mols_tran.cp_UNK"
mols_file = open(mols_path, mode='r',encoding="utf-8")
mols_all = mols_file.readlines()
all_num = len(mols_all)
random.shuffle(mols_all)
train_num = all_num * 0.8
w_file = open("./data/my_smi_0/mols_train.cp_UNK", mode='w', encoding="utf-8")
mols_train = mols_all[:int(train_num)]
for i in mols_train:
w_file.write(str(i).strip()+"\n")
w_file.close()
val_num = all_num * 0.9
w_file = open("./data/my_smi_0/mols_val.cp_UNK", mode='w', encoding="utf-8")
mols_train = mols_all[int(train_num):int(val_num)]
for i in mols_train:
w_file.write(str(i).strip()+"\n")
w_file.close()
w_file = open("./data/datasets/my_smi_0/mols_test.cp_UNK", mode='w', encoding="utf-8")
mols_train = mols_all[int(val_num):]
for i in mols_train:
w_file.write(str(i).strip()+"\n")
w_file.close()
| Python |
3D | lvqiujie/Mol2Context-vec | context_vec/custom_layers/__init__.py | .py | 141 | 4 | from .dropout import TimestepDropout
from .masking import Camouflage
from .highway import Highway
from .sampled_softmax import SampledSoftmax | Python |
3D | lvqiujie/Mol2Context-vec | context_vec/custom_layers/masking.py | .py | 1,345 | 40 | # -*- coding: utf-8 -*-
"""Core Keras layers.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from keras import backend as K
from keras.engine.base_layer import Layer
class Camouflage(Layer):
"""Masks a sequence by using a mask value to skip timesteps based on another sequence.
LSTM and Convolution layers may produce fake tensors for padding timesteps. We need
to eliminate those tensors by replicating their initial values presented in the second input.
inputs = Input()
lstms = LSTM(units=100, return_sequences=True)(inputs)
padded_lstms = Camouflage()([lstms, inputs])
...
"""
def __init__(self, mask_value=0., **kwargs):
super(Camouflage, self).__init__(**kwargs)
self.mask_value = mask_value
def call(self, inputs):
boolean_mask = K.any(K.not_equal(inputs[1], self.mask_value),
axis=-1, keepdims=True)
return inputs[0] * K.cast(boolean_mask, K.dtype(inputs[0]))
def get_config(self):
config = {'mask_value': self.mask_value}
base_config = super(Camouflage, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def compute_output_shape(self, input_shape):
return input_shape[0]
| Python |
3D | lvqiujie/Mol2Context-vec | context_vec/custom_layers/sampled_softmax.py | .py | 3,026 | 64 | from keras.layers import Layer
import keras.backend as K
class SampledSoftmax(Layer):
"""Sampled Softmax, a faster way to train a softmax classifier over a huge number of classes.
# Arguments
num_classes: number of classes
num_sampled: number of classes to be sampled at each batch
tied_to: layer to be tied with (e.g., Embedding layer)
kwargs:
# Input shape
2D tensor with shape: `(nb_samples, input_dim)`.
# Output shape
2D tensor with shape: `(nb_samples, input_dim)`.
# References
- [Tensorflow code](tf.nn.sampled_softmax_loss)
- [Sampled SoftMax](https://www.tensorflow.org/extras/candidate_sampling.pdf)
"""
def __init__(self, num_classes=50000, num_sampled=1000, tied_to=None, **kwargs):
super(SampledSoftmax, self).__init__(**kwargs)
self.num_sampled = num_sampled
self.num_classes = num_classes
self.tied_to = tied_to
self.sampled = (self.num_classes != self.num_sampled)
def build(self, input_shape):
if self.tied_to is None:
self.softmax_W = self.add_weight(shape=(self.num_classes, input_shape[0][-1]), name='W_soft', initializer='lecun_normal')
self.softmax_b = self.add_weight(shape=(self.num_classes,), name='b_soft', initializer='zeros')
self.built = True
def call(self, x, mask=None):
lstm_outputs, next_token_ids = x
def sampled_softmax(x):
lstm_outputs_batch, next_token_ids_batch = x
batch_losses = K.tf.nn.sampled_softmax_loss(
self.softmax_W if self.tied_to is None else self.tied_to.weights[0], self.softmax_b,
next_token_ids_batch, lstm_outputs_batch,
num_classes=self.num_classes,
num_sampled=self.num_sampled,
partition_strategy='div')
batch_losses = K.tf.reduce_mean(batch_losses)
return [batch_losses, batch_losses]
def softmax(x):
lstm_outputs_batch, next_token_ids_batch = x
logits = K.tf.matmul(lstm_outputs_batch,
K.tf.transpose(self.softmax_W) if self.tied_to is None else K.tf.transpose(self.tied_to.weights[0]))
logits = K.tf.nn.bias_add(logits, self.softmax_b)
batch_predictions = K.tf.nn.softmax(logits)
labels_one_hot = K.tf.one_hot(K.tf.cast(next_token_ids_batch, dtype=K.tf.int32), self.num_classes)
batch_losses = K.tf.nn.softmax_cross_entropy_with_logits(labels=labels_one_hot, logits=logits)
return [batch_losses, batch_predictions]
losses, predictions = K.tf.map_fn(sampled_softmax if self.sampled else softmax, [lstm_outputs, next_token_ids])
self.add_loss(0.5 * K.tf.reduce_mean(losses[0]))
return lstm_outputs if self.sampled else predictions
def compute_output_shape(self, input_shape):
return input_shape[0] if self.sampled else (input_shape[0][0], input_shape[0][1], self.num_classes)
| Python |
3D | lvqiujie/Mol2Context-vec | context_vec/custom_layers/dropout.py | .py | 985 | 38 | # -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from keras import backend as K
from keras.engine import InputSpec
from keras.layers import Dropout
class TimestepDropout(Dropout):
"""Word Dropout.
This version performs the same function as Dropout, however it drops
entire timesteps (e.g., words embeddings) instead of individual elements (features).
# Arguments
rate: float between 0 and 1. Fraction of the timesteps to drop.
# Input shape
3D tensor with shape:
`(samples, timesteps, channels)`
# Output shape
Same as input
# References
- N/A
"""
def __init__(self, rate, **kwargs):
super(TimestepDropout, self).__init__(rate, **kwargs)
self.input_spec = InputSpec(ndim=3)
def _get_noise_shape(self, inputs):
input_shape = K.shape(inputs)
noise_shape = (input_shape[0], input_shape[1], 1)
return noise_shape
| Python |
3D | lvqiujie/Mol2Context-vec | context_vec/custom_layers/highway.py | .py | 7,259 | 140 | from keras.layers.core import Layer
from keras import initializers, regularizers, constraints, activations
from keras.initializers import Constant
from keras import backend as K
class Highway(Layer):
"""Highway network, a natural extension of LSTMs to feedforward networks.
# Arguments
activation: Activation function to use
(see [activations](../activations.md)).
Default: no activation is applied
(ie. "linear" activation: `a(x) = x`).
transform_activation: Activation function to use
for the transform unit
(see [activations](../activations.md)).
Default: sigmoid (`sigmoid`).
If you pass `None`, no activation is applied
(ie. "linear" activation: `a(x) = x`).x
kernel_initializer: Initializer for the `kernel` weights matrix,
used for the linear transformation of the inputs
(see [initializers](../initializers.md)).
transform_initializer: Initializer for the `transform` weights matrix,
used for the linear transformation of the inputs
(see [initializers](../initializers.md)).
bias_initializer: Initializer for the bias vector
(see [initializers](../initializers.md)).
transform_bias_initializer: Initializer for the bias vector
(see [initializers](../initializers.md)).
Default: -2 constant.
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix
(see [regularizer](../regularizers.md)).
transform_regularizer: Regularizer function applied to
the `transform` weights matrix
(see [regularizer](../regularizers.md)).
bias_regularizer: Regularizer function applied to the bias vector
(see [regularizer](../regularizers.md)).
transform_bias_regularizer: Regularizer function applied to the transform bias vector
(see [regularizer](../regularizers.md)).
kernel_constraint: Constraint function applied to
the `kernel` weights matrix
(see [constraints](../constraints.md)).
bias_constraint: Constraint function applied to the bias vector
(see [constraints](../constraints.md)).
# Input shape
2D tensor with shape: `(nb_samples, input_dim)`.
# Output shape
2D tensor with shape: `(nb_samples, input_dim)`.
# References
- [Highway Networks](http://arxiv.org/pdf/1505.00387v2.pdf)
"""
def __init__(self,
activation='relu',
transform_activation='sigmoid',
kernel_initializer='glorot_uniform',
transform_initializer='glorot_uniform',
bias_initializer='zeros',
transform_bias_initializer=-2,
kernel_regularizer=None,
transform_regularizer=None,
bias_regularizer=None,
transform_bias_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
self.activation = activations.get(activation)
self.transform_activation = activations.get(transform_activation)
self.kernel_initializer = initializers.get(kernel_initializer)
self.transform_initializer = initializers.get(transform_initializer)
self.bias_initializer = initializers.get(bias_initializer)
if isinstance(transform_bias_initializer, int):
self.transform_bias_initializer = Constant(value=transform_bias_initializer)
else:
self.transform_bias_initializer = initializers.get(transform_bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.transform_regularizer = regularizers.get(transform_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.transform_bias_regularizer = regularizers.get(transform_bias_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
super(Highway, self).__init__(**kwargs)
def build(self, input_shape):
assert len(input_shape) == 2
input_dim = input_shape[-1]
self.W = self.add_weight(shape=(input_dim, input_dim),
name='{}_W'.format(self.name),
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.W_transform = self.add_weight(shape=(input_dim, input_dim),
name='{}_W_transform'.format(self.name),
initializer=self.transform_initializer,
regularizer=self.transform_regularizer,
constraint=self.kernel_constraint)
self.bias = self.add_weight(shape=(input_dim,),
name='{}_bias'.format(self.name),
initializer=self.bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
self.bias_transform = self.add_weight(shape=(input_dim,),
name='{}_bias_transform'.format(self.name),
initializer=self.transform_bias_initializer,
regularizer=self.transform_bias_regularizer)
self.built = True
def call(self, x, mask=None):
x_h = self.activation(K.dot(x, self.W) + self.bias)
x_trans = self.transform_activation(K.dot(x, self.W_transform) + self.bias_transform)
output = x_h * x_trans + (1 - x_trans) * x
return output
def get_config(self):
config = {'activation': activations.serialize(self.activation),
'transform_activation': activations.serialize(self.transform_activation),
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'transform_initializer': initializers.serialize(self.transform_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'transform_bias_initializer': initializers.serialize(self.transform_bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'transform_regularizer': regularizers.serialize(self.transform_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'transform_bias_regularizer': regularizers.serialize(self.transform_bias_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint)
}
base_config = super(Highway, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
| Python |
3D | czimaginginstitute/2024_czii_mlchallenge_notebooks | blob_detector_inference.ipynb | .ipynb | 17,911 | 448 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a blob detector that does a poor job but is an example of a fast running solution that processes all tomograms and generates a submission without training."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"vscode": {
"languageId": "plaintext"
}
},
"outputs": [],
"source": [
"# Install packages\n",
"\n",
"# !pip install copick git+https://github.com/copick/copick-utils.git scikit-image cupy-cuda12x torch torchvision tqdm matplotlib"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"vscode": {
"languageId": "plaintext"
}
},
"outputs": [],
"source": [
"# Make a copick project\n",
"\n",
"config_blob = \"\"\"{\n",
" \"name\": \"czii_cryoet_mlchallenge_2024\",\n",
" \"description\": \"2024 CZII CryoET ML Challenge training data.\",\n",
" \"version\": \"1.0.0\",\n",
"\n",
" \"pickable_objects\": [\n",
" {\n",
" \"name\": \"apo-ferritin\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"4V1W\",\n",
" \"label\": 1,\n",
" \"color\": [ 0, 117, 220, 128],\n",
" \"radius\": 60,\n",
" \"map_threshold\": 0.0418\n",
" },\n",
" {\n",
" \"name\": \"beta-amylase\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"1FA2\",\n",
" \"label\": 2,\n",
" \"color\": [153, 63, 0, 128],\n",
" \"radius\": 65,\n",
" \"map_threshold\": 0.035\n",
" },\n",
" {\n",
" \"name\": \"beta-galactosidase\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"6X1Q\",\n",
" \"label\": 3,\n",
" \"color\": [ 76, 0, 92, 128],\n",
" \"radius\": 90,\n",
" \"map_threshold\": 0.0578\n",
" },\n",
" {\n",
" \"name\": \"ribosome\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"6EK0\",\n",
" \"label\": 4,\n",
" \"color\": [ 0, 92, 49, 128],\n",
" \"radius\": 150,\n",
" \"map_threshold\": 0.0374\n",
" },\n",
" {\n",
" \"name\": \"thyroglobulin\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"6SCJ\",\n",
" \"label\": 5,\n",
" \"color\": [ 43, 206, 72, 128],\n",
" \"radius\": 130,\n",
" \"map_threshold\": 0.0278\n",
" },\n",
" {\n",
" \"name\": \"virus-like-particle\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"6N4V\", \n",
" \"label\": 6,\n",
" \"color\": [255, 204, 153, 128],\n",
" \"radius\": 135,\n",
" \"map_threshold\": 0.201\n",
" }\n",
" ],\n",
"\n",
" \"overlay_root\": \"/kaggle/working/test/overlay\",\n",
"\n",
" \"overlay_fs_args\": {\n",
" \"auto_mkdir\": true\n",
" },\n",
"\n",
" \"static_root\": \"/kaggle/input/czii-cryo-et-object-identification/test/static\"\n",
"}\"\"\"\n",
"\n",
"copick_config_path = \"/kaggle/working/copick.config\"\n",
"\n",
"with open(copick_config_path, \"w\") as f:\n",
" f.write(config_blob)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"vscode": {
"languageId": "plaintext"
}
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import torch\n",
"import torch.nn.functional as F\n",
"import numpy as np\n",
"from skimage.measure import regionprops\n",
"from skimage.morphology import ball\n",
"from skimage.segmentation import watershed\n",
"from tqdm import tqdm\n",
"import scipy.ndimage as ndi\n",
"import time\n",
"import csv\n",
"import os\n",
"import copick\n",
"import zarr\n",
"\n",
"DEVICE = 'cuda'\n",
"OUTPUT_CSV_PATH = \"submission.csv\"\n",
"tomo_type = 'denoised'\n",
"RESOLUTION_THRESHOLD = 16\n",
"\n",
"def gaussian_kernel(size, sigma):\n",
" \"\"\"Generate a 3D Gaussian kernel.\"\"\"\n",
" kernel = np.fromfunction(\n",
" lambda x, y, z: (1/ (2 * np.pi * sigma**2)) * \n",
" np.exp(- ((x - (size[0] - 1) / 2) ** 2 + \n",
" (y - (size[1] - 1) / 2) ** 2 + \n",
" (z - (size[2] - 1) / 2) ** 2) / (2 * sigma ** 2)),\n",
" size\n",
" )\n",
" return torch.tensor(kernel).float().unsqueeze(0).unsqueeze(0).to(DEVICE) # Add batch and channel dimensions\n",
"\n",
"def create_hessian_particle_mask(tomogram, sigma):\n",
" \"\"\"\n",
" Generate a binary mask for dark, blob-like particles in a cryo-ET tomogram\n",
" using Hessian-based filtering with PyTorch.\n",
"\n",
" Args:\n",
" tomogram (torch.Tensor): The input 3D tomogram (C, D, H, W).\n",
" sigma (float): The standard deviation for Gaussian smoothing.\n",
"\n",
" Returns:\n",
" torch.Tensor: Binary mask highlighting dark blob-like areas in the tomogram.\n",
" \"\"\"\n",
" kernel_size = (5, 5, 5)\n",
" gaussian_k = gaussian_kernel(kernel_size, sigma)\n",
" \n",
" tomogram_smoothed = F.conv3d(tomogram.unsqueeze(0).unsqueeze(0), gaussian_k, padding=2).squeeze()\n",
"\n",
" # Compute Hessian components\n",
" hessian_xx = F.conv3d(tomogram_smoothed.unsqueeze(0).unsqueeze(0), gaussian_k, padding=2)\n",
" hessian_yy = F.conv3d(tomogram_smoothed.unsqueeze(0).unsqueeze(0), gaussian_k, padding=2)\n",
" hessian_xy = F.conv3d(tomogram_smoothed.unsqueeze(0).unsqueeze(0), gaussian_k, padding=2)\n",
"\n",
" hessian_response = hessian_xx + hessian_yy + hessian_xy # Simplified combination\n",
" binary_mask = hessian_response < 0 # Adjust threshold based on your needs\n",
"\n",
" return binary_mask.squeeze().byte()\n",
"\n",
"def erode_dilate_mask(mask, radius):\n",
" \"\"\"\n",
" Perform binary erosion and dilation on a binary mask using a spherical structuring element.\n",
" \n",
" Args:\n",
" mask (torch.Tensor): Input binary mask\n",
" radius (int): Radius of the spherical structuring element\n",
" \n",
" Returns:\n",
" torch.Tensor: Dilated mask after erosion and dilation operations\n",
" \"\"\"\n",
" # Create a spherical structuring element\n",
" radius = int(radius) # Ensure radius is an integer\n",
" struct_elem = ball(radius)\n",
" struct_elem_tensor = torch.tensor(struct_elem, dtype=torch.float32, device=DEVICE).unsqueeze(0).unsqueeze(0)\n",
"\n",
" # Reshape mask for conv3d\n",
" mask_reshaped = mask.unsqueeze(0).unsqueeze(0).float() # Shape (1, 1, D, H, W)\n",
" \n",
" # Calculate padding size - ensure it's an integer\n",
" pad_size = int(radius // 2)\n",
" \n",
" # Debug: Print shapes\n",
" print(f\"Mask shape for erosion: {mask_reshaped.shape}\")\n",
" print(f\"Structuring element shape: {struct_elem_tensor.shape}\")\n",
" print(f\"Padding size: {pad_size}\")\n",
" \n",
" # Erosion: Use a negative structuring element for max pooling\n",
" # Convert padding to the expected format (left, right, top, bottom, front, back)\n",
" # Ensure all values are integers\n",
" pad_3d = (int(pad_size), int(pad_size), \n",
" int(pad_size), int(pad_size), \n",
" int(pad_size), int(pad_size))\n",
" \n",
" mask_padded = F.pad(mask_reshaped, pad_3d, mode='constant', value=1)\n",
" eroded = -F.conv3d(\n",
" -mask_padded,\n",
" struct_elem_tensor,\n",
" stride=1,\n",
" padding=0,\n",
" dilation=1,\n",
" groups=1\n",
" )\n",
" eroded = (eroded >= struct_elem_tensor.sum()).squeeze().byte()\n",
"\n",
" # Dilation\n",
" mask_padded = F.pad(eroded.unsqueeze(0).unsqueeze(0).float(), pad_3d, mode='constant', value=0)\n",
" dilated = F.conv3d(\n",
" mask_padded,\n",
" struct_elem_tensor,\n",
" stride=1,\n",
" padding=0,\n",
" dilation=1,\n",
" groups=1\n",
" )\n",
" dilated = (dilated > 0).squeeze().byte()\n",
" \n",
" return dilated\n",
"\n",
"def distance_transform(mask):\n",
" \"\"\"\n",
" Compute the distance transform using a simple distance transform approach.\n",
" \n",
" Args:\n",
" mask (torch.Tensor): Binary mask tensor\n",
" \n",
" Returns:\n",
" torch.Tensor: Distance transform result\n",
" \"\"\"\n",
" # Ensure mask is boolean, then convert to float for distance calculation\n",
" mask = mask.bool()\n",
" # Invert the mask (using logical not instead of bitwise not)\n",
" inverted_mask = (~mask).float()\n",
" \n",
" # Add batch and channel dimensions\n",
" inverted_mask = inverted_mask.unsqueeze(0).unsqueeze(0)\n",
" \n",
" # Create kernel on the correct device\n",
" kernel = torch.ones(1, 1, 3, 3, 3, device=DEVICE)\n",
" \n",
" # Compute distance transform using convolution\n",
" distance = F.conv3d(inverted_mask, kernel, padding=1)\n",
" \n",
" return distance.squeeze()\n",
"\n",
"def local_maxima(distance, radius):\n",
" \"\"\"\n",
" Detect local maxima in the distance transform.\n",
" \n",
" Args:\n",
" distance (torch.Tensor): Distance transform tensor\n",
" radius (int): Radius for local maxima detection\n",
" \n",
" Returns:\n",
" torch.Tensor: Binary mask of local maxima\n",
" \"\"\"\n",
" # Ensure radius is an integer\n",
" radius = int(radius)\n",
" \n",
" # Add batch dimension for max_pool3d\n",
" distance = distance.unsqueeze(0)\n",
" \n",
" # Create kernel size tuple (must be odd numbers)\n",
" kernel_size = (2 * radius + 1, 2 * radius + 1, 2 * radius + 1)\n",
" \n",
" # Compute local maxima\n",
" maxpool = F.max_pool3d(\n",
" distance,\n",
" kernel_size=kernel_size,\n",
" stride=1,\n",
" padding=radius\n",
" )\n",
" \n",
" # Compare with original distance to find local maxima\n",
" local_max = (distance == maxpool)\n",
" \n",
" return local_max.squeeze()\n",
"\n",
"def get_tomogram_data(run, voxel_spacing, radius):\n",
" \"\"\"\n",
" Get tomogram data at appropriate resolution based on particle radius.\n",
" \n",
" Args:\n",
" run: Run object\n",
" voxel_spacing (float): Base voxel spacing\n",
" radius (float): Particle radius\n",
" \n",
" Returns:\n",
" tuple: (tomogram tensor, effective_voxel_spacing, scale_factor)\n",
" \"\"\"\n",
" tomogram_wrapper = run.get_voxel_spacing(voxel_spacing).get_tomogram(tomo_type)\n",
" z = zarr.open(store=tomogram_wrapper.zarr(), path=\"/\", mode=\"r\")\n",
" \n",
" if radius <= RESOLUTION_THRESHOLD:\n",
" # Use highest resolution\n",
" tomogram = z['0'][:]\n",
" effective_voxel_spacing = voxel_spacing\n",
" scale_factor = 1\n",
" else:\n",
" # Use medium resolution\n",
" tomogram = z['1'][:]\n",
" effective_voxel_spacing = voxel_spacing * 2 # Scale factor is 2 for level 1\n",
" scale_factor = 2\n",
" \n",
" return torch.tensor(tomogram).to(DEVICE), effective_voxel_spacing, scale_factor\n",
"\n",
"def process_all_runs(root, session_id, user_id, voxel_spacing):\n",
" \"\"\"Process all runs and save results to CSV.\"\"\"\n",
" results = []\n",
" pick_id = 0\n",
" \n",
" for run in tqdm(root.runs):\n",
" start_time = time.time()\n",
" print(f\"\\nProcessing run: {run.meta.name}\")\n",
" \n",
" # Process each particle type separately since they might need different resolutions\n",
" for obj in root.pickable_objects:\n",
" if not obj.is_particle:\n",
" continue\n",
" \n",
" radius = obj.radius\n",
" print(f\"Processing {obj.name} with radius {radius}\")\n",
" \n",
" # Get appropriate resolution data\n",
" tomogram_tensor, effective_voxel_spacing, scale_factor = get_tomogram_data(\n",
" run, voxel_spacing, radius)\n",
"\n",
" print(f\"Using scale factor {scale_factor} (effective voxel spacing: {effective_voxel_spacing})\")\n",
"\n",
" # Create segmentation at appropriate scale\n",
" segmentation = create_hessian_particle_mask(tomogram_tensor, sigma=3)\n",
" \n",
" if torch.sum(segmentation) == 0:\n",
" print(f\"No particles detected in segmentation for {obj.name}\")\n",
" continue\n",
"\n",
" # Adjust radius for effective voxel spacing\n",
" scaled_radius = radius / effective_voxel_spacing\n",
"\n",
" # Erode and dilate the segmentation\n",
" dilated_mask = erode_dilate_mask(segmentation, scaled_radius)\n",
"\n",
" # Distance transform and local maxima detection\n",
" distance = distance_transform(dilated_mask)\n",
" local_max = local_maxima(distance, scaled_radius)\n",
"\n",
" # Convert tensors to numpy for watershed\n",
" local_max_np = local_max.cpu().numpy()\n",
" distance_np = distance.cpu().numpy()\n",
" dilated_mask_np = dilated_mask.cpu().numpy()\n",
"\n",
" # Watershed segmentation\n",
" markers, _ = ndi.label(local_max_np)\n",
" watershed_labels = watershed(-distance_np, markers, mask=dilated_mask_np)\n",
"\n",
" # Extract region properties and scale coordinates back to original space\n",
" centroids = []\n",
" for region in regionprops(watershed_labels):\n",
" # Scale the centroid coordinates back to original space\n",
" centroid = np.array(region.centroid) * scale_factor\n",
" centroids.append(centroid) # ZYX order\n",
"\n",
" # Save centroids as picks and add to results\n",
" if centroids:\n",
" pick_set = run.get_picks(obj.name)\n",
" if pick_set:\n",
" pick_set = pick_set[0]\n",
" else:\n",
" pick_set = run.new_picks(obj.name, session_id, user_id)\n",
" \n",
" for centroid in centroids:\n",
" # Convert from ZYX to XYZ order and apply voxel spacing\n",
" x = centroid[2] * voxel_spacing # Z -> X\n",
" y = centroid[1] * voxel_spacing # Y -> Y\n",
" z = centroid[0] * voxel_spacing # X -> Z\n",
" \n",
" # Add to results list\n",
" row = [pick_id, run.meta.name, obj.name, x, y, z]\n",
" results.append(row)\n",
" pick_id += 1\n",
" \n",
" # Store pick set\n",
" pick_set.points = [{'x': c[2] * voxel_spacing,\n",
" 'y': c[1] * voxel_spacing,\n",
" 'z': c[0] * voxel_spacing}\n",
" for c in centroids]\n",
" pick_set.store()\n",
" print(f\"Saved {len(centroids)} centroids for {obj.name}\")\n",
" else:\n",
" print(f\"No valid centroids found for {obj.name}\")\n",
"\n",
" # Print timing for this run\n",
" end_time = time.time()\n",
" print(f\"Run {run.meta.name} completed in {end_time - start_time:.2f} seconds\")\n",
"\n",
" print(f\"\\nTotal picks found: {len(results)}\")\n",
"\n",
" # Write results to CSV\n",
" with open(OUTPUT_CSV_PATH, mode='w', newline='') as file:\n",
" writer = csv.writer(file)\n",
" writer.writerow([\"id\", \"experiment\", \"particle_type\", \"x\", \"y\", \"z\"])\n",
" writer.writerows(results)\n",
" \n",
" print(f\"Results saved to {OUTPUT_CSV_PATH}\")\n",
" return results\n",
"\n",
"# Run the processing\n",
"root = copick.from_file(copick_config_path)\n",
"results = process_all_runs(\n",
" root=root,\n",
" session_id=\"0\",\n",
" user_id=\"blobDetector\",\n",
" voxel_spacing=10\n",
")"
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| Unknown |
3D | czimaginginstitute/2024_czii_mlchallenge_notebooks | SECURITY.md | .md | 193 | 4 | ## Reporting Security Issues
If you believe you have found a security issue, please responsibly disclose by contacting us at [security@chanzuckerberg.com](mailto:security@chanzuckerberg.com).
| Markdown |
3D | czimaginginstitute/2024_czii_mlchallenge_notebooks | overview.ipynb | .ipynb | 805,308 | 808 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting napari\n",
" Downloading napari-0.5.3-py3-none-any.whl.metadata (13 kB)\n",
"Collecting monai\n",
" Using cached monai-1.3.2-py3-none-any.whl.metadata (10 kB)\n",
"Collecting zarr\n",
" Downloading zarr-2.18.3-py3-none-any.whl.metadata (5.7 kB)\n",
"Requirement already satisfied: numpy<2 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (1.26.4)\n",
"Collecting appdirs>=1.4.4 (from napari)\n",
" Using cached appdirs-1.4.4-py2.py3-none-any.whl.metadata (9.0 kB)\n",
"Collecting app-model<0.3.0,>=0.2.8 (from napari)\n",
" Using cached app_model-0.2.8-py3-none-any.whl.metadata (3.7 kB)\n",
"Collecting cachey>=0.2.1 (from napari)\n",
" Using cached cachey-0.2.1-py3-none-any.whl.metadata (2.2 kB)\n",
"Requirement already satisfied: certifi>=2018.1.18 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from napari) (2024.8.30)\n",
"Collecting dask>=2021.10.0 (from dask[array]>=2021.10.0->napari)\n",
" Using cached dask-2024.8.2-py3-none-any.whl.metadata (3.7 kB)\n",
"Collecting imageio!=2.22.1,>=2.20 (from napari)\n",
" Using cached imageio-2.35.1-py3-none-any.whl.metadata (4.9 kB)\n",
"Requirement already satisfied: jsonschema>=3.2.0 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from napari) (4.23.0)\n",
"Collecting lazy-loader>=0.2 (from napari)\n",
" Using cached lazy_loader-0.4-py3-none-any.whl.metadata (7.6 kB)\n",
"Collecting magicgui>=0.7.0 (from napari)\n",
" Using cached magicgui-0.9.1-py3-none-any.whl.metadata (7.2 kB)\n",
"Collecting napari-console>=0.0.9 (from napari)\n",
" Using cached napari_console-0.0.9-py3-none-any.whl.metadata (3.5 kB)\n",
"Collecting napari-plugin-engine>=0.1.9 (from napari)\n",
" Using cached napari_plugin_engine-0.2.0-py3-none-any.whl.metadata (3.9 kB)\n",
"Collecting napari-svg>=0.1.8 (from napari)\n",
" Using cached napari_svg-0.2.0-py3-none-any.whl.metadata (3.6 kB)\n",
"Collecting npe2>=0.7.6 (from napari)\n",
" Using cached npe2-0.7.7-py3-none-any.whl.metadata (3.1 kB)\n",
"Collecting numpydoc>=0.9.2 (from napari)\n",
" Using cached numpydoc-1.8.0-py3-none-any.whl.metadata (4.3 kB)\n",
"Collecting pandas>=1.3.0 (from napari)\n",
" Using cached pandas-2.2.2-cp310-cp310-macosx_10_9_x86_64.whl.metadata (19 kB)\n",
"Collecting Pillow>=9.0 (from napari)\n",
" Using cached pillow-10.4.0-cp310-cp310-macosx_10_10_x86_64.whl.metadata (9.2 kB)\n",
"Collecting pint>=0.17 (from napari)\n",
" Using cached Pint-0.24.3-py3-none-any.whl.metadata (8.5 kB)\n",
"Requirement already satisfied: psutil>=5.0 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from napari) (6.0.0)\n",
"Collecting psygnal>=0.5.0 (from napari)\n",
" Using cached psygnal-0.11.1-cp310-cp310-macosx_10_16_x86_64.whl.metadata (7.2 kB)\n",
"Collecting pydantic>=1.9.0 (from napari)\n",
" Using cached pydantic-2.9.1-py3-none-any.whl.metadata (146 kB)\n",
"Requirement already satisfied: pygments>=2.6.0 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from napari) (2.18.0)\n",
"Collecting PyOpenGL>=3.1.0 (from napari)\n",
" Using cached PyOpenGL-3.1.7-py3-none-any.whl.metadata (3.2 kB)\n",
"Requirement already satisfied: PyYAML>=5.1 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from napari) (6.0.2)\n",
"Collecting qtpy>=1.10.0 (from napari)\n",
" Using cached QtPy-2.4.1-py3-none-any.whl.metadata (12 kB)\n",
"Collecting scikit-image>=0.19.1 (from scikit-image[data]>=0.19.1->napari)\n",
" Using cached scikit_image-0.24.0-cp310-cp310-macosx_10_9_x86_64.whl.metadata (14 kB)\n",
"Collecting scipy>=1.5.4 (from napari)\n",
" Using cached scipy-1.14.1-cp310-cp310-macosx_14_0_x86_64.whl.metadata (60 kB)\n",
"Collecting superqt>=0.6.7 (from napari)\n",
" Using cached superqt-0.6.7-py3-none-any.whl.metadata (5.7 kB)\n",
"Collecting tifffile>=2022.4.8 (from napari)\n",
" Using cached tifffile-2024.8.30-py3-none-any.whl.metadata (31 kB)\n",
"Collecting toolz>=0.10.0 (from napari)\n",
" Using cached toolz-0.12.1-py3-none-any.whl.metadata (5.1 kB)\n",
"Collecting tqdm>=4.56.0 (from napari)\n",
" Using cached tqdm-4.66.5-py3-none-any.whl.metadata (57 kB)\n",
"Requirement already satisfied: typing-extensions>=4.2.0 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from napari) (4.12.2)\n",
"Collecting vispy<0.15,>=0.14.1 (from napari)\n",
" Using cached vispy-0.14.3-cp310-cp310-macosx_10_9_x86_64.whl.metadata (8.4 kB)\n",
"Collecting wrapt>=1.11.1 (from napari)\n",
" Using cached wrapt-1.16.0-cp310-cp310-macosx_10_9_x86_64.whl.metadata (6.6 kB)\n",
"Collecting torch>=1.9 (from monai)\n",
" Using cached torch-2.2.2-cp310-none-macosx_10_9_x86_64.whl.metadata (25 kB)\n",
"Collecting asciitree (from zarr)\n",
" Using cached asciitree-0.3.3-py3-none-any.whl\n",
"Collecting numcodecs>=0.10.0 (from zarr)\n",
" Using cached numcodecs-0.13.0-cp310-cp310-macosx_10_9_x86_64.whl.metadata (2.9 kB)\n",
"Collecting fasteners (from zarr)\n",
" Using cached fasteners-0.19-py3-none-any.whl.metadata (4.9 kB)\n",
"Collecting in-n-out>=0.1.5 (from app-model<0.3.0,>=0.2.8->napari)\n",
" Using cached in_n_out-0.2.1-py3-none-any.whl.metadata (4.1 kB)\n",
"Collecting pydantic-compat>=0.1.1 (from app-model<0.3.0,>=0.2.8->napari)\n",
" Using cached pydantic_compat-0.1.2-py3-none-any.whl.metadata (8.4 kB)\n",
"Collecting heapdict (from cachey>=0.2.1->napari)\n",
" Using cached HeapDict-1.0.1-py3-none-any.whl.metadata (1.9 kB)\n",
"Collecting click>=8.1 (from dask>=2021.10.0->dask[array]>=2021.10.0->napari)\n",
" Using cached click-8.1.7-py3-none-any.whl.metadata (3.0 kB)\n",
"Collecting cloudpickle>=3.0.0 (from dask>=2021.10.0->dask[array]>=2021.10.0->napari)\n",
" Using cached cloudpickle-3.0.0-py3-none-any.whl.metadata (7.0 kB)\n",
"Collecting fsspec>=2021.09.0 (from dask>=2021.10.0->dask[array]>=2021.10.0->napari)\n",
" Using cached fsspec-2024.9.0-py3-none-any.whl.metadata (11 kB)\n",
"Requirement already satisfied: packaging>=20.0 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from dask>=2021.10.0->dask[array]>=2021.10.0->napari) (24.1)\n",
"Collecting partd>=1.4.0 (from dask>=2021.10.0->dask[array]>=2021.10.0->napari)\n",
" Using cached partd-1.4.2-py3-none-any.whl.metadata (4.6 kB)\n",
"Requirement already satisfied: importlib-metadata>=4.13.0 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from dask>=2021.10.0->dask[array]>=2021.10.0->napari) (8.5.0)\n",
"Requirement already satisfied: attrs>=22.2.0 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from jsonschema>=3.2.0->napari) (24.2.0)\n",
"Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from jsonschema>=3.2.0->napari) (2023.12.1)\n",
"Requirement already satisfied: referencing>=0.28.4 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from jsonschema>=3.2.0->napari) (0.35.1)\n",
"Requirement already satisfied: rpds-py>=0.7.1 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from jsonschema>=3.2.0->napari) (0.20.0)\n",
"Collecting docstring-parser>=0.7 (from magicgui>=0.7.0->napari)\n",
" Using cached docstring_parser-0.16-py3-none-any.whl.metadata (3.0 kB)\n",
"Requirement already satisfied: IPython>=7.7.0 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from napari-console>=0.0.9->napari) (8.27.0)\n",
"Requirement already satisfied: ipykernel>=5.2.0 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from napari-console>=0.0.9->napari) (6.29.5)\n",
"Collecting qtconsole!=4.7.6,!=5.4.2,>=4.5.1 (from napari-console>=0.0.9->napari)\n",
" Using cached qtconsole-5.6.0-py3-none-any.whl.metadata (5.0 kB)\n",
"Collecting build>=1 (from npe2>=0.7.6->napari)\n",
" Downloading build-1.2.2-py3-none-any.whl.metadata (6.2 kB)\n",
"Collecting rich (from npe2>=0.7.6->napari)\n",
" Downloading rich-13.8.1-py3-none-any.whl.metadata (18 kB)\n",
"Collecting tomli-w (from npe2>=0.7.6->napari)\n",
" Using cached tomli_w-1.0.0-py3-none-any.whl.metadata (4.9 kB)\n",
"Requirement already satisfied: tomli in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from npe2>=0.7.6->napari) (2.0.1)\n",
"Collecting typer (from npe2>=0.7.6->napari)\n",
" Using cached typer-0.12.5-py3-none-any.whl.metadata (15 kB)\n",
"Collecting sphinx>=6 (from numpydoc>=0.9.2->napari)\n",
" Using cached sphinx-8.0.2-py3-none-any.whl.metadata (6.2 kB)\n",
"Collecting tabulate>=0.8.10 (from numpydoc>=0.9.2->napari)\n",
" Using cached tabulate-0.9.0-py3-none-any.whl.metadata (34 kB)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from pandas>=1.3.0->napari) (2.9.0)\n",
"Requirement already satisfied: pytz>=2020.1 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from pandas>=1.3.0->napari) (2024.2)\n",
"Collecting tzdata>=2022.7 (from pandas>=1.3.0->napari)\n",
" Using cached tzdata-2024.1-py2.py3-none-any.whl.metadata (1.4 kB)\n",
"Collecting flexcache>=0.3 (from pint>=0.17->napari)\n",
" Using cached flexcache-0.3-py3-none-any.whl.metadata (7.0 kB)\n",
"Collecting flexparser>=0.3 (from pint>=0.17->napari)\n",
" Using cached flexparser-0.3.1-py3-none-any.whl.metadata (18 kB)\n",
"Collecting annotated-types>=0.6.0 (from pydantic>=1.9.0->napari)\n",
" Using cached annotated_types-0.7.0-py3-none-any.whl.metadata (15 kB)\n",
"Collecting pydantic-core==2.23.3 (from pydantic>=1.9.0->napari)\n",
" Using cached pydantic_core-2.23.3-cp310-cp310-macosx_10_12_x86_64.whl.metadata (6.6 kB)\n",
"Collecting networkx>=2.8 (from scikit-image>=0.19.1->scikit-image[data]>=0.19.1->napari)\n",
" Using cached networkx-3.3-py3-none-any.whl.metadata (5.1 kB)\n",
"Collecting pooch>=1.6.0 (from scikit-image[data]>=0.19.1->napari)\n",
" Using cached pooch-1.8.2-py3-none-any.whl.metadata (10 kB)\n",
"Collecting filelock (from torch>=1.9->monai)\n",
" Using cached filelock-3.16.0-py3-none-any.whl.metadata (3.0 kB)\n",
"Collecting sympy (from torch>=1.9->monai)\n",
" Using cached sympy-1.13.2-py3-none-any.whl.metadata (12 kB)\n",
"Requirement already satisfied: jinja2 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from torch>=1.9->monai) (3.1.4)\n",
"Collecting freetype-py (from vispy<0.15,>=0.14.1->napari)\n",
" Using cached freetype_py-2.5.1-py3-none-macosx_10_9_universal2.whl.metadata (6.3 kB)\n",
"Collecting hsluv (from vispy<0.15,>=0.14.1->napari)\n",
" Using cached hsluv-5.0.4-py2.py3-none-any.whl.metadata (2.7 kB)\n",
"Collecting kiwisolver (from vispy<0.15,>=0.14.1->napari)\n",
" Downloading kiwisolver-1.4.7-cp310-cp310-macosx_10_9_x86_64.whl.metadata (6.3 kB)\n",
"Collecting pyproject_hooks (from build>=1->npe2>=0.7.6->napari)\n",
" Using cached pyproject_hooks-1.1.0-py3-none-any.whl.metadata (1.3 kB)\n",
"Requirement already satisfied: zipp>=3.20 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from importlib-metadata>=4.13.0->dask>=2021.10.0->dask[array]>=2021.10.0->napari) (3.20.1)\n",
"Requirement already satisfied: appnope in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from ipykernel>=5.2.0->napari-console>=0.0.9->napari) (0.1.4)\n",
"Requirement already satisfied: comm>=0.1.1 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from ipykernel>=5.2.0->napari-console>=0.0.9->napari) (0.2.2)\n",
"Requirement already satisfied: debugpy>=1.6.5 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from ipykernel>=5.2.0->napari-console>=0.0.9->napari) (1.8.5)\n",
"Requirement already satisfied: jupyter-client>=6.1.12 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from ipykernel>=5.2.0->napari-console>=0.0.9->napari) (8.6.2)\n",
"Requirement already satisfied: jupyter-core!=5.0.*,>=4.12 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from ipykernel>=5.2.0->napari-console>=0.0.9->napari) (5.7.2)\n",
"Requirement already satisfied: matplotlib-inline>=0.1 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from ipykernel>=5.2.0->napari-console>=0.0.9->napari) (0.1.7)\n",
"Requirement already satisfied: nest-asyncio in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from ipykernel>=5.2.0->napari-console>=0.0.9->napari) (1.6.0)\n",
"Requirement already satisfied: pyzmq>=24 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from ipykernel>=5.2.0->napari-console>=0.0.9->napari) (26.2.0)\n",
"Requirement already satisfied: tornado>=6.1 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from ipykernel>=5.2.0->napari-console>=0.0.9->napari) (6.4.1)\n",
"Requirement already satisfied: traitlets>=5.4.0 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from ipykernel>=5.2.0->napari-console>=0.0.9->napari) (5.14.3)\n",
"Requirement already satisfied: decorator in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from IPython>=7.7.0->napari-console>=0.0.9->napari) (5.1.1)\n",
"Requirement already satisfied: jedi>=0.16 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from IPython>=7.7.0->napari-console>=0.0.9->napari) (0.19.1)\n",
"Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from IPython>=7.7.0->napari-console>=0.0.9->napari) (3.0.47)\n",
"Requirement already satisfied: stack-data in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from IPython>=7.7.0->napari-console>=0.0.9->napari) (0.6.2)\n",
"Requirement already satisfied: exceptiongroup in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from IPython>=7.7.0->napari-console>=0.0.9->napari) (1.2.2)\n",
"Requirement already satisfied: pexpect>4.3 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from IPython>=7.7.0->napari-console>=0.0.9->napari) (4.9.0)\n",
"Collecting locket (from partd>=1.4.0->dask>=2021.10.0->dask[array]>=2021.10.0->napari)\n",
" Using cached locket-1.0.0-py2.py3-none-any.whl.metadata (2.8 kB)\n",
"Requirement already satisfied: platformdirs>=2.5.0 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from pooch>=1.6.0->scikit-image[data]>=0.19.1->napari) (4.3.2)\n",
"Requirement already satisfied: requests>=2.19.0 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from pooch>=1.6.0->scikit-image[data]>=0.19.1->napari) (2.32.3)\n",
"Requirement already satisfied: six>=1.5 in /Users/kharrington/.local/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas>=1.3.0->napari) (1.16.0)\n",
"Collecting sphinxcontrib-applehelp (from sphinx>=6->numpydoc>=0.9.2->napari)\n",
" Using cached sphinxcontrib_applehelp-2.0.0-py3-none-any.whl.metadata (2.3 kB)\n",
"Collecting sphinxcontrib-devhelp (from sphinx>=6->numpydoc>=0.9.2->napari)\n",
" Using cached sphinxcontrib_devhelp-2.0.0-py3-none-any.whl.metadata (2.3 kB)\n",
"Collecting sphinxcontrib-jsmath (from sphinx>=6->numpydoc>=0.9.2->napari)\n",
" Using cached sphinxcontrib_jsmath-1.0.1-py2.py3-none-any.whl.metadata (1.4 kB)\n",
"Collecting sphinxcontrib-htmlhelp>=2.0.0 (from sphinx>=6->numpydoc>=0.9.2->napari)\n",
" Using cached sphinxcontrib_htmlhelp-2.1.0-py3-none-any.whl.metadata (2.3 kB)\n",
"Collecting sphinxcontrib-serializinghtml>=1.1.9 (from sphinx>=6->numpydoc>=0.9.2->napari)\n",
" Using cached sphinxcontrib_serializinghtml-2.0.0-py3-none-any.whl.metadata (2.4 kB)\n",
"Collecting sphinxcontrib-qthelp (from sphinx>=6->numpydoc>=0.9.2->napari)\n",
" Using cached sphinxcontrib_qthelp-2.0.0-py3-none-any.whl.metadata (2.3 kB)\n",
"Collecting docutils<0.22,>=0.20 (from sphinx>=6->numpydoc>=0.9.2->napari)\n",
" Using cached docutils-0.21.2-py3-none-any.whl.metadata (2.8 kB)\n",
"Collecting snowballstemmer>=2.2 (from sphinx>=6->numpydoc>=0.9.2->napari)\n",
" Using cached snowballstemmer-2.2.0-py2.py3-none-any.whl.metadata (6.5 kB)\n",
"Requirement already satisfied: babel>=2.13 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from sphinx>=6->numpydoc>=0.9.2->napari) (2.14.0)\n",
"Collecting alabaster>=0.7.14 (from sphinx>=6->numpydoc>=0.9.2->napari)\n",
" Using cached alabaster-1.0.0-py3-none-any.whl.metadata (2.8 kB)\n",
"Collecting imagesize>=1.3 (from sphinx>=6->numpydoc>=0.9.2->napari)\n",
" Using cached imagesize-1.4.1-py2.py3-none-any.whl.metadata (1.5 kB)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from jinja2->torch>=1.9->monai) (2.1.5)\n",
"Collecting pyconify>=0.1.4 (from superqt[iconify]>=0.6.1->magicgui>=0.7.0->napari)\n",
" Using cached pyconify-0.1.6-py3-none-any.whl.metadata (5.5 kB)\n",
"Collecting markdown-it-py>=2.2.0 (from rich->npe2>=0.7.6->napari)\n",
" Using cached markdown_it_py-3.0.0-py3-none-any.whl.metadata (6.9 kB)\n",
"Collecting mpmath<1.4,>=1.1.0 (from sympy->torch>=1.9->monai)\n",
" Using cached mpmath-1.3.0-py3-none-any.whl.metadata (8.6 kB)\n",
"Collecting shellingham>=1.3.0 (from typer->npe2>=0.7.6->napari)\n",
" Using cached shellingham-1.5.4-py2.py3-none-any.whl.metadata (3.5 kB)\n",
"Requirement already satisfied: parso<0.9.0,>=0.8.3 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from jedi>=0.16->IPython>=7.7.0->napari-console>=0.0.9->napari) (0.8.4)\n",
"Collecting mdurl~=0.1 (from markdown-it-py>=2.2.0->rich->npe2>=0.7.6->napari)\n",
" Using cached mdurl-0.1.2-py3-none-any.whl.metadata (1.6 kB)\n",
"Requirement already satisfied: ptyprocess>=0.5 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from pexpect>4.3->IPython>=7.7.0->napari-console>=0.0.9->napari) (0.7.0)\n",
"Requirement already satisfied: wcwidth in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->IPython>=7.7.0->napari-console>=0.0.9->napari) (0.2.13)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from requests>=2.19.0->pooch>=1.6.0->scikit-image[data]>=0.19.1->napari) (3.3.2)\n",
"Requirement already satisfied: idna<4,>=2.5 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from requests>=2.19.0->pooch>=1.6.0->scikit-image[data]>=0.19.1->napari) (3.8)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from requests>=2.19.0->pooch>=1.6.0->scikit-image[data]>=0.19.1->napari) (2.2.2)\n",
"Requirement already satisfied: executing>=1.2.0 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from stack-data->IPython>=7.7.0->napari-console>=0.0.9->napari) (2.1.0)\n",
"Requirement already satisfied: asttokens>=2.1.0 in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from stack-data->IPython>=7.7.0->napari-console>=0.0.9->napari) (2.4.1)\n",
"Requirement already satisfied: pure-eval in /Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages (from stack-data->IPython>=7.7.0->napari-console>=0.0.9->napari) (0.2.3)\n",
"Downloading napari-0.5.3-py3-none-any.whl (3.0 MB)\n",
"\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m3.0/3.0 MB\u001B[0m \u001B[31m4.0 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0ma \u001B[36m0:00:01\u001B[0m\n",
"\u001B[?25hUsing cached monai-1.3.2-py3-none-any.whl (1.4 MB)\n",
"Downloading zarr-2.18.3-py3-none-any.whl (210 kB)\n",
"Using cached app_model-0.2.8-py3-none-any.whl (64 kB)\n",
"Using cached appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)\n",
"Using cached cachey-0.2.1-py3-none-any.whl (6.4 kB)\n",
"Using cached dask-2024.8.2-py3-none-any.whl (1.2 MB)\n",
"Using cached imageio-2.35.1-py3-none-any.whl (315 kB)\n",
"Using cached lazy_loader-0.4-py3-none-any.whl (12 kB)\n",
"Using cached magicgui-0.9.1-py3-none-any.whl (124 kB)\n",
"Using cached napari_console-0.0.9-py3-none-any.whl (8.2 kB)\n",
"Using cached napari_plugin_engine-0.2.0-py3-none-any.whl (33 kB)\n",
"Using cached napari_svg-0.2.0-py3-none-any.whl (15 kB)\n",
"Using cached npe2-0.7.7-py3-none-any.whl (92 kB)\n",
"Using cached numcodecs-0.13.0-cp310-cp310-macosx_10_9_x86_64.whl (1.6 MB)\n",
"Using cached numpydoc-1.8.0-py3-none-any.whl (64 kB)\n",
"Using cached pandas-2.2.2-cp310-cp310-macosx_10_9_x86_64.whl (12.6 MB)\n",
"Using cached pillow-10.4.0-cp310-cp310-macosx_10_10_x86_64.whl (3.5 MB)\n",
"Using cached Pint-0.24.3-py3-none-any.whl (301 kB)\n",
"Using cached psygnal-0.11.1-cp310-cp310-macosx_10_16_x86_64.whl (462 kB)\n",
"Using cached pydantic-2.9.1-py3-none-any.whl (434 kB)\n",
"Using cached pydantic_core-2.23.3-cp310-cp310-macosx_10_12_x86_64.whl (1.8 MB)\n",
"Using cached PyOpenGL-3.1.7-py3-none-any.whl (2.4 MB)\n",
"Using cached QtPy-2.4.1-py3-none-any.whl (93 kB)\n",
"Using cached scikit_image-0.24.0-cp310-cp310-macosx_10_9_x86_64.whl (14.1 MB)\n",
"Using cached scipy-1.14.1-cp310-cp310-macosx_14_0_x86_64.whl (25.5 MB)\n",
"Using cached superqt-0.6.7-py3-none-any.whl (90 kB)\n",
"Using cached tifffile-2024.8.30-py3-none-any.whl (227 kB)\n",
"Using cached toolz-0.12.1-py3-none-any.whl (56 kB)\n",
"Using cached torch-2.2.2-cp310-none-macosx_10_9_x86_64.whl (150.8 MB)\n",
"Using cached tqdm-4.66.5-py3-none-any.whl (78 kB)\n",
"Using cached vispy-0.14.3-cp310-cp310-macosx_10_9_x86_64.whl (1.5 MB)\n",
"Using cached wrapt-1.16.0-cp310-cp310-macosx_10_9_x86_64.whl (37 kB)\n",
"Using cached fasteners-0.19-py3-none-any.whl (18 kB)\n",
"Using cached annotated_types-0.7.0-py3-none-any.whl (13 kB)\n",
"Downloading build-1.2.2-py3-none-any.whl (22 kB)\n",
"Using cached click-8.1.7-py3-none-any.whl (97 kB)\n",
"Using cached cloudpickle-3.0.0-py3-none-any.whl (20 kB)\n",
"Using cached docstring_parser-0.16-py3-none-any.whl (36 kB)\n",
"Using cached flexcache-0.3-py3-none-any.whl (13 kB)\n",
"Using cached flexparser-0.3.1-py3-none-any.whl (27 kB)\n",
"Using cached fsspec-2024.9.0-py3-none-any.whl (179 kB)\n",
"Using cached in_n_out-0.2.1-py3-none-any.whl (19 kB)\n",
"Using cached networkx-3.3-py3-none-any.whl (1.7 MB)\n",
"Using cached partd-1.4.2-py3-none-any.whl (18 kB)\n",
"Using cached pooch-1.8.2-py3-none-any.whl (64 kB)\n",
"Using cached pydantic_compat-0.1.2-py3-none-any.whl (13 kB)\n",
"Using cached qtconsole-5.6.0-py3-none-any.whl (124 kB)\n",
"Using cached sphinx-8.0.2-py3-none-any.whl (3.5 MB)\n",
"Using cached tabulate-0.9.0-py3-none-any.whl (35 kB)\n",
"Using cached tzdata-2024.1-py2.py3-none-any.whl (345 kB)\n",
"Using cached filelock-3.16.0-py3-none-any.whl (16 kB)\n",
"Using cached freetype_py-2.5.1-py3-none-macosx_10_9_universal2.whl (1.7 MB)\n",
"Using cached HeapDict-1.0.1-py3-none-any.whl (3.9 kB)\n",
"Using cached hsluv-5.0.4-py2.py3-none-any.whl (5.3 kB)\n",
"Downloading kiwisolver-1.4.7-cp310-cp310-macosx_10_9_x86_64.whl (65 kB)\n",
"Downloading rich-13.8.1-py3-none-any.whl (241 kB)\n",
"Using cached sympy-1.13.2-py3-none-any.whl (6.2 MB)\n",
"Using cached tomli_w-1.0.0-py3-none-any.whl (6.0 kB)\n",
"Using cached typer-0.12.5-py3-none-any.whl (47 kB)\n",
"Using cached alabaster-1.0.0-py3-none-any.whl (13 kB)\n",
"Using cached docutils-0.21.2-py3-none-any.whl (587 kB)\n",
"Using cached imagesize-1.4.1-py2.py3-none-any.whl (8.8 kB)\n",
"Using cached markdown_it_py-3.0.0-py3-none-any.whl (87 kB)\n",
"Using cached mpmath-1.3.0-py3-none-any.whl (536 kB)\n",
"Using cached pyconify-0.1.6-py3-none-any.whl (18 kB)\n",
"Using cached shellingham-1.5.4-py2.py3-none-any.whl (9.8 kB)\n",
"Using cached snowballstemmer-2.2.0-py2.py3-none-any.whl (93 kB)\n",
"Using cached sphinxcontrib_htmlhelp-2.1.0-py3-none-any.whl (98 kB)\n",
"Using cached sphinxcontrib_serializinghtml-2.0.0-py3-none-any.whl (92 kB)\n",
"Using cached locket-1.0.0-py2.py3-none-any.whl (4.4 kB)\n",
"Using cached pyproject_hooks-1.1.0-py3-none-any.whl (9.2 kB)\n",
"Using cached sphinxcontrib_applehelp-2.0.0-py3-none-any.whl (119 kB)\n",
"Using cached sphinxcontrib_devhelp-2.0.0-py3-none-any.whl (82 kB)\n",
"Using cached sphinxcontrib_jsmath-1.0.1-py2.py3-none-any.whl (5.1 kB)\n",
"Using cached sphinxcontrib_qthelp-2.0.0-py3-none-any.whl (88 kB)\n",
"Using cached mdurl-0.1.2-py3-none-any.whl (10.0 kB)\n",
"Installing collected packages: snowballstemmer, PyOpenGL, mpmath, heapdict, asciitree, appdirs, wrapt, tzdata, tqdm, toolz, tomli-w, tifffile, tabulate, sympy, sphinxcontrib-serializinghtml, sphinxcontrib-qthelp, sphinxcontrib-jsmath, sphinxcontrib-htmlhelp, sphinxcontrib-devhelp, sphinxcontrib-applehelp, shellingham, scipy, qtpy, pyproject_hooks, pydantic-core, psygnal, Pillow, numcodecs, networkx, napari-plugin-engine, mdurl, locket, lazy-loader, kiwisolver, in-n-out, imagesize, hsluv, fsspec, freetype-py, flexparser, flexcache, filelock, fasteners, docutils, docstring-parser, cloudpickle, click, cachey, annotated-types, alabaster, zarr, vispy, torch, superqt, sphinx, pydantic, pyconify, pooch, pint, partd, pandas, markdown-it-py, imageio, build, scikit-image, rich, pydantic-compat, numpydoc, napari-svg, monai, dask, typer, magicgui, app-model, qtconsole, npe2, napari-console, napari\n",
"\u001B[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"virtualenv 20.21.0 requires platformdirs<4,>=2.4, but you have platformdirs 4.3.2 which is incompatible.\u001B[0m\u001B[31m\n",
"\u001B[0mSuccessfully installed Pillow-10.4.0 PyOpenGL-3.1.7 alabaster-1.0.0 annotated-types-0.7.0 app-model-0.2.8 appdirs-1.4.4 asciitree-0.3.3 build-1.2.2 cachey-0.2.1 click-8.1.7 cloudpickle-3.0.0 dask-2024.8.2 docstring-parser-0.16 docutils-0.21.2 fasteners-0.19 filelock-3.16.0 flexcache-0.3 flexparser-0.3.1 freetype-py-2.5.1 fsspec-2024.9.0 heapdict-1.0.1 hsluv-5.0.4 imageio-2.35.1 imagesize-1.4.1 in-n-out-0.2.1 kiwisolver-1.4.7 lazy-loader-0.4 locket-1.0.0 magicgui-0.9.1 markdown-it-py-3.0.0 mdurl-0.1.2 monai-1.3.2 mpmath-1.3.0 napari-0.5.3 napari-console-0.0.9 napari-plugin-engine-0.2.0 napari-svg-0.2.0 networkx-3.3 npe2-0.7.7 numcodecs-0.13.0 numpydoc-1.8.0 pandas-2.2.2 partd-1.4.2 pint-0.24.3 pooch-1.8.2 psygnal-0.11.1 pyconify-0.1.6 pydantic-2.9.1 pydantic-compat-0.1.2 pydantic-core-2.23.3 pyproject_hooks-1.1.0 qtconsole-5.6.0 qtpy-2.4.1 rich-13.8.1 scikit-image-0.24.0 scipy-1.14.1 shellingham-1.5.4 snowballstemmer-2.2.0 sphinx-8.0.2 sphinxcontrib-applehelp-2.0.0 sphinxcontrib-devhelp-2.0.0 sphinxcontrib-htmlhelp-2.1.0 sphinxcontrib-jsmath-1.0.1 sphinxcontrib-qthelp-2.0.0 sphinxcontrib-serializinghtml-2.0.0 superqt-0.6.7 sympy-1.13.2 tabulate-0.9.0 tifffile-2024.8.30 tomli-w-1.0.0 toolz-0.12.1 torch-2.2.2 tqdm-4.66.5 typer-0.12.5 tzdata-2024.1 vispy-0.14.3 wrapt-1.16.0 zarr-2.18.3\n"
]
}
],
"source": "!pip install napari monai zarr \"numpy<2\" cryoet-data-portal==4.0.0 s3fs trimesh ome-zarr scikit-image trimesh pyside2"
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting copick\n",
" Using cached copick-0.5.6-py3-none-any.whl.metadata (6.7 kB)\n",
"Using cached copick-0.5.6-py3-none-any.whl (32 kB)\n",
"Installing collected packages: copick\n",
"Successfully installed copick-0.5.6\n",
"Collecting git+https://github.com/copick/copick-utils.git\n",
" Cloning https://github.com/copick/copick-utils.git to /private/var/folders/z0/_bcy172x6xg12j6wylfkhc_h0000gq/T/pip-req-build-we5npo_q\n",
" Running command git clone --filter=blob:none --quiet https://github.com/copick/copick-utils.git /private/var/folders/z0/_bcy172x6xg12j6wylfkhc_h0000gq/T/pip-req-build-we5npo_q\n",
" Resolved https://github.com/copick/copick-utils.git to commit a21f5f289d54844287f512da36045e66c9d10f09\n",
" Installing build dependencies ... \u001B[?25ldone\n",
"\u001B[?25h Getting requirements to build wheel ... \u001B[?25ldone\n",
"\u001B[?25h Preparing metadata (pyproject.toml) ... \u001B[?25ldone\n",
"\u001B[?25hBuilding wheels for collected packages: copick-utils\n",
" Building wheel for copick-utils (pyproject.toml) ... \u001B[?25ldone\n",
"\u001B[?25h Created wheel for copick-utils: filename=copick_utils-0.0.1-py3-none-any.whl size=8703 sha256=5f9f9f87b1c8163ba18bb51034f3be7b7e70ae09b3f283e51111de60ed7dc5ce\n",
" Stored in directory: /private/var/folders/z0/_bcy172x6xg12j6wylfkhc_h0000gq/T/pip-ephem-wheel-cache-b6yw7zz1/wheels/a6/a7/39/72b64bd4eecf4d57d5d4e1fcec96fdb0cd49ae7dbb336fcc4c\n",
"Successfully built copick-utils\n",
"Installing collected packages: copick-utils\n",
"Successfully installed copick-utils-0.0.1\n",
"Collecting h5py\n",
" Using cached h5py-3.11.0-cp310-cp310-macosx_10_9_x86_64.whl.metadata (2.5 kB)\n",
"Using cached h5py-3.11.0-cp310-cp310-macosx_10_9_x86_64.whl (3.5 MB)\n",
"Installing collected packages: h5py\n",
"Successfully installed h5py-3.11.0\n",
"Collecting morphospaces\n",
" Using cached morphospaces-0.0.4-py3-none-any.whl.metadata (4.5 kB)\n",
"Using cached morphospaces-0.0.4-py3-none-any.whl (63 kB)\n",
"Installing collected packages: morphospaces\n",
"Successfully installed morphospaces-0.0.4\n",
"Collecting https://github.com/copick/copick-torch.git\n",
" Downloading https://github.com/copick/copick-torch.git\n",
"\u001B[2K \u001B[32m-\u001B[0m \u001B[32m276.6 kB\u001B[0m \u001B[31m6.6 MB/s\u001B[0m \u001B[33m0:00:00\u001B[0m\n",
"\u001B[?25h\u001B[31m ERROR: Cannot unpack file /private/var/folders/z0/_bcy172x6xg12j6wylfkhc_h0000gq/T/pip-unpack-j4tbf0d4/copick-torch.git (downloaded from /private/var/folders/z0/_bcy172x6xg12j6wylfkhc_h0000gq/T/pip-req-build-vzcxxv97, content-type: text/html; charset=utf-8); cannot detect archive format\u001B[0m\u001B[31m\n",
"\u001B[0m\u001B[31mERROR: Cannot determine archive format of /private/var/folders/z0/_bcy172x6xg12j6wylfkhc_h0000gq/T/pip-req-build-vzcxxv97\u001B[0m\u001B[31m\n",
"\u001B[0m"
]
}
],
"source": [
"!pip install --no-deps copick>=0.6.0\n",
"!pip install --no-deps git+https://github.com/copick/copick-utils.git\n",
"!pip install --no-deps h5py\n",
"!pip install --no-deps morphospaces\n",
"!pip install --no-deps git+https://github.com/copick/copick-torch.git"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"copick_config = \"\"\"{\n",
" \"config_type\": \"cryoet_data_portal\",\n",
" \"name\": \"polnet\",\n",
" \"description\": \"A Data Portal project.\",\n",
" \"version\": \"1.0.0\",\n",
"\n",
" \"pickable_objects\": [\n",
" {\n",
" \"name\": \"membrane\",\n",
" \"is_particle\": false,\n",
" \"label\": 1,\n",
" \"go_id\": \"GO:0016020\", \n",
" \"color\": [150,150, 150, 255],\n",
" \"radius\": 10\n",
" },\n",
" {\n",
" \"name\": \"adp-mitochondrial\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"6MRD\",\n",
" \"go_id\": \"GO:1990220\",\n",
" \"label\": 2,\n",
" \"color\": [ 0, 117, 220, 255],\n",
" \"radius\": 75,\n",
" \"map_threshold\": 0.037\n",
" },\n",
" {\n",
" \"name\": \"alkaline-phosphate\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"1ZEF\",\n",
" \"go_id\": \"UniProtKB:P05187\",\n",
" \"label\": 3,\n",
" \"color\": [ 0, 117, 220, 255],\n",
" \"radius\": 75,\n",
" \"map_threshold\": 0.037\n",
" },\n",
" {\n",
" \"name\": \"nucleosome\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"6UPH\",\n",
" \"go_id\": \"GO:0000786\",\n",
" \"label\": 4,\n",
" \"color\": [ 0, 117, 220, 255],\n",
" \"radius\": 65,\n",
" \"map_threshold\": 0.037\n",
" }, \n",
" {\n",
" \"name\": \"ribosome\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"7TM3\",\n",
" \"go_id\": \"GO:0022626\",\n",
" \"label\": 5,\n",
" \"color\": [ 0, 117, 220, 255],\n",
" \"radius\": 165,\n",
" \"map_threshold\": 0.037\n",
" },\n",
" {\n",
" \"name\": \"vault\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"7PKZ\",\n",
" \"go_id\": \"UniProtKB:Q62667\",\n",
" \"label\": 6,\n",
" \"color\": [ 0, 117, 220, 255],\n",
" \"radius\": 165,\n",
" \"map_threshold\": 0.037\n",
" },\n",
" {\n",
" \"name\": \"virus-like-capsid\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"6NK5\",\n",
" \"go_id\": \"GO:0170047\",\n",
" \"label\": 7,\n",
" \"color\": [ 0, 117, 220, 255],\n",
" \"radius\": 140,\n",
" \"map_threshold\": 0.037\n",
" } \n",
" ],\n",
"\n",
" \"overlay_root\": \"local:///tmp/CZDP_10439/overlay/\",\n",
"\n",
" \"overlay_fs_args\": {\n",
" \"auto_mkdir\": true\n",
" },\n",
"\n",
" \"dataset_ids\": [10439]\n",
"}\n",
"\"\"\"\n",
"# TODO set overlay root to a place safe in kaggle notebooks\n",
"\n",
"config_file = \"synthetic_data.json\"\n",
"\n",
"with open(config_file, \"w\") as f:\n",
" f.write(copick_config)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import copick\n",
"import copick_utils\n",
"\n",
"# config_file = \"/Users/kharrington/Data/copick/synthetic_data_10439.json\"\n",
"\n",
"root = copick.from_file(config_file)\n",
"\n",
"run_name = \"16193\"\n",
"example_run = root.get_run(run_name)\n",
"voxel_spacing= 10\n",
"tomo_type = \"wbp\"\n",
"paint_scale = 0.75\n",
"painting_segmentation_name = \"paintedPicks\""
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Painting CopickObject(name=adp-mitochondrial, is_particle=True, label=2, color=(0, 117, 220, 255), emdb_id=None, pdb_id=6MRD, threshold=0.037) at 0x181891210\n",
"Painting CopickObject(name=alkaline-phosphate, is_particle=True, label=3, color=(0, 117, 220, 255), emdb_id=None, pdb_id=1ZEF, threshold=0.037) at 0x181891510\n",
"Painting CopickObject(name=nucleosome, is_particle=True, label=4, color=(0, 117, 220, 255), emdb_id=None, pdb_id=6UPH, threshold=0.037) at 0x181891060\n",
"Painting CopickObject(name=ribosome, is_particle=True, label=5, color=(0, 117, 220, 255), emdb_id=None, pdb_id=7TM3, threshold=0.037) at 0x181892680\n",
"Painting CopickObject(name=vault, is_particle=True, label=6, color=(0, 117, 220, 255), emdb_id=None, pdb_id=7PKZ, threshold=0.037) at 0x181891180\n",
"Painting CopickObject(name=virus-like-capsid, is_particle=True, label=7, color=(0, 117, 220, 255), emdb_id=None, pdb_id=6NK5, threshold=0.037) at 0x181891000\n"
]
}
],
"source": [
"# Convert picks into segmentations\n",
"import re\n",
"from copick_utils.segmentation.segmentation_from_picks import segmentation_from_picks\n",
"\n",
"for pickable_object in root.pickable_objects:\n",
" if pickable_object.is_particle:\n",
" print(f\"Painting {pickable_object}\")\n",
" # radius = pickable_object.radius / voxel_spacing * paint_scale\n",
" radius = 10\n",
" object_name = pickable_object.name\n",
" pick_set = example_run.get_picks(object_name=object_name)[0]\n",
" segmentation_from_picks(radius, painting_segmentation_name, example_run, voxel_spacing, tomo_type, pickable_object, pick_set, user_id=\"paintedPicks\", session_id=\"0\")\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/kharrington/.album/micromamba/envs/cz_cryoet_mlchallenge/lib/python3.10/site-packages/dask/array/core.py:1706: FutureWarning: The `numpy.copy` function is not implemented by Dask array. You may want to use the da.map_blocks function or something similar to silence this warning. Your code may stop working in a future release.\n",
" warnings.warn(\n"
]
}
],
"source": [
"# napari visualization\n",
"\n",
"import napari\n",
"import numpy as np\n",
"import zarr\n",
"from copick_torch import data, transforms\n",
"from morphospaces.datasets.utils import PatchManager\n",
"from torch.utils.data import ConcatDataset\n",
"\n",
"voxel_spacing_obj = example_run.get_voxel_spacing(voxel_spacing)\n",
"tomogram = voxel_spacing_obj.tomograms[0]\n",
"image = zarr.open(tomogram.zarr(), mode='r')['0']\n",
"tomogram_data = image[:]\n",
"\n",
"# Get the segmentation mask\n",
"seg = example_run.get_segmentations(is_multilabel=True, name=painting_segmentation_name, voxel_size=voxel_spacing)\n",
"if len(seg) == 0:\n",
" raise ValueError(f\"No segmentations found for session '{session_id}' and segmentation type '{segmentation_type}'.\")\n",
"segmentation = zarr.open(seg[0].zarr().path, mode=\"r\")['0']\n",
"segmentation_data = segmentation[:]\n",
"\n",
"# Create a Napari viewer\n",
"viewer = napari.Viewer()\n",
"\n",
"# Display the tomogram data\n",
"viewer.add_image(np.asarray(tomogram_data), name='Tomogram')\n",
"\n",
"# Display the segmentation mask\n",
"viewer.add_labels(np.asarray(segmentation_data), name='Segmentation Mask', opacity=0.5)\n",
"\n",
"# Create a labels array to show the patches\n",
"labels = np.zeros_like(tomogram_data, dtype=np.int32)\n",
"\n",
"user_id = \"paintedPicks\"\n",
"session_id = \"0\"\n",
"\n",
"image_key = \"zarr_tomogram\"\n",
"labels_key = \"zarr_mask\"\n",
"patch_shape = (96, 96, 96)\n",
"# patch_stride = (24, 24, 24)\n",
"# patch_stride = (8, 8, 8)\n",
"patch_stride = (48, 48, 48)\n",
"patch_threshold = 0.9\n",
"\n",
"ds, unique_label_values = data.load_dataset(\n",
" config_file, run_name, tomo_type, user_id, session_id, painting_segmentation_name, voxel_spacing, None, patch_shape, patch_stride, labels_key, patch_threshold\n",
")\n",
"\n",
"# Check if ds is a ConcatDataset\n",
"if isinstance(ds, ConcatDataset):\n",
" patches = []\n",
" for dataset in ds.datasets:\n",
" if hasattr(dataset, 'patches'):\n",
" patches.extend(dataset.patches.slices)\n",
" else:\n",
" print(f\"Dataset {type(dataset)} does not have 'patches' attribute\")\n",
"else:\n",
" patches = ds.patches.slices\n",
"\n",
"# Label the patches\n",
"patch_id = 1\n",
"for patch in patches:\n",
" if len(patch) == 3:\n",
" z_start, y_start, x_start = patch[0].start, patch[1].start, patch[2].start\n",
" z_end, y_end, x_end = patch[0].stop, patch[1].stop, patch[2].stop\n",
" labels[z_start:z_end, y_start:y_end, x_start:x_end] = patch_id\n",
" patch_id += 1\n",
" else:\n",
" print(f\"Unexpected patch format: {patch}\")\n",
"\n",
"# Add the labels layer to highlight patches in 3D\n",
"viewer.add_labels(labels, name='Patch Labels')\n",
"\n",
"viewer.layers[0].bounding_box.visible = True\n",
"\n",
"# Start the Napari event loop\n",
"napari.run()\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"labels: [0 2 3 4 5 6 7]\n"
]
}
],
"source": [
"seg_data = np.asarray(segmentation_data)\n",
"print(f\"labels: {np.unique(seg_data)}\")\n",
"\n",
"viewer = napari.Viewer()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Image layer 'Image' at 0x1f65af850>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"viewer.add_image(seg_data>0)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"napari.run()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/kharrington/.album/micromamba/envs/monai-napari/lib/python3.10/site-packages/dask/array/core.py:1706: FutureWarning: The `numpy.copy` function is not implemented by Dask array. You may want to use the da.map_blocks function or something similar to silence this warning. Your code may stop working in a future release.\n",
" warnings.warn(\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1000x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 500x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# matplotlib visualization\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import zarr\n",
"from copick_torch import data, transforms\n",
"from morphospaces.datasets.utils import PatchManager\n",
"from torch.utils.data import ConcatDataset\n",
"\n",
"# Load the tomogram and segmentation data\n",
"voxel_spacing_obj = example_run.get_voxel_spacing(voxel_spacing)\n",
"tomogram = voxel_spacing_obj.tomograms[0]\n",
"image = zarr.open(tomogram.zarr(), mode='r')['0']\n",
"tomogram_data = image[:]\n",
"\n",
"# Get the segmentation mask\n",
"seg = example_run.get_segmentations(is_multilabel=True, name=painting_segmentation_name, voxel_size=voxel_spacing)\n",
"if len(seg) == 0:\n",
" raise ValueError(f\"No segmentations found for session '{session_id}' and segmentation type '{segmentation_type}'.\")\n",
"segmentation = zarr.open(seg[0].zarr().path, mode=\"r\")['0']\n",
"segmentation_data = segmentation[:]\n",
"\n",
"# Create a labels array to show the patches\n",
"labels = np.zeros_like(tomogram_data, dtype=np.int32)\n",
"\n",
"user_id = \"paintedPicks\"\n",
"session_id = \"0\"\n",
"image_key = \"zarr_tomogram\"\n",
"labels_key = \"zarr_mask\"\n",
"patch_shape = (96, 96, 96)\n",
"patch_stride = (8, 8, 8)\n",
"patch_threshold = 0.05\n",
"\n",
"# Load the dataset\n",
"ds, unique_label_values = data.load_dataset(\n",
" config_file, run_name, tomo_type, user_id, session_id, painting_segmentation_name, voxel_spacing, None, patch_shape, patch_stride, labels_key, patch_threshold\n",
")\n",
"\n",
"# Check if ds is a ConcatDataset\n",
"if isinstance(ds, ConcatDataset):\n",
" patches = []\n",
" for dataset in ds.datasets:\n",
" if hasattr(dataset, 'patches'):\n",
" patches.extend(dataset.patches.slices)\n",
" else:\n",
" print(f\"Dataset {type(dataset)} does not have 'patches' attribute\")\n",
"else:\n",
" patches = ds.patches.slices\n",
"\n",
"# Label the patches\n",
"patch_id = 1\n",
"for patch in patches:\n",
" if len(patch) == 3:\n",
" z_start, y_start, x_start = patch[0].start, patch[1].start, patch[2].start\n",
" z_end, y_end, x_end = patch[0].stop, patch[1].stop, patch[2].stop\n",
" labels[z_start:z_end, y_start:y_end, x_start:x_end] = patch_id\n",
" patch_id += 1\n",
" else:\n",
" print(f\"Unexpected patch format: {patch}\")\n",
"\n",
"# Choose a slice to display (e.g., middle slice in z-axis)\n",
"slice_index = tomogram_data.shape[0] // 2\n",
"\n",
"# Plot the tomogram slice\n",
"plt.figure(figsize=(10, 5))\n",
"\n",
"plt.subplot(1, 2, 1)\n",
"plt.imshow(tomogram_data[slice_index], cmap='gray')\n",
"plt.title('Tomogram Slice')\n",
"\n",
"# Plot the segmentation mask overlay\n",
"plt.subplot(1, 2, 2)\n",
"plt.imshow(tomogram_data[slice_index], cmap='gray')\n",
"plt.imshow(segmentation_data[slice_index], cmap='jet', alpha=0.5)\n",
"plt.title('Segmentation Mask')\n",
"\n",
"plt.show()\n",
"\n",
"# Plot the patches (just showing patches on a 2D slice for illustration)\n",
"plt.figure(figsize=(5, 5))\n",
"plt.imshow(tomogram_data[slice_index], cmap='gray')\n",
"plt.imshow(labels[slice_index], cmap='nipy_spectral', alpha=0.5)\n",
"plt.title('Patches')\n",
"plt.show()\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| Unknown |
3D | czimaginginstitute/2024_czii_mlchallenge_notebooks | tomotwin_picking_notebook/copick_tools.py | .py | 17,174 | 520 | import json
import os
from typing import Any, Dict, List
import copick
import numpy as np
import ome_zarr.writer
import starfile
import zarr
from scipy.spatial.transform import Rotation as R
def get_copick_project_tomo_ids(copickRoot):
copickRoot = copick.from_file(copickRoot)
tomoIDs = [run.name for run in copickRoot.runs]
return tomoIDs
def read_copick_tomogram_group(copickRoot, voxelSize, tomoAlgorithm, tomoID=None):
"""Find the Zarr Group Relating to a Copick Tomogram.
Args:
copickRoot: Target Copick Run to Extract Tomogram Zarrr Group.
voxelSize: Name of the Tomogram.
tomoAlgorithm: Session ID for the Tomogram.
tomoID: Tomogram ID for that Dataset.
Returns:
ZarrGroup for the Tomogram object.
"""
# Get First Run and Pull out Tomgram
if tomoID == None:
run = copickRoot.get_run(copickRoot.runs[0].name)
else:
run = copickRoot.get_run(tomoID)
tomogram = run.get_voxel_spacing(voxelSize).get_tomogram(tomoAlgorithm)
# Convert Zarr into Vol and Extract Shape
group = zarr.open(tomogram.zarr())
return group
def get_copick_tomogram(copickRoot, voxelSize=10, tomoAlgorithm="denoised", tomoID=None):
"""Return a Tomogram from a Copick Run.
Args:
copickRoot: Target Copick Root to Extract Tomogram.
voxelSize: Name of the Tomogram.
tomoAlgorithm: Session ID for the Tomogram.
tomoID: Tomogram ID for that Dataset.
Returns:
ZarrGroup for the Tomogram object.
"""
group = read_copick_tomogram_group(copickRoot, voxelSize, tomoAlgorithm, tomoID)
# Return Volume
return list(group.arrays())[0][1]
def get_copick_tomogram_shape(copickRoot, voxelSize=10, tomoAlgorithm="denoised"):
"""Return a Tomogram Dimensions (nx, ny, nz) from a Copick Run.
Args:
copickRoot: Target Copick Run to Extract Tomogram Dimensions.
voxelSize: Name of the Tomogram.
tomoAlgorithm: Session ID for the Tomogram.
tomoID: Tomogram ID for that Dataset.
Returns:
TomogrameShape
"""
# Return Volume Shape
return get_copick_tomogram(copickRoot, voxelSize, tomoAlgorithm).shape
def get_target_empty_tomogram(copickRoot, voxelSize=10, tomoAlgorithm="denoised"):
"""Return an Empty Tomogram with Equivalent Dimensions (nx, ny, nz) from a Copick Run.
Args:
copickRoot: Target Copick Run to Extract Empty Tomogram.
voxelSize: Name of the Tomogram.
tomoAlgorithm: Session ID for the Tomogram.
Returns:
A Tomogram Composed of Zeros with the Same Shape as in Copick Project.
"""
return np.zeros(get_copick_tomogram_shape(copickRoot, voxelSize, tomoAlgorithm), dtype=np.int8)
def get_copick_segmentation(copickRun, segmentationName="test-segmentation7", userID="deepfinder"):
"""Return a Specified Copick Segmentation.
Args:
copickRun: Target Copick Run to Extract Tomogram.
segmentationName: Name of the Segmentation.
userID: User who Created the Segmentation.
Returns:
The Segmentation within that Copick-Run.
"""
# Get the Segmentation from the Following Copick Run
seg = copickRun.get_segmentations(name=segmentationName, user_id=userID)[0]
# Return the Corresponding Segmentation Volume
store = seg.zarr()
return zarr.open(store, mode="r")[0]
def get_ground_truth_coordinates(copickRun, voxelSize, proteinIndex):
"""Get the Ground Truth Coordinates From Copick and Return as a Numpy Array.
Args:
copickRun: Voxel size for the segmentation.
voxelSize: Name of the segmentation.
proteinIndex: Session ID for the segmentation.
Returns:
coords: The newly created segmentation object.
"""
picks = copickRun.picks[proteinIndex]
coords = []
for ii in range(len(picks.points)):
coords.append((
picks.points[ii].location.x / voxelSize,
picks.points[ii].location.y / voxelSize,
picks.points[ii].location.z / voxelSize,
))
return np.array(coords)
def custom_get_copick_protein_coords(
copick_root,
tomo_id,
protein_name,
session_id=None,
user_id="deepfinder",
voxel_spacing=10,
):
"""
Get the coordinates of a specific protein in a tomogram.
Parameters:
copick_root (CopickRoot): The root object of the Copick data structure.
tomo_id (str): The ID of the tomogram.
protein_name (str): The name of the protein.
session_id (str, optional): The ID of the session. Defaults to None.
user_id (str, optional): The ID of the user. Defaults to "deepfinder".
voxel_spacing (int, optional): The voxel spacing. Defaults to 10.
Returns:
numpy.ndarray: An array of coordinates (x, y, z) of the protein. If `session_id` is None, the coordinates of the pick with the highest session ID are returned.
Raises:
ValueError: If no picks match the parameter combination specified.
"""
copick_run = copick_root.get_run(tomo_id)
_pick_idx = None
for _ip, _pick in enumerate(copick_run.picks):
if _pick.pickable_object_name != protein_name or _pick.user_id != user_id:
continue
if session_id is None:
_pick_idx = max(_pick_idx, _ip) if _pick_idx is not None else _ip
elif _pick.session_id == session_id:
_pick_idx = _ip
break
coords = []
if _pick_idx is None:
picked_objects = [_pick.pickable_object_name for _pick in copick_run.picks]
for p in copick_run.picks:
print(p)
if protein_name not in picked_objects:
print(
f"No picks for protein {protein_name} in tomogram {tomo_id} with session_id {session_id}"
)
return np.array(coords).astype("int")
else:
print(
f"Specified parameters: user_id: {user_id}, tomo_id: {tomo_id}, protein_name: {protein_name}, session_id: {session_id}"
)
raise ValueError("No picks match the parameter combination specified.")
_pick = copick_run.picks[_pick_idx]
coords = []
for ii in range(len(_pick.points)):
coords.append((
_pick.points[ii].location.x / voxel_spacing,
_pick.points[ii].location.y / voxel_spacing,
_pick.points[ii].location.z / voxel_spacing,
))
return np.floor(coords).astype("int")
# I need to Figure Out if I want Option 1 or Option 2..
# def get_pickable_object_label(copickRun, objectName):
# for ii in range(len(copickRun.picks)):
# if copickRun.picks[ii].pickable_object_name == objectName:
# return ii
def get_pickable_object_label(copickRoot, objectName):
for ii in range(len(copickRoot.pickable_objects)):
if copickRoot.pickable_objects[ii].name == objectName:
return copickRoot.pickable_objects[ii].label
def read_copick_json(filePath):
"""
Read and processes a copick JSON coordinate file and returns as NumPy array.
Args:
- filePath (str): The path to the JSON file to be read.
Returns:
- np.ndarray: A NumPy array where first three columns are the coordinates (x, y, z), and the next three columns are the Euler angles (in degrees).
Note: X/Y/Z coordinates are returned in Angstroms.
"""
# Load JSON data from a file
with open(os.path.join(filePath), "r") as jFile:
data = json.load(jFile)
# Initialize lists to hold the converted data
coordinates = []
eulerAngles = []
# Loop through each point in the JSON data
for point in data["points"]:
rotationMatrix = []
# Extract the location and convert it to a NumPy array
currLocation = np.array([
point["location"]["x"],
point["location"]["y"],
point["location"]["z"],
])
# Extract the transformation matrix and convert it to a NumPy array
rotationMatrix = R.from_matrix(np.array(point["transformation_"])[:3, :3])
currEulerAngles = rotationMatrix.as_euler("ZYZ", degrees=True)
coordinates.append(currLocation)
eulerAngles.append(currEulerAngles)
return np.hstack((np.array(coordinates), np.array(eulerAngles)))
def convert_copick_coordinates_to_xml(copickRun, xml_objects, pixelSize=10):
picks = copickRun.picks
for proteinLabel in range(len(picks)):
xml_objects.append()
return xml_objects
def write_relion_output(
specimen,
tomoID,
coords,
outputDirectory="refinedCoPicks/ExperimentRuns",
pixelSize=10,
):
"""
Read and processes a copick JSON coordinate file and returns as NumPy array.
Args:
- filePath (str): The path to the JSON file to be read.
Returns:
- np.ndarray: A NumPy array where first three columns are the coordinates (x, y, z), and the next three columns are the Euler angles (in degrees).
Note: X/Y/Z coordinates are returned in Angstroms.
"""
outputStarFile = {}
# Coordinates
if coords.shape[0] > 0:
outputStarFile["rlnCoordinateX"] = coords[:, 0] / pixelSize
outputStarFile["rlnCoordinateY"] = coords[:, 1] / pixelSize
outputStarFile["rlnCoordinateZ"] = coords[:, 2] / pixelSize
# Angles
outputStarFile["rlnAngleRot"] = coords[:, 3]
outputStarFile["rlnAngleTilt"] = coords[:, 4]
outputStarFile["rlnAnglePsi"] = coords[:, 5]
else:
outputStarFile["rlnCoordinateX"] = []
outputStarFile["rlnCoordinateY"] = []
outputStarFile["rlnCoordinateZ"] = []
# Angles
outputStarFile["rlnAngleRot"] = []
outputStarFile["rlnAngleTilt"] = []
outputStarFile["rlnAnglePsi"] = []
# Write
if tomoID == None:
savePath = os.path.join(outputDirectory, f"{specimen}.star")
else:
savePath = os.path.join(outputDirectory, tomoID, f"{tomoID}_{specimen}.star")
starfile.write({"particles": pd.DataFrame(outputStarFile)}, savePath)
def write_copick_output(
specimen,
tomoID,
finalPicks,
outputDirectory="refinedCoPicks/ExperimentRuns",
pickMethod="deepfinder",
sessionID="0",
knownTemplate=False,
):
"""
Writes the output data from 3D protein picking algorithm into a JSON file.
Args:
- specimen (str): The name of the specimen.
- tomoID (str): The ID of the tomogram.
- finalPicks (np.ndarray): An array of final picks, where each row contains coordinates (x, y, z) and Euler angles (rotation, tilt, psi).
- outputDirectory (str): The directory where the output JSON file will be saved.
- pickMethod (str): The method used for picking. Default is 'deepfinder'.
- sessionID (str): The ID of the session. Default is '0'.
- knownTemplate (bool): A flag indicating whether the template is known. Default is False.
"""
# Define the JSON structure
json_data = {
"pickable_object_name": specimen,
"user_id": pickMethod,
"session_id": sessionID,
"run_name": tomoID,
"voxel_spacing": None,
"unit": "angstrom",
}
if not knownTemplate:
json_data["trust_orientation"] = "false"
json_data["points"] = []
for ii in range(finalPicks.shape[0]):
rotationMatrix = convert_euler_to_rotation_matrix(
finalPicks[ii, 3], finalPicks[ii, 4], finalPicks[ii, 5]
)
# Append to points data
json_data["points"].append({
"location": {
"x": finalPicks[ii, 0],
"y": finalPicks[ii, 1],
"z": finalPicks[ii, 2],
},
"transformation_": rotationMatrix, # Convert matrix to list for JSON serialization
"instance_id": 0,
"score": 1.0,
})
# Generate custom formatted JSON
formatted_json = custom_format_json(json_data)
# Save to file
os.makedirs(os.path.join(outputDirectory, tomoID, "Picks"), exist_ok=True)
savePath = os.path.join(
outputDirectory, tomoID, "Picks", f"{pickMethod}_{sessionID}_{specimen}.json"
)
with open(savePath, "w") as json_file:
json_file.write(formatted_json)
def custom_format_json(data):
result = "{\n"
for key, value in data.items():
if key == "points":
result += ' "{}": [\n'.format(key)
for point in value:
result += " {\n"
for p_key, p_value in point.items():
if p_key in ["location", "transformation_"]:
if p_key == "location":
loc_str = ", ".join([
'"{}": {}'.format(k, v) for k, v in p_value.items()
])
result += ' "{}": {{ {} }},\n'.format(p_key, loc_str)
if p_key == "transformation_":
trans_str = ",\n ".join([
"[{}]".format(", ".join(map(str, row))) for row in p_value
])
result += ' "{}": [\n {}\n ],\n'.format(
p_key, trans_str
)
else:
result += ' "{}": {},\n'.format(p_key, json.dumps(p_value))
result = result.rstrip(",\n") + "\n },\n"
result = result.rstrip(",\n") + "\n ]\n"
else:
result += ' "{}": {},\n'.format(key, json.dumps(value))
result = result.rstrip(",\n") + "\n}"
return result
def convert_euler_to_rotation_matrix(angleRot, angleTilt, anglePsi):
"""
Convert Euler angles (rotation, tilt, and psi) in the Relion 'ZYZ' convention into a 4x4 rotation matrix.
Parameters:
- angleRot (float): The rotation angle (in degrees) about the Z-axis.
- angleTilt (float): The tilt angle (in degrees) about the Y-axis.
- anglePsi (float): The psi angle (in degrees) about the Z-axis.
Returns:
- np.ndarray: Rotation matrix.
"""
rotation = R.from_euler("zyz", [angleRot, angleTilt, anglePsi], degrees=True)
rotation_matrix = rotation.as_matrix()
# Append a zero column to the right and zero row at the bottom
new_column = np.zeros((3, 1))
new_row = np.zeros((1, 4))
rotation_matrix = np.vstack((np.hstack((rotation_matrix, new_column)), new_row))
# Set the last element to 1
rotation_matrix[-1, -1] = 1
rotation_matrix = np.round(rotation_matrix, 3)
return rotation_matrix
def ome_zarr_axes() -> List[Dict[str, str]]:
"""
Returns a list of dictionaries defining the axes information for an OME-Zarr dataset.
Returns:
- List[Dict[str, str]]: A list of dictionaries, each specifying the name, type, and unit of an axis.
The axes are 'z', 'y', and 'x', all of type 'space' and unit 'angstrom'.
"""
return [
{
"name": "z",
"type": "space",
"unit": "angstrom",
},
{
"name": "y",
"type": "space",
"unit": "angstrom",
},
{
"name": "x",
"type": "space",
"unit": "angstrom",
},
]
def ome_zarr_transforms(voxel_size: float) -> List[Dict[str, Any]]:
"""
Return a list of dictionaries defining the coordinate transformations of OME-Zarr dataset.
Parameters:
- voxel_size (float): The size of a voxel.
Returns:
- List[Dict[str, Any]]: A list containing a single dictionary with the 'scale' transformation,
specifying the voxel size for each axis and the transformation type as 'scale'.
"""
return [{"scale": [voxel_size, voxel_size, voxel_size], "type": "scale"}]
def write_ome_zarr_segmentation(
run,
inputSegmentVol,
voxelSize=10,
segmentationName="segmentation",
userID="deepfinder",
sessionID="0",
):
"""
Write a OME-Zarr segmentation into a Copick Directory.
Parameters:
- run: The run object, which provides a method to create a new segmentation.
- segmentation: The segmentation data to be written.
- voxelsize (float): The size of the voxels. Default is 10.
"""
# Create a new segmentation or Read Previous Segmentation
try:
seg = run.new_segmentation(
voxel_size=voxelSize,
name=segmentationName,
session_id=sessionID,
is_multilabel=True,
user_id=userID,
)
except:
seg = run.get_segmentations(name=segmentationName, user_id=userID, session_id=sessionID)[0]
# Write the zarr file
loc = seg.zarr()
root_group = zarr.group(loc, overwrite=True)
ome_zarr.writer.write_multiscale(
[inputSegmentVol],
group=root_group,
axes=ome_zarr_axes(),
coordinate_transformations=[ome_zarr_transforms(voxelSize)],
storage_options=dict(chunks=(256, 256, 256), overwrite=True),
compute=True,
)
| Python |
3D | czimaginginstitute/2024_czii_mlchallenge_notebooks | tomotwin_picking_notebook/tomotwin_notebook_helper.py | .py | 14,350 | 447 | import subprocess
import numpy as np
import pandas as pd
from pathlib import Path
def save_mrc(
mic: np.ndarray,
filename: str | Path,
*,
overwrite: bool = True,
voxel_size: list | tuple = None,
):
"""
Save a numpy array to MRC format.
Parameters:
mic (np.ndarray): The numpy array to be saved.
filename (str): The name of the output MRC file.
overwrite (bool, optional): Whether to overwrite the file if it already exists. Defaults to True.
voxel_size (list | tuple, optional): The voxel size of the data. Defaults to None.
Returns:
None
"""
import mrcfile
if mic.dtype != np.dtype("float32"):
mic = mic.astype(np.float32)
with mrcfile.new(str(filename), overwrite=overwrite) as mrc:
mrc.set_data(mic)
if voxel_size:
mrc.voxel_size = voxel_size
return filename
def convert_single_zarr_to_mrc(
*,
zarr_fname: str | Path,
mrc_fname: str | Path,
pyramid_level: int = 0,
voxel_size: list | float = None,
):
"""
Convert a single zarr file to MRC format.
Args:
zarr_fname (str): The path to the zarr file.
mrc_fname (str): The path to save the MRC file.
pyramid_level (int, optional): The pyramid level to extract from the zarr file. Defaults to 0.
voxel_size (list or float, optional): The voxel size of the output MRC file. If a float is provided, it will be
used for all dimensions. If a list of 3 floats is provided, each float will correspond to the voxel size
along the x, y, and z dimensions, respectively. Defaults to None.
Returns:
bool: True if the conversion is successful.
Raises:
ValueError: If voxel_size is not a float or a list of 3 floats.
Note:
We can use this function within a python API to convert a single zarr file to MRC format. For bulk processing,
we can use the `convert_copick_zarrs_to_mrc` function or the command line interface.
"""
import zarr
t1 = zarr.open(
str(zarr_fname),
mode="r",
)
if np.size(voxel_size) == 1:
voxel_size = [voxel_size, voxel_size, voxel_size]
elif np.size(voxel_size) != 3:
raise ValueError("Voxel_size must be a float or a list of 3 floats")
save_mrc(np.array(t1[pyramid_level]), mrc_fname, voxel_size=voxel_size)
return mrc_fname
def run_tomotwin_subolume_extraction(
*,
coords_fname: str | Path,
mrc_fname: str | Path,
out_dir_path: str | Path,
protein_name: str,
):
"""
Runs the TomoTwin subvolume extraction tool.
Args:
coords_fname (str | Path): The filename of the coordinates file in ".coords" format. This is just a csv file with x, y, z coordinates, separated by a space.
mrc_fname (str | Path): The filename of the tomogram MRC file.
out_dir_path (str | Path): The output directory path.
protein_name (str): The name of the protein. This will be used to name the output files.
Raises:
CalledProcessError: If the subprocess command fails.
Returns:
None
"""
command = [
"tomotwin_tools.py",
"extractref",
"--tomo",
str(mrc_fname),
"--coords",
str(coords_fname),
"--out",
str(out_dir_path),
"--filename",
protein_name,
]
command = " ".join(command)
print("Executing command:")
print(command)
subprocess.run(command, check=True, text=True, shell=True)
def run_tomotwin_embedding_calculation(
*,
input_mrc_path: str | Path,
model_path: str | Path,
output_dir_path: str | Path,
stride: int = 2,
batchsize: int = 64,
):
"""
Runs TomoTwin script to calculate embeddings for a given MRC file.
Args:
input_mrc_path (str): The path to the input MRC file.
output_dir_path (str): The directory where the output files will be saved.
model_path (str, optional): The path to the model file. Defaults to "/hpc/projects/group.czii/saugat.kandel/tomotwin_album/tomotwin_latest.pth".
stride (int, optional): The stride value. Defaults to 2.
batchsize (int, optional): The batch size. Defaults to 64.
"""
print("input_mrc_path:", input_mrc_path)
# Construct the command to run the tomoslice.py script
command = [
"tomotwin_embed.py",
"tomogram",
"-m",
str(model_path),
"-v",
str(input_mrc_path),
"-o",
str(output_dir_path),
"-s",
str(stride),
"-b",
str(batchsize),
]
command = " ".join(command)
print("Executing command:")
print(command)
# Execute the command
result = subprocess.run(command, check=True, text=True, shell=True)
print("Script executed successfully.")
print("Output:\n", result.stdout)
def run_tomotwin_subvolume_embedding_calculation(
*, model_path: str | Path, subvolume_mrc_paths: list, out_dir_path: str | Path
):
"""
Runs the TomoTwin subvolume embedding calculation.
Args:
model_path (str): The path to the model file.
subvolume_mrc_paths (list): A list of paths to the subvolume MRC files.
out_dir_path (str): The output directory path.
Raises:
CalledProcessError: If the command execution fails.
"""
command = [
"tomotwin_embed.py",
"subvolumes",
"-m",
str(model_path),
"-v",
" ".join([str(p) for p in subvolume_mrc_paths]),
"-o",
str(out_dir_path),
]
command = " ".join(command)
print("Executing command:")
print(command)
subprocess.run(command, check=True, text=True, shell=True)
def run_tomotwin_similarity_calculation(
*,
ref_embedding_fname: str | Path,
tomo_embedding_fname: str | Path,
out_dir_path: str | Path,
):
"""
Runs TomoTwin script to calculate the per-embedding pairwise similiarity between the reference embeddings and the tomogram volume embeddings.
Args:
ref_embedding_fname (str | Path): File name or path to the reference embedding.
tomo_embedding_fname (str | Path): File name or path to the tomogram embedding.
out_dir_path (str | Path): Directory path to save the output.
Raises:
subprocess.CalledProcessError: If the command execution fails.
"""
command = [
"tomotwin_map.py",
"distance",
"-r",
str(ref_embedding_fname),
"-v",
str(tomo_embedding_fname),
"--out",
str(out_dir_path),
]
command = " ".join(command)
print("Executing command:")
print(command)
subprocess.run(command, text=True, check=True, shell=True)
def run_tomotwin_particle_locator(*, map_fname: str | Path, out_dir_path: str | Path):
"""
Runs TomoTwin script lo locate potential particle locations in the similarity map and output the coordinates.
Args:
map_fname (str or Path): The path to the map file.
out_dir_path (str or Path): The output directory path where the results will be saved.
Raises:
subprocess.CalledProcessError: If the command execution fails.
"""
command = [
"tomotwin_locate.py",
"findmax",
"-m",
str(map_fname),
"-o",
str(out_dir_path),
]
command = " ".join(command)
print("Executing command:")
print(command)
subprocess.run(command, text=True, check=True, shell=True)
def get_particle_slab_projection(
tomo_array_xyz: np.ndarray,
particle_coord_xyz: tuple | list,
*,
x_width: int = 50,
y_width: int = 50,
z_width: int = 30,
projection_axis: str = "z",
):
"""
Retrieves a slab of particles from an MRC file based on the given coordinates and dimensions.
Args:
tomo_array_xyz (np.ndarray): The tomogram array, with the coordinates arranged in xyz format.
coordinate_xyz (tuple | list): The coordinates (x, y, z) of the particle center.
x_width (int, optional): The width of the slab in the x-direction in voxels. Defaults to 50.
y_width (int, optional): The width of the slab in the y-direction in voxels. Defaults to 50.
z_width (int, optional): The width of the slab in the z-direction in voxels. Defaults to 30.
projection_axis (str, optional): The axis along which to project the slab. Defaults to "z".
Returns:
numpy.ndarray: The slab of particles from the MRC file.
"""
x, y, z = particle_coord_xyz
x0, x1 = max(x - x_width // 2, 0), x + x_width // 2
y0, y1 = max(y - y_width // 2, 0), y + y_width // 2
z0, z1 = max(z - z_width // 2, 0), z + z_width // 2
slab = tomo_array_xyz[x0:x1, y0:y1, z0:z1]
projection_axis = projection_axis.lower()
proj_axis_num = {"x": 0, "y": 1, "z": 2}[projection_axis]
projection = np.sum(slab, axis=proj_axis_num)
return projection
def remove_repeated_picks(coordinates: np.ndarray, distance_threshold: float):
"""
Use agglomerative clustering with a distance threshold to identify particle locations that would be considered the same pick.
Args:
coordinates (np.ndarray): Array of coordinates with shape (n, 3), where n is the number of points.
distance_threshold (float): The maximum distance allowed between two points for them to be considered as repeated picks.
Returns:
np.ndarray: Array of mean coordinates for each cluster after removing repeated picks.
"""
from sklearn.cluster import AgglomerativeClustering
agg = AgglomerativeClustering(
n_clusters=None,
distance_threshold=distance_threshold,
linkage="complete",
metric="euclidean",
)
clusters = agg.fit_predict(coordinates)
unique_clusters = np.unique(clusters)
# Initialize an array to store the average of each group
mean_coordinates = np.zeros((unique_clusters.size, coordinates.shape[1]))
# medoid_coordinates = np.zeros((unique_clusters.size, coordinates.shape[1]))
# Calculate the mean and medoid for each cluster
for i, c in enumerate(unique_clusters):
mean_coordinates[i] = np.mean(coordinates[clusters == c], axis=0)
# medoid_coordinates[i] = find_cluster_medoid(coordinates[clusters == c])
return mean_coordinates # , medoid_coordinates
def precision(*, true_positives: int, false_positives: int):
out = np.divide(
true_positives, (true_positives + false_positives + 1e-10), casting="unsafe"
)
return out
def recall(*, true_positives: int, false_negatives: int):
out = np.divide(
true_positives,
(true_positives + false_negatives + 1e-10),
casting="unsafe",
)
return out
def fbeta_score(
*, true_positives: int, false_positives: int, false_negatives: int, beta: float = 1
):
num = (1 + beta**2) * true_positives
denom = (1 + beta**2) * true_positives + beta**2 * false_negatives + false_positives
out = np.divide(num, denom + 1e-10, casting="unsafe")
return out
def calculate_metrics(
*,
gt_picks_df: pd.DataFrame,
tomotwin_picks_df: pd.DataFrame,
class_prediction_threshold: float,
connected_size_threshold: int,
gt_threshold_distance: float,
fp_repeat_threshold_distance: float,
):
"""
Calculate various metrics for evaluating the performance of a prediction algorithm.
Parameters:
- gt_picks_df (pd.DataFrame): DataFrame containing coordinates for the ground truth picks.
- tomotwin_picks_df (pd.DataFrame): DataFrame containing the predicted picks and their metrics.
- class_prediction_threshold (float): Threshold for class prediction.
- connected_size_threshold (int): Threshold for connected size.
- gt_threshold_distance (float): Threshold distance for ground truth picks.
- fp_repeat_threshold_distance (float): Threshold distance for removing repeated false positives.
Returns:
- output (dict): Dictionary containing the calculated metrics:
- "true_positives" (pd.DataFrame): DataFrame of true positive picks.
- "false_negatives" (pd.DataFrame): DataFrame of false negative picks.
- "false_positives" (pd.DataFrame): DataFrame of false positive picks.
- "precision" (float): Precision score.
- "recall" (float): Recall score.
- "f10" (float): F-beta score with beta=10.
"""
from cupyx.scipy import spatial
# This is the default output if there are no predicted positives
output = {
"true_positives": None,
"false_negatives": gt_picks_df,
"false_positives": None,
"precision": 0,
"recall": 0,
"f10": 0,
}
predicted_positives = tomotwin_picks_df[
(tomotwin_picks_df["metric_best"] > class_prediction_threshold)
& (tomotwin_picks_df["size"] > connected_size_threshold)
]
if predicted_positives.shape[0] < 1:
return output
# running this on the gpu gives a significant speedup
dist_matrix = spatial.distance.cdist(
predicted_positives[["X", "Y", "Z"]],
gt_picks_df[["X", "Y", "Z"]],
"euclidean",
).get()
false_negatives = gt_picks_df[np.min(dist_matrix, axis=0) >= gt_threshold_distance]
true_positives = gt_picks_df[np.min(dist_matrix, axis=0) < gt_threshold_distance]
false_positives_all = predicted_positives[
np.min(dist_matrix, axis=1) >= gt_threshold_distance
]
if false_positives_all.shape[0] < 3:
false_positives_means = false_positives_all
else:
false_positives_means = remove_repeated_picks(
false_positives_all[["X", "Y", "Z"]].values, fp_repeat_threshold_distance
)
# Precision, Recall, F1 Score
tp = len(true_positives)
fp = len(false_positives_means)
fn = len(false_negatives)
output = {
"true_positives": true_positives,
"false_negatives": false_negatives,
"false_positives": false_positives_means,
"precision": precision(true_positives=tp, false_positives=fp),
"recall": recall(true_positives=tp, false_negatives=fn),
"f10": fbeta_score(
true_positives=tp, false_positives=fp, false_negatives=fn, beta=10
),
}
return output
| Python |
3D | czimaginginstitute/2024_czii_mlchallenge_notebooks | tomotwin_picking_notebook/tomotwin_notebook_inference.ipynb | .ipynb | 396,036 | 1,052 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# <span style=\"color:#cc241d\">Using TomoTwin for particle picking</span>\n",
"\n",
"TomoTwin is an embedding-based cryo-ET particle picking procedure for cryo-ET [[1](https://tomotwin-cryoet.readthedocs.io/en/stable/index.html)] [[2](https://doi.org/10.1038/s41592-023-01878-z)]. TomoTwin was originally designed to identify proteins in a single tomogram de novo without manually creating training data. Since this workflow is not optimized for large-throughput data analysis of hundreds of tomograms, we modify it slightly to use some manually annotated tomograms as training for subsequent particle picking in unseen tomograms. This is essentially a two step procedure:\n",
"\n",
"1. From the particle picks in the annotated tomograms, identify a few `reference` particles for each particle type of interest, such as for ribosomes, apo-ferritins, etc.\n",
"2. Use the embeddings of the `reference` particles for similarity-based annotation in unseen tomograms.\n",
"\n",
"In this notebook, we provide an example of a workflow to pick ribosomes from unseen tomograms.\n",
"We divide this notebook into the following sections:\n",
"\n",
"- [Section 0](#install): Install and setup.\n",
"- [Section 1](#section1): Data preprocessing and embedding calculation.\n",
"- [Section 2](#section2): Similarity-based particle picking.\n",
"- [Section 3](#section3): Refining and analyzing the picks.\n",
"- [Section 4](#observations): Observations\n",
"\n",
"The inputs and outputs are as follows: \n",
"**Inputs**:\n",
"\n",
"- Tomograms (>1) stored using the copick data model. Thhe copick data model is described in the Copick documentation [[3]](https://uermel.github.io/copick/).\n",
"- Ribosome annotations for one of the tomograms, stored in copick format. We refer to the remaining tomograms as `unseen` tomograms.\n",
"- TomoTwin Pytorch model.\n",
"- Optionally, ribosome annotations for the unseen tomograms.\n",
"\n",
"**Outputs**:\n",
"\n",
"- Proposed ribosome annotations for the remaining tomograms.\n",
"- Optionally, if the annotations for the unseen tomgorams are provided, then we also calculate the evaluation metrics for the unseen tomograms.\n",
"\n",
"\\[1\\] [Tomotwin's User Guide](https://tomotwin-cryoet.readthedocs.io/en/stable/index.html) \n",
"\\[2\\] [Tomotwin paper](https://doi.org/10.1038/s41592-023-01878-z) \n",
"\\[3\\] [Copick documentation](https://uermel.github.io/copick/)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"vscode": {
"languageId": "plaintext"
}
},
"source": [
"<a id='install'></a>\n",
"\n",
"## <span style=\"color:#d65d0e\">Install and setup</span>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <span style=\"color:#d79921\">Installation</span>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are two ways to install the packages required for this notebook: directly installing all the packages using a conda environment file, or a custom step-by-step installation.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### <span style=\"color:#689d6a\"> Option 1: direct install </span>\n",
"\n",
"Download the provided `tomotwin_copick_environment.yml` file and run\n",
"\n",
"```bash\n",
"mamba env create -f tomotwin_copick_environment.yml\n",
"mamba activate tomotwin_copick\n",
"```\n",
"\n",
"This might not work if Cuda version is different from 12.0, or for some version issues. If this does not work, try the custom install option.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### <span style=\"color:#689d6a\">Option 2: custom install</span>\n",
"\n",
"- Go to the [RapidsAI install page](https://docs.rapids.ai/install). Select the appropriate CUDA version along with the options `Conda`, `Python 3.11`, `Standard`, `JupyterLab`, and `PyTorch`. Copy and modify the command thus generated. The modified command might look like:\n",
"\n",
"```bash\n",
"mamba create -n tomotwin2 -c rapidsai -c conda-forge -c nvidia \\\n",
" rapids=24.06 python=3.11 cuda-version=12.0 \\\n",
" jupyterlab pytorch\n",
"```\n",
"\n",
"- Activate the new environment:\n",
"\n",
"```bash\n",
"mamba activate tomotwin2\n",
"```\n",
"\n",
"- Install other conda packages:\n",
"\n",
"```bash\n",
"mamba install numpy scikit-learn scikit-image pytorch-metric-learning \\\n",
" numba tabulate tqdm ipywidgets zarr ome-zarr fsspec trimesh matplotlib \\\n",
" mrcfile pydantic starfile pytorch::torchtriton\n",
"```\n",
"\n",
"There could be some redundancies in packages listed here.\n",
"\n",
"- Install necessary pypi packages:\n",
"\n",
"```bash\n",
"pip install tomotwin-cryoet --no-deps\n",
"pip install copick --no-deps\n",
"```\n",
"\n",
"The \"--no-deps\" part is to remove any conflict between the stated dependencies in tomotwin-cryoet and copick libraries with the previously installed packages.\n",
"\n",
"We have verified that this workflow works for Python 3.11 and CUDA 12.0, but this might need modification for other setups. Note that when copick is installed this way, it can only be used for the data access, not for the ChimeraX visualization.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <span style=\"color:#d79921\">Downloading the TomoTwin embedding model</span>\n",
"\n",
"The TomoTwin embeddings are calculated using a pre-trained Pytorch model provided by the TomoTwin developers. The latest model is available to download from https://zenodo.org/records/8358240. The path to the model is used in the subsequent processing.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <span style=\"color:#d79921\">Imports and configurations</span>\n",
"\n",
"Setting up the imports and parameters used broadly in the subsequent sections. These parameters are based on a few assumptions about the copick data structure.\n",
"\n",
"**Assumptions**\n",
"\n",
"1. The copick directory structure is described in the file `filesystem_overlay_only.json`\n",
"2. It contains tomograms with voxel size 10 A.\n",
"3. It contains weighted-back-projected tomograms under the `denoised` tomogram type. Optionally,the notebook also uses the `denoised` tomo type to visualize some reference and picked particles.\n",
"\n",
"Note that we choose the tomograms generated from weighted backprojection since some limited exploratory tests showed that TomoTwin performed well with these among our available reconstructions. We recommend analysis of more data and multiple reconstruction approaches for practical applications of this, or any such similar, workflow.\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"cur_dir = (\n",
" \"/home/saugat.kandel/projects_sk/code/2024_czii_mlchallenge_notebooks/tomotwin_picking_notebook\"\n",
")\n",
"\n",
"os.chdir(cur_dir)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"from copick.impl.filesystem import CopickRootFSSpec\n",
"\n",
"import copick_tools\n",
"import tomotwin_notebook_helper as helper\n",
"from copy import deepcopy"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"# Parameter setup for the notebook\n",
"\n",
"# This is the list of tomograms used in the notebook.\n",
"# TS_99_9 is used as the reference, and the other two are used as test tomograms.\n",
"#\n",
"tomo_list = [\"TS_99_9\", \"TS_73_6\", \"TS_5_4\"]\n",
"data_type = \"denoised\"\n",
"voxel_size = 10.0\n",
"\n",
"copick_root_path = Path(\n",
" \"/hpc/projects/group.czii/mlchallenge_202406_curation/ml_curated_pickathon_June2024/\"\n",
")\n",
"overlay_fname = \"filesystem_overlay_only.json\"\n",
"copick_root = CopickRootFSSpec.from_file(copick_root_path / overlay_fname)\n",
"\n",
"# Setting up the directory for the TomoTwin embeddings\n",
"tomotwin_root = Path(\"embeddings\")\n",
"tomotwin_root.mkdir(exist_ok=True)\n",
"\n",
"# Choosing the tomogram to use as \"training\" reference.\n",
"ref_tomo = \"TS_99_9\"\n",
"test_tomos = [\"TS_73_6\", \"TS_5_4\"]\n",
"\n",
"protein = \"ribosome\"\n",
"protein_diameters = {\"ribosome\": 320 / voxel_size}\n",
"\n",
"out_dir_base = Path(cur_dir)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='section1'></a>\n",
"\n",
"## <span style=\"color:#d65d0e\">Data preprocessing and embedding calculation</span>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <span style=\"color:#d79921\">Converting tomograms from `OME-ZARR` format to `.mrc` files:</span>\n",
"\n",
"The provied tomograms are stored in OME-ZARR format with the copick directory structure. Since Tomotwin only reads tomograms in MRC format, we have to convert these tomograms.\n",
"\n",
"**Input**:\n",
"\n",
"- Copick denoised tomograms.\n",
"\n",
"**Output**:\n",
"\n",
"- Individual mrc files in directory `mrc_phantom/denoised` as `{tomo_name}_denoised.mrc`.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Conversion script. This should take only a few seconds.\n",
"\n",
"out_dir = out_dir_base / f\"mrc_phantom/{data_type}\"\n",
"out_dir.mkdir(exist_ok=True, parents=True)\n",
"\n",
"mrc_paths = {}\n",
"for tomo in tomo_list:\n",
" print(\"Converting \", tomo)\n",
" tomo_array = (\n",
" copick_root.get_run(tomo).get_voxel_spacing(voxel_size).get_tomogram(data_type).numpy()\n",
" )\n",
"\n",
" out_mrc_fname = out_dir / f\"{tomo}_{data_type}.mrc\"\n",
" helper.save_mrc(tomo_array, out_mrc_fname, voxel_size=voxel_size)\n",
" mrc_paths[tomo] = out_mrc_fname"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <span style=\"color:#d79921\">Extracting the \"subvolumes\" around selected reference particles from the annotated tomogram.</span>\n",
"\n",
"We use the tomogram TS_115_4 as the reference tomogram from which the reference particles are extracted. The tomogram contains 82 ribosome --- using each of these particles as references for subsequent processing would require a lot of computation, but our exploratory results showed that the overall particle picking did not improve when we used more than about 10-15 particles. We can reduce the particle count using many approaches, of which we discuss two:\n",
"\n",
"1. Random selection of a predefined number particles. We follow the random selection approach in this notebook.\n",
"2. Using a dimensionality reduction and clustering based approach using the `napari-tomotwin` plugin for the Napari GUI. The [tomotwin tutorial](https://tomotwin-cryoet.readthedocs.io/en/stable/tutorials/tutorials_overview.html#tutorial-2-clustering-based-particle-picking) discusses this approach. We were unable to get the `napari-tomotwin` plugin during the time of writing this notebook --- the plugin authors are looking into this.\n",
"\n",
"**Input**:\n",
"\n",
"- Locations of the centers of the ribosomes for the reference tomogram. This should be in copick format.\n",
"\n",
"**Outputs**:\n",
"\n",
"- Individual mrc files containing the `37 X 37 X 37 voxels` subvolume around 10 randomly selected reference particles. They are stored in the directory `embeddings/denoised/references/ribosome/`.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### <span style=\"color:#689d6a\"> Selecting reference particles </span>\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"# Getting the ribosome coordinates from the copick filesystem\n",
"reference_ribo_coords_xyz = copick_tools.custom_get_copick_protein_coords(\n",
" copick_root=copick_root, tomo_id=ref_tomo, protein_name=\"ribosome\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"# Random selection of the reference particles\n",
"rng = np.random.default_rng(10)\n",
"reference_ribo_coords_xyz = rng.choice(reference_ribo_coords_xyz, 10, replace=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### <span style=\"color:#689d6a\">Visualizing the denoised z-projection (of width 300A = 30 voxels) of the reference particles </span>\n",
"\n",
"It is hard to see the particles in the denoised tomogram, so we use the denoised tomogram for the visualization.\n"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"# First retrieve the corresponding denoised mrc file\n",
"\n",
"tomo_array = (\n",
" copick_root.get_run(ref_tomo).get_voxel_spacing(voxel_size).get_tomogram(data_type).numpy()\n",
")\n",
"tomo_array_xyz = tomo_array.transpose(2, 1, 0)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1000x400 with 10 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Extract the reference projections and plot them\n",
"\n",
"reference_projections = [\n",
" helper.get_particle_slab_projection(tomo_array_xyz=tomo_array_xyz, particle_coord_xyz=coords)\n",
" for coords in reference_ribo_coords_xyz\n",
"]\n",
"\n",
"fig, axs = plt.subplots(2, 5, figsize=(10, 4))\n",
"for i, ax in enumerate(axs.flat):\n",
" ax.imshow(reference_projections[i], cmap=\"gray\")\n",
" ax.axis(\"off\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <span style=\"color:#689d6a\">Extracting the particle subvolumes from the denoised tomograms</span>\n",
"\n",
"1. First save the coordinates as a `.coords` file.\n",
"2. Extract a 37 x 37 x 37 voxel subvolume centered at each particle using TomoTwin. The subvolume size is a TomoTwin default. This step is related to [Step 2](https://tomotwin-cryoet.readthedocs.io/en/stable/tutorials/tutorials_overview.html#tutorial-1-reference-based-particle-picking) in the TomoTwin tutorial.\n",
"3. Store the subvolumes in the directory structure \n",
" `embeddings/denoised/references/ribosome/`\n"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"# Storing the reference ribosome coordinates\n",
"ref_ribo_coords_out_dir = tomotwin_root / data_type / \"references/ribosome\"\n",
"ref_ribo_coords_out_dir.mkdir(exist_ok=True, parents=True)\n",
"\n",
"ref_ribo_coords_out_fname = ref_ribo_coords_out_dir / f\"{ref_tomo}.coords\"\n",
"\n",
"np.savetxt(ref_ribo_coords_out_fname, reference_ribo_coords_xyz, delimiter=\" \")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Extracting the subvolumes for the particles. This should take a minute or less.\n",
"# If tomotwin exits with a error about how the file already exists, then try removing any .mrc files {ref_ribo_coords_out} directory.\n",
"# If there is a \"command not found\" error, then try running the command in the terminal in the current directory and with the tomotwin_copick conda environment.\n",
"subvolume_out_path = tomotwin_root / data_type / \"subvolumes\"\n",
"helper.run_tomotwin_subolume_extraction(\n",
" coords_fname=ref_ribo_coords_out_fname,\n",
" mrc_fname=mrc_paths[ref_tomo],\n",
" out_dir_path=ref_ribo_coords_out_dir,\n",
" protein_name=\"ribosome\",\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### <span style=\"color:#d79921\">Calculating the embeddings for the test tomogram volumes and the reference particle subvolumes:</span>\n",
"\n",
"We generate the embeddings using the TomoTwin command line interface, following [Steps 3 and 4](https://tomotwin-cryoet.readthedocs.io/en/stable/tutorials/tutorials_overview.html#embed-your-tomogram) in the TomoTwin Tutorial 1. The important parameter here is the \"stride\". For the tomograms, we follow the tomotwin tutorial and use a strid of 2, thereby calculating the embedding for every other voxel in the tomogram volume.\n",
"\n",
"It takes about an hour to calculate the embeddings for a single tomogram in a single A40 GPU. The embeddings for different tomograms can be calculated in parallel with multiple GPUs or cluster nodes by running the command in the terminal, as shown in the [Tomotwin tutorial](https://tomotwin-cryoet.readthedocs.io/en/stable/tutorials/tutorials_overview.html). The tutorial also contains some strategies to accelerate the embedding calculation.\n",
"\n",
"**Input**:\n",
"\n",
"- The path to the tomotwin embedding model.\n",
"- Tomogram mrc files stored in `mrc_phantom/denoised/{tomo_name}_denoised.mrc`\n",
"- Reference particle subvolume mrcs stored as ``embeddings/denoised/references/ribosome/*.mrc`.\n",
"\n",
"**Outputs**:\n",
"\n",
"- Tomogram embeddings are stored as `embeddings/denoised/tembs/{tomo_name}_denoised.temb`.\n",
"- Embeddings for the all the reference ribosomes are concatenated and stored in `embeddings/denoised/references/ribosome/embeddings.temb`.\n",
"\n",
"The embeddings files are pandas dataframes stored in pickle format.\n"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"model_path = \"/hpc/projects/group.czii/saugat.kandel/tomotwin_album/tomotwin_latest.pth\""
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PosixPath('/home/saugat.kandel/projects_sk/code/2024_czii_mlchallenge_notebooks/tomotwin_picking_notebook/mrc_phantom/denoised/TS_99_9_denoised.mrc')"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_mrc_paths = deepcopy(mrc_paths)\n",
"test_mrc_paths.pop(ref_tomo)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Calculating the embeddings for the tomograms\n",
"for tomo, mrc_path in test_mrc_paths.items():\n",
" out_dir_path = tomotwin_root / data_type / \"tembs\"\n",
" helper.run_tomotwin_embedding_calculation(\n",
" input_mrc_path=mrc_path, model_path=model_path, output_dir_path=out_dir_path\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Calculating the embeddings for the subvolumes\n",
"subvolume_mrc_paths = list(ref_ribo_coords_out_dir.glob(\"*.mrc\"))\n",
"print(subvolume_mrc_paths)\n",
"helper.run_tomotwin_subvolume_embedding_calculation(\n",
" model_path=model_path,\n",
" subvolume_mrc_paths=subvolume_mrc_paths,\n",
" out_dir_path=ref_ribo_coords_out_dir,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='section2'></a>\n",
"\n",
"## <span style=\"color:#d65d0e\"> Similarity-based particle picking </span>\n",
"\n",
"Similarity-based particle picking is a two step process:\n",
"\n",
"1. similarity score calculation.\n",
"2. locating potential particle positions.\n",
"\n",
"These steps follow Steps 5 and 6 in the [TomoTwin tutorial](https://tomotwin-cryoet.readthedocs.io/en/stable/tutorials/tutorials_overview.html).\n",
"\n",
"**Inputs**:\n",
"\n",
"- Individual tomogram embeddings stored as `embeddings/denoised/tembs/{tomo_name}_denoised.temb`.\n",
"- Refererence ribosome embeddings stored in `embeddings/denoised/references/ribosome/embeddings.temb`\n",
"\n",
"**Outputs**:\n",
"\n",
"- The similarity scores calculated for each tomogram are stored in `embeddings/denoised/maps/{tomo_name}/ribosome/map.tmap`.\n",
"- The potential particle picks for each tomogram are stored in `embeddings/denoised/maps/{tomo_name}/ribosome/located.tloc`.\n",
"\n",
"The map and location files are both pandas dataframes stored in pickle format.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### <span style=\"color:#d79921\">Calculating the similarity score map</span>\n",
"\n",
"We compare the reference particle embedding to the each embedding of the tomogram volume embedding to generate a per-particle similarity score map for the tomogram volume. This involves a pairwise similarity calculation between each pair of the reference embeddings and voxel-wise embeddings of the tomogram volume.\n"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"tomo_embedding_paths = {\n",
" tomo: tomotwin_root / data_type / \"tembs\" / f\"{tomo}_{data_type}_embeddings.temb\"\n",
" for tomo in test_tomos\n",
"}\n",
"assert all([path.exists() for path in tomo_embedding_paths.values()])"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"ref_embedding_path = tomotwin_root / data_type / \"references/ribosome/embeddings.temb\"\n",
"assert ref_embedding_path.exists()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"base_map_out_dir = tomotwin_root / data_type / \"maps\"\n",
"base_map_out_dir.mkdir(exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'TS_73_6': PosixPath('embeddings/denoised/tembs/TS_73_6_denoised_embeddings.temb'),\n",
" 'TS_5_4': PosixPath('embeddings/denoised/tembs/TS_5_4_denoised_embeddings.temb')}"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tomo_embedding_paths"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Embedding for TS_73_6 is stored at embeddings/denoised/tembs/TS_73_6_denoised_embeddings.temb\n",
"Similarity map for TS_73_6 is stored in embeddings/denoised/maps/TS_73_6\n",
"Embedding for TS_5_4 is stored at embeddings/denoised/tembs/TS_5_4_denoised_embeddings.temb\n",
"Similarity map for TS_5_4 is stored in embeddings/denoised/maps/TS_5_4\n"
]
}
],
"source": [
"# Running the similarity map calculations\n",
"\n",
"ribo_map_out_dirs = {}\n",
"for tomo, emb_path in tomo_embedding_paths.items():\n",
" print(f\"Embedding for {tomo} is stored at {emb_path}\")\n",
" map_out_dir = base_map_out_dir / tomo\n",
" map_out_dir.mkdir(exist_ok=True)\n",
"\n",
" ribo_map_out_dir = map_out_dir / \"ribosome\"\n",
" ribo_map_out_dir.mkdir(exist_ok=True)\n",
"\n",
" print(f\"Similarity map for {tomo} is stored in {map_out_dir}\")\n",
" helper.run_tomotwin_similarity_calculation(\n",
" ref_embedding_fname=ref_embedding_path,\n",
" tomo_embedding_fname=emb_path,\n",
" out_dir_path=ribo_map_out_dir,\n",
" )\n",
" ribo_map_out_dirs[tomo] = ribo_map_out_dir"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### <span style=\"color:#d79921\">Calculating the potential particle locations</span>\n",
"\n",
"A TomoTwin tool uses local maxima finding and non-maximum suppression procedures to identify potential particle locations. The output dataframe (in each `located.tloc` file) contains a list of potential particle coordinates along with a `best_metric` and `size` parameter per coordinate. The `best_metric` indicates the distance between the embedding for the voxel and the ribosome that is closest to it in embedding space. The `size` metric is related to the number of other ribosome-like voxels in the neighborhood of the flagged partice location.\n",
"\n",
"The output also contains the class id for each particle --- we ignore this since all of our classes are ribosomes.\n",
"\n",
"Note that this maxima-based picking also could also flag identify any fluctuations in the similarity map, even in regions with low similarity values, as potential particle locations. We therefore need to refine the particles based on the similarity metric and size to remove such false positives.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Running the particle location calculations\n",
"\n",
"for tomo, map_out_dir in ribo_map_out_dirs.items():\n",
" map_fname = map_out_dir / \"map.tmap\"\n",
" helper.run_tomotwin_particle_locator(map_fname=map_fname, out_dir_path=map_out_dir)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='section3'></a>\n",
"\n",
"## <span style=\"color:#d65d0e\"> Refining and analyzing the particle picks </span>\n",
"\n",
"**Inputs**:\n",
"\n",
"- Per-tomogram information for potential particles. These are extracted from the `embeddings/denoised/maps/{tomo_name}/ribosome/located.tloc` files.\n",
"- If available, ground truth ribosome annotations (with particle center coordinates) for the test tomograms. This should be in copick format.\n",
"\n",
"**Outputs**:\n",
"\n",
"- Refined (or filtered) particle coordinate picks.\n",
"- If the ground truths are available, we can output the classification metrics evaluating the picked particles.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <span style=\"color:#689d6a\">Refining the picks </span>\n",
"\n",
"The potential particle picks can be refined by tuning the thresholds for the similarity metric and size criteria per protein. The tomotwin tutorial suggests tuning these using the `napari-tomotwin` GUI, but we were unable to get the GUI running. Instead, we used a grid search to picked thresholds that maximize the $f_\\beta (\\beta = 10)$ score (https://scikit-learn.org/stable/modules/generated/sklearn.metrics.fbeta_score.html). THe $\\beta=10$ choice assumes that recall is significantly more important than precision --- it is much easier to remove false positives as ribosomes than to identify false negatives. Of course, this assumes that the number of false positives is not so high that examining each false positive is infeasible.\n",
"\n",
"We emphasize that the optimal values could depend on the specific tomogram, the data type, and other considerations.\n",
"\n",
"We can also change these thresholds to experiment with how they affect the picks.\n"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [],
"source": [
"# We did some tuning on test data and found that these numbers work wwell for maximal reduction of false negatives\n",
"class_prediction_threshold = 0.97\n",
"connected_size_threshold = 30"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [],
"source": [
"location_fnames = {\n",
" tomo: f\"embeddings/denoised/maps/{tomo}/ribosome/located.tloc\" for tomo in test_tomos\n",
"}\n",
"tomotwin_picks_dfs = {tomo: pd.read_pickle(location_fnames[tomo]) for tomo in test_tomos}"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Went from 27776 particles to 2202 particles in TS_73_6.\n",
"Went from 29385 particles to 2045 particles in TS_5_4.\n"
]
}
],
"source": [
"predicted_positives = {}\n",
"for tomo in test_tomos:\n",
" tomo_df = tomotwin_picks_dfs[tomo]\n",
" positives = tomo_df[\n",
" (tomo_df[\"metric_best\"] > class_prediction_threshold)\n",
" & (tomo_df[\"size\"] > connected_size_threshold)\n",
" ]\n",
" predicted_positives[tomo] = positives\n",
"\n",
" print(f\"Went from {len(tomo_df)} particles to {len(positives)} particles in {tomo}.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <span style=\"color:#d65d0e\"> Visualizing the refined picks </span>\n",
"\n",
"The refinement process has reduced the number of particle picks. We can visualize some randomly selected picks to see how well the refinement process has worked.\n"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [],
"source": [
"rng = np.random.default_rng(10)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1000x400 with 10 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1000x400 with 10 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Random selection of the reference particles\n",
"\n",
"\n",
"for tomo in predicted_positives:\n",
" selected_picks = rng.choice(\n",
" predicted_positives[tomo][[\"X\", \"Y\", \"Z\"]].values, 10, replace=False\n",
" )\n",
"\n",
" # the located coordinates are often subpixel, so we need to round them to the nearest integer\n",
" selected_picks = np.floor(selected_picks).astype(int)\n",
"\n",
" tomo_array = copick_tools.get_copick_tomogram(\n",
" copick_root, voxelSize=voxel_size, tomoAlgorithm=\"denoised\", tomoID=tomo\n",
" )\n",
" tomo_array_xyz = np.transpose(tomo_array, (2, 1, 0))\n",
"\n",
" # Extract the reference projections and plot them\n",
"\n",
" reference_projections = [\n",
" helper.get_particle_slab_projection(\n",
" tomo_array_xyz=tomo_array_xyz, particle_coord_xyz=coords\n",
" )\n",
" for coords in selected_picks\n",
" ]\n",
" fig, axs = plt.subplots(\n",
" 2,\n",
" 5,\n",
" figsize=(10, 4),\n",
" )\n",
" for i, ax in enumerate(axs.flat):\n",
" ax.imshow(reference_projections[i], cmap=\"gray\")\n",
" ax.axis(\"off\")\n",
" plt.suptitle(f\"Randomly selected picks for {tomo}\")\n",
" plt.tight_layout()\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <span style=\"color:#d65d0e\"> Evaluating the picks </span>\n",
"\n",
"The figures above should show that some picks that do not look like ribosomes. For annotated tomograms, we can retrieve the ground truth particles to quantitatively evaluate the picking.\n"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [],
"source": [
"# First getting the ground truth ribosome locations for the test tomograms\n",
"gt_coords = {}\n",
"for tomo in test_tomos:\n",
" coords_xyz = copick_tools.custom_get_copick_protein_coords(copick_root, tomo, \"ribosome\")\n",
" gt_coords[tomo] = pd.DataFrame(coords_xyz, columns=[\"X\", \"Y\", \"Z\"])"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Calculating metrics for TS_73_6\n",
"Calculating metrics for TS_5_4\n"
]
}
],
"source": [
"classification_metrics = {}\n",
"for tomo in test_tomos:\n",
" print(f\"Calculating metrics for {tomo}\")\n",
" classification_metrics[tomo] = helper.calculate_metrics(\n",
" gt_picks_df=gt_coords[tomo],\n",
" tomotwin_picks_df=tomotwin_picks_dfs[tomo],\n",
" class_prediction_threshold=class_prediction_threshold,\n",
" connected_size_threshold=connected_size_threshold,\n",
" gt_threshold_distance=protein_diameters[protein] / 2,\n",
" fp_repeat_threshold_distance=protein_diameters[protein],\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Metrics for TS_73_6\n",
"Number of true positives: 69\n",
"Number of false positives: 520\n",
"Number of false negatives: 4\n",
"Precision: 0.12\n",
"Recall: 0.95\n",
"\n",
"Metrics for TS_5_4\n",
"Number of true positives: 36\n",
"Number of false positives: 553\n",
"Number of false negatives: 9\n",
"Precision: 0.06\n",
"Recall: 0.80\n",
"\n"
]
}
],
"source": [
"for tomo in test_tomos:\n",
" print(f\"Metrics for {tomo}\")\n",
" print(\n",
" \"Number of true positives: \",\n",
" len(classification_metrics[tomo][\"true_positives\"]),\n",
" )\n",
" print(\n",
" \"Number of false positives: \",\n",
" len(classification_metrics[tomo][\"false_positives\"]),\n",
" )\n",
" print(\n",
" \"Number of false negatives: \",\n",
" len(classification_metrics[tomo][\"false_negatives\"]),\n",
" )\n",
" print(f\"Precision: {classification_metrics[tomo]['precision']: 3.2f}\")\n",
" print(f\"Recall: {classification_metrics[tomo]['recall']: 3.2f}\")\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='section4'></a>\n",
"\n",
"## <span style=\"color:#d65d0e\"> Observations </span>\n",
"\n",
"Particle picking is hard!\n",
"\n",
"Tomotwin is a powerful state-of-the-art approach for de novo, interactive, particle picking for cryo-EM tomograms. When we adopt it for ribosome picking in high-throughput non-interactive particle picking, we can identify some ribosomes, but we also miss some, and we generate a lot of false positives. In our experience, picking small particles is even more difficult.\n",
"\n",
"We want to emphasize that this notebook is only an example of a particle picking workflow, and is not expected to be the gold standard for Tomotwin-based workflows, let alone for a broader variety of workflows. We hope that other approaches can greatly simplify and improve upon these workflows. For now, however, particle picking is hard!\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "tomotwin_copick",
"language": "python",
"name": "tomotwin_copick"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| Unknown |
3D | czimaginginstitute/2024_czii_mlchallenge_notebooks | 3d_unet_monai/train.ipynb | .ipynb | 589,540 | 2,500 | {
"cells": [
{
"cell_type": "markdown",
"id": "d8cbcb34-b7a9-4bd3-af85-6263ac3ee83d",
"metadata": {},
"source": [
"# Training 3D U-Net model for multi-class semantic segmentation"
]
},
{
"cell_type": "markdown",
"id": "936c827d-9270-4137-8049-3b2b8845f6f1",
"metadata": {},
"source": [
"### Install pkgs"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "922dd5d3-7999-47b2-affe-de4794faa2ed",
"metadata": {},
"outputs": [],
"source": [
"#!pip install git+https://github.com/copick/copick-utils.git matplotlib tqdm copick \n",
"#!pip install -q \"monai-weekly[mlflow]\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6148c6d2",
"metadata": {},
"outputs": [],
"source": [
"# Make a copick project\n",
"import os\n",
"import shutil\n",
"\n",
"config_blob = \"\"\"{\n",
" \"name\": \"czii_cryoet_mlchallenge_2024\",\n",
" \"description\": \"2024 CZII CryoET ML Challenge training data.\",\n",
" \"version\": \"1.0.0\",\n",
"\n",
" \"pickable_objects\": [\n",
" {\n",
" \"name\": \"apo-ferritin\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"4V1W\",\n",
" \"label\": 1,\n",
" \"color\": [ 0, 117, 220, 128],\n",
" \"radius\": 60,\n",
" \"map_threshold\": 0.0418\n",
" },\n",
" {\n",
" \"name\": \"beta-galactosidase\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"6X1Q\",\n",
" \"label\": 3,\n",
" \"color\": [ 76, 0, 92, 128],\n",
" \"radius\": 90,\n",
" \"map_threshold\": 0.0578\n",
" },\n",
" {\n",
" \"name\": \"ribosome\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"6EK0\",\n",
" \"label\": 4,\n",
" \"color\": [ 0, 92, 49, 128],\n",
" \"radius\": 150,\n",
" \"map_threshold\": 0.0374\n",
" },\n",
" {\n",
" \"name\": \"thyroglobulin\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"6SCJ\",\n",
" \"label\": 5,\n",
" \"color\": [ 43, 206, 72, 128],\n",
" \"radius\": 130,\n",
" \"map_threshold\": 0.0278\n",
" },\n",
" {\n",
" \"name\": \"virus-like-particle\",\n",
" \"is_particle\": true,\n",
" \"label\": 6,\n",
" \"color\": [255, 204, 153, 128],\n",
" \"radius\": 135,\n",
" \"map_threshold\": 0.201\n",
" },\n",
" {\n",
" \"name\": \"membrane\",\n",
" \"is_particle\": false,\n",
" \"label\": 8,\n",
" \"color\": [100, 100, 100, 128]\n",
" },\n",
" {\n",
" \"name\": \"background\",\n",
" \"is_particle\": false,\n",
" \"label\": 9,\n",
" \"color\": [10, 150, 200, 128]\n",
" }\n",
" ],\n",
"\n",
" \"overlay_root\": \"/kaggle/working/overlay\",\n",
"\n",
" \"overlay_fs_args\": {\n",
" \"auto_mkdir\": true\n",
" },\n",
"\n",
" \"static_root\": \"/kaggle/input/czii-cryo-et-object-identification/train/static\"\n",
"}\"\"\"\n",
"\n",
"copick_config_path = \"/kaggle/working/copick.config\"\n",
"output_overlay = \"/kaggle/working/overlay\"\n",
"\n",
"with open(copick_config_path, \"w\") as f:\n",
" f.write(config_blob)\n",
" \n",
"# Update the overlay\n",
"# Define source and destination directories\n",
"source_dir = '/kaggle/input/czii-cryo-et-object-identification/train/overlay'\n",
"destination_dir = '/kaggle/working/overlay'\n",
"\n",
"# Walk through the source directory\n",
"for root, dirs, files in os.walk(source_dir):\n",
" # Create corresponding subdirectories in the destination\n",
" relative_path = os.path.relpath(root, source_dir)\n",
" target_dir = os.path.join(destination_dir, relative_path)\n",
" os.makedirs(target_dir, exist_ok=True)\n",
" \n",
" # Copy and rename each file\n",
" for file in files:\n",
" if file.startswith(\"curation_0_\"):\n",
" new_filename = file\n",
" else:\n",
" new_filename = f\"curation_0_{file}\"\n",
" \n",
" \n",
" # Define full paths for the source and destination files\n",
" source_file = os.path.join(root, file)\n",
" destination_file = os.path.join(target_dir, new_filename)\n",
" \n",
" # Copy the file with the new name\n",
" shutil.copy2(source_file, destination_file)\n",
" print(f\"Copied {source_file} to {destination_file}\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "277b5d8e-9a75-4e24-a8d1-4b99ef32b39a",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import numpy as np\n",
"from pathlib import Path\n",
"import torch\n",
"import torchinfo\n",
"import zarr, copick\n",
"from tqdm import tqdm\n",
"from monai.data import DataLoader, Dataset, CacheDataset, decollate_batch\n",
"from monai.transforms import (\n",
" Compose, \n",
" EnsureChannelFirstd, \n",
" Orientationd, \n",
" AsDiscrete, \n",
" RandFlipd, \n",
" RandRotate90d, \n",
" NormalizeIntensityd,\n",
" RandCropByLabelClassesd,\n",
")\n",
"from monai.networks.nets import UNet\n",
"from monai.losses import DiceLoss, FocalLoss, TverskyLoss\n",
"from monai.metrics import DiceMetric, ConfusionMatrixMetric\n",
"import mlflow\n",
"import mlflow.pytorch"
]
},
{
"cell_type": "markdown",
"id": "4724011a-eb1e-4ff1-8451-fb9aac29550a",
"metadata": {},
"source": [
"## Prepare the dataset"
]
},
{
"cell_type": "markdown",
"id": "9126f6f4-25f6-4c02-868e-c2ffeb81ae9b",
"metadata": {},
"source": [
"### 1. Get copick root"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8cd3bff4-7135-4fa2-bb11-6f805603f71a",
"metadata": {},
"outputs": [],
"source": [
"root = copick.from_file(copick_config_path)\n",
"\n",
"copick_user_name = \"copickUtils\"\n",
"copick_segmentation_name = \"paintedPicks\"\n",
"voxel_size = 10\n",
"tomo_type = \"denoised\""
]
},
{
"cell_type": "markdown",
"id": "b2ee1fd6-a107-4044-a03e-2dc8a77e86e2",
"metadata": {},
"source": [
"### 2. Generate multi-class segmentation masks from picks, and saved them to the copick overlay directory (one-time)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ab607ddb-3c61-44f0-89c4-9215842ba996",
"metadata": {},
"outputs": [],
"source": [
"from copick_utils.segmentation import segmentation_from_picks\n",
"import copick_utils.writers.write as write\n",
"from collections import defaultdict\n",
"\n",
"# Just do this once\n",
"generate_masks = True\n",
"\n",
"if generate_masks:\n",
" target_objects = defaultdict(dict)\n",
" for object in root.pickable_objects:\n",
" if object.is_particle:\n",
" target_objects[object.name]['label'] = object.label\n",
" target_objects[object.name]['radius'] = object.radius\n",
"\n",
"\n",
" for run in tqdm(root.runs):\n",
" tomo = run.get_voxel_spacing(10)\n",
" tomo = tomo.get_tomogram(tomo_type).numpy()\n",
" target = np.zeros(tomo.shape, dtype=np.uint8)\n",
" for pickable_object in root.pickable_objects:\n",
" pick = run.get_picks(object_name=pickable_object.name, user_id=\"curation\")\n",
" if len(pick): \n",
" target = segmentation_from_picks.from_picks(pick[0], \n",
" target, \n",
" target_objects[pickable_object.name]['radius'] * 0.8,\n",
" target_objects[pickable_object.name]['label']\n",
" )\n",
" write.segmentation(run, target, copick_user_name, name=copick_segmentation_name)\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "8f1f0486-d056-4f42-a7ed-842cd39d771f",
"metadata": {},
"source": [
"### 3. Get tomograms and their segmentaion masks (from picks) arrays\n",
"\n",
"This 10439 dataset contains 27 tomograms. We will use the first 5 tomograms for training, and the following 2 tomograms for validation. Therefore, we will get the first 7 tomograms from the cryoET data portal in this notebook, and we will use the rest 20 tomograms for inference in a different notebook."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "3646b23b-93b6-4e8a-9915-5d6c8c570b0d",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 7/7 [01:10<00:00, 10.07s/it]\n"
]
}
],
"source": [
"data_dicts = []\n",
"for run in tqdm(root.runs):\n",
" tomogram = run.get_voxel_spacing(voxel_size).get_tomogram(tomo_type).numpy()\n",
" segmentation = run.get_segmentations(name=copick_segmentation_name, user_id=copick_user_name, voxel_size=voxel_size, is_multilabel=True)[0].numpy()\n",
" data_dicts.append({\"image\": tomogram, \"label\": segmentation})\n",
" \n",
"print(np.unique(data_dicts[0]['label']))"
]
},
{
"cell_type": "markdown",
"id": "bbbd22d1-0dba-41c2-b5d8-aac460a726e5",
"metadata": {},
"source": [
"Since memebranes do not have picks, we should see 6 distinct labels for particles, with 0 being the default background label. "
]
},
{
"cell_type": "markdown",
"id": "3a98a9cb-8752-4c30-87e3-1ffd5c3452c9",
"metadata": {},
"source": [
"### 4. Visualize the tomogram and painted segmentation from ground-truth picks"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "021533cd-5065-4bdc-b3e5-89124fde935f",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABHIAAAHqCAYAAABoRmllAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9d5isd102fk/vvZfd2Xp6Cp4QEIxAjLRIFwTElwQVGyC+iOCrP4oikIgCLyUg+oaOSlURRFBBEEEwCUnOyWlbZmen916feX5/HO4PuyTAAUJOIt/7urjCmZ2deeZpO5/7exeDrus6FBQUFBQUFBQUFBQUFBQUFBTu9zBe7A1QUFBQUFBQUFBQUFBQUFBQULgwKCJHQUFBQUFBQUFBQUFBQUFB4QECReQoKCgoKCgoKCgoKCgoKCgoPECgiBwFBQUFBQUFBQUFBQUFBQWFBwgUkaOgoKCgoKCgoKCgoKCgoKDwAIEichQUFBQUFBQUFBQUFBQUFBQeIFBEjoKCgoKCgoKCgoKCgoKCgsIDBIrIUVBQUFBQUFBQUFBQUFBQUHiAQBE5CgoKCgoKCgoKCgoKCgoKCg8QKCJHQUFBQUFBQUFBQeEHxtLSEq677rqLvRn7sL29DYPBgHe/+90Xe1MU9uBinitf+9rX8LCHPQwulwsGgwG33XbbRdmOexPXXXcdlpaWvu/fMxgMeMELXnDvb5DCfQZF5Cj8WMJgMFzQ/z7/+c9f7E1VUFBQUFBQULhX8e53v3vf9x273Y4DBw7gBS94Acrl8n26LW9/+9svOtmyvb2N66+/Hqurq7Db7YjH4/jpn/5pvPKVr7yo23UxMBgM8KpXveqH+g785S9/Ga961avQarXute36YTGdTvH0pz8djUYDb3zjG/G+970PmUzmYm/Wd8T96RpVuH/CfLE3QEHhYuB973vfvn+/973vxWc/+9m7PX748OH7crMUFBQUFBQUFO4z/NEf/RGWl5cxGo3wpS99CTfddBM+9alP4c4774TT6bzg1zl9+jSMxh9sffjtb387wuHwRVNpnDt3Dg9+8IPhcDjwvOc9D0tLSygWi7jllltwww034NWvfvVF2a6LhcFgIJ/5kY985A/0Gl/+8pfx6le/Gtdddx38fv++n/0w58oPg42NDWSzWbzrXe/Cr/zKr9zn7/+D4ntdo+9617swn88v9mYqXAQoIkfhxxLPec5z9v37K1/5Cj772c/e7fH/KZjNZpjP57BarRd7UxQUFBQUFBTuJ3jc4x6HK664AgDwK7/yKwiFQvjzP/9z/N3f/R2e9axnXfDr2Gy2H9Um/sjxxje+Eb1eD7fddtvdFBqVSuUibdX/XFysc4XH8tuJpXtCv9+Hy+X6EW/RheF7XaMWi+Uib6HCxYKyVikofAf0+3285CUvwcLCAmw2Gw4ePIg3vOEN0HV93/PoMf3whz+MI0eOwOFw4Cd/8idxxx13AADe+c53Ym1tDXa7HY985COxvb19t/f68Ic/jOPHj8PhcCAcDuM5z3kO8vn8PT7vyJEjsNvtOHbsGD7+8Y/fzRtLT/gb3vAGvOlNb8Lq6ipsNhtOnjyJyWSCV7ziFTh+/Dh8Ph9cLheuuuoq/Nu//du+99n7Gm9729uwsrICp9OJRz/60cjlctB1HX/8x3+MdDoNh8OBJz3pSWg0Gj/8TldQUFBQUFC4aLj66qsBAFtbWwCAN7zhDXjYwx6GUCgEh8OB48eP4yMf+cjdfu/bc09oC/mP//gP/O///b8RiUTgcrnwlKc8BdVqdd/vnThxAl/4whfEQrJXBdJqtfDiF79Yvoutra3hhhtuuJsCodVq4brrroPP54Pf78dzn/vcC7b1bGxsIJ1O36PNJhqN3u2xT3/607jqqqvgcrng8Xhw7bXX4sSJE3d73vf7ne2H+b51Idt03XXXwe12I5/P48lPfjLcbjcikQh+93d/F5qmyfZEIhEAwKtf/Wo5Jq961asAALfffjuuu+46rKysiAXtec97Hur1urzPq171Krz0pS8FACwvL8tr8PvvPWXkbG5u4ulPfzqCwSCcTice+tCH4h//8R/3Pefzn/88DAYD/vZv/xZ/8id/gnQ6Dbvdjp/5mZ/BuXPn7rZPvv2zP+IRjwAAPP3pT993nnG/bGxs4PGPfzw8Hg9+8Rd/EcB9OwtcKL79Gr2njJz5fI43v/nNuOSSS2C32xGJRPDYxz4WX//617/ra7/mNa+B0WjEW97yFnnsLW95C44ePQqn04lAIIArrrgCH/zgB3/g7Ve496AUOQoK9wBd1/HEJz4R//Zv/4Zf/uVfxuWXX47PfOYzeOlLX4p8Po83vvGN+57/xS9+EX//93+P3/qt3wIAvO51r8PP/dzP4fd+7/fw9re/Hb/5m7+JZrOJG2+8Ec973vPwr//6r/K77373u3H99dfjwQ9+MF73utehXC7jzW9+M/7jP/4Dt956q6wc/OM//iN+4Rd+AZdccgle97rXodls4pd/+ZeRSqXu8TPcfPPNGI1GeP7znw+bzYZgMIhOp4O//Mu/xLOe9Sz86q/+KrrdLv7qr/4Kj3nMY/Bf//VfuPzyy/e9xgc+8AFMJhO88IUvRKPRwI033ohnPOMZuPrqq/H5z38eL3vZy3Du3Dm85S1vwe/+7u/i//2//3fvHQQFBQUFBQWF+xQbGxsAgFAoBAB485vfjCc+8Yn4xV/8RUwmE/z1X/81nv70p+OTn/wkrr322u/5ei984QsRCATwyle+Etvb23jTm96EF7zgBfibv/kbAMCb3vQmvPCFL4Tb7cYf/MEfAABisRiA8xafRzziEcjn8/i1X/s1LC4u4stf/jJ+//d/H8ViEW9605sAnP/O9qQnPQlf+tKX8Ou//us4fPgwPv7xj+O5z33uBX3mTCaDz33uc/jXf/1XGZK/E973vvfhuc99Lh7zmMfghhtuwGAwwE033YSf+qmfwq233ioD9ff7ne2H+b51odsEAJqm4TGPeQwe8pCH4A1veAM+97nP4c/+7M+wurqK3/iN30AkEsFNN92E3/iN38BTnvIUPPWpTwUAXHrppQCAz372s9jc3MT111+PeDyOEydO4C/+4i9w4sQJfOUrX4HBYMBTn/pUnDlzBh/60Ifwxje+EeFwGACEIPp2lMtlPOxhD8NgMMCLXvQihEIhvOc978ETn/hEfOQjH8FTnvKUfc9//etfD6PRiN/93d9Fu93GjTfeiF/8xV/EV7/61e943H7t134NqVQKr33ta/GiF70ID37wg+U8A84r1x/zmMfgp37qp/CGN7wBTqfzPp0Fvh98+zV6T/jlX/5lvPvd78bjHvc4/Mqv/Apmsxm++MUv4itf+Yqoe74df/iHf4jXvva1eOc734lf/dVfBQC8613vwote9CL8/M//PH77t38bo9EIt99+O7761a/i2c9+9g+0/Qr3InQFBQX9t37rt/S9l8MnPvEJHYD+mte8Zt/zfv7nf143GAz6uXPn5DEAus1m07e2tuSxd77znToAPR6P651ORx7//d//fR2APHcymejRaFQ/duyYPhwO5Xmf/OQndQD6K17xCnnskksu0dPptN7tduWxz3/+8zoAPZPJyGNbW1s6AN3r9eqVSmXf9s9mM308Hu97rNls6rFYTH/e8553t9eIRCJ6q9W62/Zfdtll+nQ6lcef9axn6VarVR+NRrqCgoKCgoLC/Rs333yzDkD/3Oc+p1erVT2Xy+l//dd/rYdCId3hcOi7u7u6ruv6YDDY93uTyUQ/duyYfvXVV+97PJPJ6M997nPv9vrXXHONPp/P5fHf+Z3f0U0m077vFkePHtUf8YhH3G0b//iP/1h3uVz6mTNn9j3+8pe/XDeZTPrOzo6u69/6znbjjTfKc2azmX7VVVfpAPSbb775u+6LO++8U3c4HDoA/fLLL9d/+7d/W//EJz6h9/v9fc/rdru63+/Xf/VXf3Xf46VSSff5fPse/36/s/2g37e+n2167nOfqwPQ/+iP/mjfcx/0oAfpx48fl39Xq1UdgP7KV77ybvvq288HXdf1D33oQzoA/d///d/lsT/90z/d9313L779XHnxi1+sA9C/+MUvymPdbldfXl7Wl5aWdE3TdF3X9X/7t3/TAeiHDx/e9132zW9+sw5Av+OOO+72XnvB3//whz+873Hul5e//OX7Hr+vZoHvhAu9Rp/73OfuO6f+9V//VQegv+hFL7rba+69FgHov/Vbv6Xruq6/5CUv0Y1Go/7ud7973/Of9KQn6UePHv2u26lw8aCsVQoK94BPfepTMJlMeNGLXrTv8Ze85CXQdR2f/vSn9z3+Mz/zM/tWPB7ykIcAAJ72tKfB4/Hc7fHNzU0AwNe//nVUKhX85m/+Jux2uzzv2muvxaFDh0RWWigUcMcdd+B//a//BbfbLc97xCMegUsuueQeP8PTnva0u61+mEwmycmZz+doNBqYzWa44oorcMstt9ztNZ7+9KfD5/Pdbfuf85znwGw273t8Mpncox1MQUFBQUFB4f6Ja665BpFIBAsLC3jmM58Jt9uNj3/846IccTgc8txms4l2u42rrrrqHr8z3BOe//znw2AwyL+vuuoqaJqGbDb7PX/3wx/+MK666ioEAgHUajX53zXXXANN0/Dv//7vAM5/ZzObzfiN3/gN+V2TyYQXvvCFF7SNR48exW233YbnPOc52N7expvf/GY8+clPRiwWw7ve9S553mc/+1m0Wi0861nP2rc9JpMJD3nIQ8Sm/oN8Z/tBv29d6Dbtxa//+q/v+/dVV10l30u/F/aeD6PRCLVaDQ996EMB4ILPiW/Hpz71KVx55ZX4qZ/6KXnM7Xbj+c9/Pra3t3Hy5Ml9z7/++uv3ZT5eddVVAHDBn+E7Ye/5w+26L2aB74XvdY1+Oz760Y/CYDDcY+Pa3msROK9me8ELXoA3v/nNeP/73383FZvf78fu7i6+9rWvXdC2Kty3UNYqBYV7QDabRTKZ3HfjBb7VYvXtX0AWFxf3/Zt/jBcWFu7x8Wazue91Dh48eLdtOHToEL70pS/te97a2trdnre2tnaPfzyXl5fv6aPhPe95D/7sz/4Mp06dwnQ6/a7P/0E/l4KCgoKCgsL9H29729tw4MABmM1mxGIxHDx4cF+j0Cc/+Um85jWvwW233YbxeCyPf/tA+J3w7d8jAoEAgAv7vnD27Fncfvvt39GSw/DabDaLRCKxjzQB7vm71XfCgQMH8L73vQ+apuHkyZP45Cc/iRtvvBHPf/7zsby8jGuuuQZnz54FgO9ov/J6vbI9wPf3ne0H/b51odtEMC9lLwKBwAV/f2s0Gnj1q1+Nv/7rv75bEHS73b6g1/h2ZLNZITf2Yu937mPHjsnjP8w59Z1gNpuRTqfvtl33xSzwvfC9rtFvx8bGBpLJJILB4Pd87fe+973o9Xq46aab7jHc/GUvexk+97nP4corr8Ta2hoe/ehH49nPfjYe/vCHX9C2K/xooYgcBYV7ASaT6ft6XP+2kLQfBfaumhDvf//7cd111+HJT34yXvrSlyIajcJkMuF1r3udeG734v74uRQUFBQUFBTuHVx55ZXfMTPji1/8Ip74xCfip3/6p/H2t78diUQCFosFN9988wWHnf4w3xfm8zl+9md/Fr/3e793jz8/cODABW3D9wOTyYRLLrkEl1xyCX7yJ38Sj3rUo/CBD3wA11xzjQQsv+9970M8Hr/b7+5Vzvwg7/v9PM799/1u03d6vQvFM57xDHz5y1/GS1/6Ulx++eVwu92Yz+d47GMfe59VYP8ovoPabLYfuhL9R/Wd+btdoz8sHv7wh+O2227DW9/6VjzjGc+4G/lz+PBhnD59Gp/85CfxT//0T/joRz+Kt7/97XjFK14hFfUKFw+KyFFQuAcw+K7b7e5j4k+dOiU/v7feBwBOnz59t9WU06dPy8/533tK5f9eSf178ZGPfAQrKyv42Mc+tm817Z7klwoKCgoKCgo/vvjoRz8Ku92Oz3zmM/sqo2+++eZ79X2+k7pndXUVvV4P11xzzXf9/Uwmg3/5l39Br9fbp8o5ffr0D7VdHJ6LxaJsD3C+yeq7bdO99Z3tQnCh2/T94Dsdj2aziX/5l3/Bq1/9arziFa+Qx6kKupDXuCdkMpl7PFb39nfu7xf31Sxwb2N1dRWf+cxn0Gg0vqcqZ21tDTfeeCMe+chH4rGPfSz+5V/+5W4KJJfLhV/4hV/AL/zCL2AymeCpT30q/uRP/gS///u/vy8WQuG+h8rIUVC4Bzz+8Y+Hpml461vfuu/xN77xjTAYDHjc4x53r7zPFVdcgWg0ine84x37JMuf/vSncdddd0kjRDKZxLFjx0QCSXzhC1+QasMLAVcF9q4CfPWrX8V//ud//rAfRUFBQUFBQeF/EEwmEwwGg1RTA+frqT/xiU/cq+/jcrnusSr8Gc94Bv7zP/8Tn/nMZ+72s1arhdlsBuD8d7bZbIabbrpJfq5p2r4K5e+GL37xi/us5sSnPvUpAN+yaD3mMY+B1+vFa1/72nt8PmvV763vbBeCC92m7wdOpxMA7nZM7uk7JABpD9sLl8t1j69xT3j84x+P//qv/9r3XbTf7+Mv/uIvsLS0hCNHjnwfW3/v4b6aBe5tPO1pT4Ou6/eomLknFdCll16KT33qU7jrrrvwhCc8AcPhUH62t1YeAKxWK44cOQJd1+/xfFO4b6EUOQoK94AnPOEJeNSjHoU/+IM/wPb2Ni677DL88z//M/7u7/4OL37xi2UF5IeFxWLBDTfcgOuvvx6PeMQj8KxnPUvqx5eWlvA7v/M78tzXvva1eNKTnoSHP/zhuP7669FsNvHWt74Vx44d2/dF4bvh537u5/Cxj30MT3nKU3Dttddia2sL73jHO3DkyJELfg0FBQUFBQWF//m49tpr8ed//ud47GMfi2c/+9moVCp429vehrW1Ndx+++332vscP34cN910E17zmtdgbW0N0WgUV199NV760pfi7//+7/FzP/dzuO6663D8+HH0+33ccccd+MhHPoLt7W2Ew2E84QlPwMMf/nC8/OUvx/b2No4cOYKPfexjF5zZcsMNN+C///u/8dSnPlVqtm+55Ra8973vRTAYxItf/GIA5/NmbrrpJvzSL/0SfuInfgLPfOYzEYlEsLOzg3/8x3/Ewx/+cBn6743vbBeC72ebLhQOhwNHjhzB3/zN3+DAgQMIBoM4duwYjh07hp/+6Z/GjTfeiOl0ilQqhX/+53/G1tbW3V7j+PHjAIA/+IM/wDOf+UxYLBY84QlPEIJnL17+8pfjQx/6EB73uMfhRS96EYLBIN7znvdga2sLH/3oR39oy9MPivtqFri38ahHPQq/9Eu/hP/7f/8vzp49K7a3L37xi3jUox6FF7zgBXf7nYc+9KH4u7/7Ozz+8Y/Hz//8z+MTn/gELBYLHv3oRyMej+PhD384YrEY7rrrLrz1rW/FtddeezfljsJ9D0XkKCjcA4xGI/7+7/8er3jFK/A3f/M3uPnmm7G0tIQ//dM/xUte8pJ79b2uu+46OJ1OvP71r8fLXvYyuFwuPOUpT8ENN9wAv98vz3vCE56AD33oQ3jVq16Fl7/85VhfX8e73/1uvOc978GJEycu+L1KpRLe+c534jOf+QyOHDmC97///fjwhz+Mz3/+8/fq51JQUFBQUFB44OLqq6/GX/3VX+H1r389XvziF2N5eRk33HADtre371Ui5xWveAWy2SxuvPFGdLtdPOIRj8DVV18Np9OJL3zhC3jta1+LD3/4w3jve98Lr9eLAwcO4NWvfrWExvI724tf/GK8//3vh8FgwBOf+ET82Z/9GR70oAd9z/f/P//n/+CDH/wgvvCFL+ADH/gABoMBEokEnvnMZ+L/+//+v31lEM9+9rORTCbx+te/Hn/6p3+K8XiMVCqFq666Ctdff7087974znahuNBt+n7wl3/5l3jhC1+I3/md38FkMsErX/lKHDt2DB/84Afxwhe+EG9729ug6zoe/ehH49Of/jSSyeS+33/wgx+MP/7jP8Y73vEO/NM//RPm8zm2trbukciJxWL48pe/jJe97GV4y1vegtFohEsvvRT/8A//IMr0i4H7cha4t3HzzTfj0ksvxV/91V/hpS99KXw+H6644go87GEP+46/c/XVV+Nv//Zv8bSnPQ2/9Eu/hA9+8IP4tV/7NXzgAx/An//5n6PX6yGdTuNFL3oR/vAP//A+/DQK3wkGXaWTKig8oHH55ZcjEongs5/97MXeFAUFBQUFBQUFhe8A9Z1NQUHh3oLKyFFQeIBgOp2KH5z4/Oc/j2984xt45CMfeXE2SkFBQUFBQUFBYR/UdzYFBYUfNZQiR0HhAYLt7W1cc801eM5znoNkMolTp07hHe94B3w+H+68806EQqGLvYkKCgoKCgoKCj/2UN/ZFBQUftRQGTkKCg8QBAIBHD9+HH/5l3+JarUKl8uFa6+9Fq9//evVFwIFBQUFBQUFhfsJ1Hc2BQWFHzWUIkdBQUFBQUFBQUFBQUFBQUHhAQKVkaOgoKCgoKCgoKCgoKCgoKDwAIEichQUFBQUFBQUFBQUFBQUFBQeIFBEjoKCgoKCgoKCgoKCgoKCgsIDBBccdvza174W7XYbVqsVwWAQw+EQNpsN8/kc1WoVJpMJkUgEzWYTkUgE7XYbnU4HBw8exG233YZIJAK3243pdIpms4lAIIBOpwOj0Yh4PI56vQ6LxYJ2u43Dhw9jc3MToVAIm5ub8Pv98Pv9GI/HmM/nsNlsKBaLWFpawsbGBlKpFBqNBpLJJE6cOIH19XU0m0243W5omnb+g5rN6Pf78Hq9qFarCAQCcLlc2NnZQSAQQLfbhdfrxWAwgNVqxWw2g6ZpcLvd6Pf7MBgMcDgcMBqN0DQNmqahXq9jZWUFlUoFbrcbADCZTDAYDODz+eD3+9Hr9VAoFAAAmUwG5XIZVqsVXq8XtVoNPp8PjUYDoVAIZrMZ5XIZiUQC+Xwehw4dQqFQQKPRwOLiIlqtFrrdLtbW1tDtdmGxWLC1tQWXy4V4PI75fI5Go4HZbIZEIoGtrS0cPHgQGxsbOHr0KLLZLAaDAVZWVnD77bfjwQ9+MFqtFs6ePYtIJIJIJIKzZ88ilUrBYDCg2Wwik8ngjjvuwMLCAnq9HtxuNywWC3K5HFKpFOx2OwqFAobDIVZXV7G7uwuXy4V0Oo1bbrkFLpcLZrMZ0WgUjUYDHo8Hk8kEs9kMg8EAmUwG7XYbo9EIw+EQKysr2NzchM/nQ71eh9vthtVqxXw+x2g0gs/nQ7lcxrFjx3DmzBnMZjMsLS2h1WqhVqthZWUFvV4PxWIRkUgEiUQCd911FzweDywWCwwGAxKJBE6fPo3BYACv1wubzYbBYIDl5WWcPn0al112GU6ePIlWq4VDhw5hMplgOBzC6XSi0WgAAEwmEzweDwaDAWazGYLBoJxjLpcLd911F1KpFMbjMbxeLwBgPp+j3+/DZDJhOp0iFAqhWq3K+y8tLUHTNFSrVfmsBoMBZrMZ1WoV6XQapVIJa2tr+NKXvoR4PI4jR47g5MmTCAQCaDabMJlMcLvdGI1G0HUdZrMZ8/lc9uFsNoPRaMRgMIDb7YbX60U2m0UikUCtVkMgEMB4PIbT6YTJZMLW1hbG4zEe+tCHYmdnBx6PB8ViER6PB7PZDDabDaPRCDabDb1eD8lkErPZDNvb23A4HIhGo+h0OpjP5wiFQigWi1hcXEShUMB4PJafJxIJnDhxAmazGWtrawCAQqEAq9WK5eVlfPWrX8Xq6iparZZc1zwWS0tL8n7hcBhGoxHnzp3DwYMHsbu7C6vVisFggEsvvRRf/epXsbS0hNlshvF4DJvNBovFgpMnT+LQoUOw2+2o1+uwWq1yv3G5XNB1HZPJBEajEbVaDYcPH8bW1hbC4TBqtRpcLhdMJhNMJhMGgwGCwSCq1Sri8bhc/yaTCTabDTabDe12Gx6PB9PpFKPRCCsrK7jllluwtraGZrMJg8GAWCyGnZ0djEYjJJNJzOdznD17FpdffjkajQaCwSAqlQqCwSDq9TparRaCwSCWlpbkGhgOh1heXkapVILVaoXdbsdgMEAoFEK5XIbZbIbdbketVsPa2prs09lshnQ6jXa7jV6vh263C4PBgHg8jlarBZ/PB5vNhp2dHRgMBiSTSfR6PVitVrRaLayurmJrawsmk0nuWbfddhuuvvpq3HrrrTh48CC+8Y1vIJPJYDqdYnd3Fz6fD2azGYFAQLbN7/ej1WrJ+cZrjJ+B1xSvv3w+j2g0imazidFoJOd9JpNBp9NBs9lELBZDp9OB3++HrutotVoAgNFohNXVVXzjG99ANBpFLBbD2bNn4fF48MpXvvKH+kOroKDwg+FnjU+/2JugoKCgoKBwn+Oz8w9/z+dcsCLHaDTK4DEej2G1WlGv19Hv9xEKhWCz2eB0OtHtdmWw8Xg86Pf7MkiaTCYZjDjUzOdztFotGW5tNhtyuRwikQjG4zGm0ykcDgcAYDwey1Bx6NAh7OzsyGtZrVZ0u10sLy9ja2sLoVAI0+lU3rPRaCAcDmM2m0HXdeTzedjtdiSTSYzHY1QqFdjtdvR6PaTTaRgMBlitVjSbTcznc4TDYYxGI0wmE4zHY2iahkQiAeD80NNsNmGz2WA2m1EsFuH1etFsNmG1WmXoAQBd1+FwOFCr1WR/GgwGGI1GGUgqlQpCoRBms5mQPJqmyTDT6XSEVHA4HHA6ndA0De12G+l0Gna7HdVqFUajEZFIBL1eD7u7uzK45/N5ZDIZNJtN6LoOo9GIyWQCTdNgt9ths9kwHo9hsVjQ6/VgMpngdDphNBrRbrcBANFoFO12W46PwWCQz6frOprNJhKJBIxGI+bzOebzuZwPRqMRvV4PgUAA29vbGI/HCIVCsNvt2NzcRDQaxXA4hNfrhcvlwmw2g9VqFbLH5/Oh3W7D4XDAZDLJgOf1etFqtVAul7GwsACfz4dmswmXyyXHcTAYYDKZwGKxIBgMyvDN48qhfmVlBQsLC6hWqxgMBvB4PHIOzedzuN1u9Ho9mM1mRCIRNBoNjEYjVKtVuSY8Hg80TUOxWEQoFBLCyu12w+Vyod/vC1ljMpnQ7/cxHA4RDofRbDbhdDphtVphNpsxnU5RLBaxvr6OQqGA5eVldDod1Go1WK1WWCwWAIDH40Gr1YLdboeu6+j3+3INB4NBIVOHw6FcU3a7Ha1WC5FIBDs7OwgGg7BYLMjn8wgGgzhw4ADOnj0rRJyu63JNOxwOuN1udLtdOX69Xg/BYBCJRAKj0QgA4HQ6Ua1WEQ6HMZ1O0e/3MZlMYDabYTQa0e12YTKZ5LyfzWYIBALY2dlBu91GMBiE0WiExWLBfD7HeDyGyWRCPB6HpmlwuVwYj8fodrsYDoeIRCIoFouIx+MYDoeIx+PY3NxELBZDqVSC3W5HJBJBNpuFrus4cOAA2u02stksPB4PjEYjjEYjZrOZnGNOpxNmsxmTyQS9Xg8ulwvD4VBI3GazCQBCQvJ+FQqFcOjQIXS7XWiaBqPRiNFohOl0ilgsBpvNhlOnTiEWi6FSqWA+nyOZTKLZbELTNASDQdjtdrTbbaysrGBjYwN2u13eczKZIBaLIZlMot1uo1arYTAYYD6fY3l5GYPBAGazGZubm7BYLLBYLLDZbPB6vdjd3UUwGITT6ZRzgPe0s2fPYj6fw+/3y3nI6280GkHTNDgcDni9XmxvbyMajaJSqcDpdAr5bbFYYLfb5RjUajUYjUbU63U5biSTDAYDhsMhyuWyEOEkXMbjMcbjsVzrDodD/g6RnBoMBojFYnLfdDqdsNlssr3cr2fOnBGyngTdcDiExWJBo9HAwsKC3AuNRiVaVVBQUFBQUFBQuP/hgr+lNhoNIWXcbjcKhQKcTif8fj8sFguGwyFmsxkmk4ms2nKwNpvNMBgMmM1msNvtKJVK8Hq9GI/H0HUdbrcbwWAQo9EI7XZbBsvRaISFhQXMZjO0Wi04HA5Mp1MYjUY0Gg243W4ZwvcOqOl0Wn7ebrcxm83gdrtht9tRLBbR6XRw6aWXotFoQNM0zOdzUfU4nU4AkEHIbrdD0zQYDAbY7XZUKhUkEgkhjqjI8Pl8MJlMqFQqOHLkCHq9HjRNw+bmJgKBgKhxer0enE4nHA4Hut0uACAUCqHf76PX64nyZzAYCCnS6/Xg8/kwmUxk8K9Wq3C73VhcXES9XpeBkq/f6/UQjUaxs7MDp9Mpg/t8PofP58N0OpVBOhQKweFwoNFoCGmjaRr6/T40TUM8Hke1WpWV93PnzskAzePu9XqFZFtZWUGxWBQ1CrdnOp3CbDZjPB7D7XbD4XCg0+nIQAVAlFAulwterxfD4RDT6RStVgsulwtOp1MICpvNhnA4jJ2dHei6jkAggEajAZfLBYPBIJ+HP2u1WqLy4rlEtRDPbZfLJYqrTqcDk8mE8XgMh8MBn8+HwWAAm80GXddhMpng9/tRLBbluEWjUeRyOVgsFnS7XSH3tra2sLa2BpfLhel0iul0ikajAZvNBoPBAKfTKYQjVUO1Wg2z2QzVahUHDx6E2WxGrVaTc8Lj8aDdbst+6XQ6cLvdcDqdaDabsFgsCIfD6Ha7cDgcKJVKGI1GsNvtiMfjGI1GqNfrMBqNCIVCqFQqiEajaLVaor7SNA3T6RR2ux1+vx8nTpwQdZ2maTCZTEI66bqO5eVlVCoVGAwG+YwWiwVGoxF+vx/ValWOr81mg9vtlvOax7tSqcBsNmM0GsHv96NUKsHlcsFoNMLpdIqCzul0olKpQNM0UT3NZjMYDAY5x6iwo8LMarUikUig0Wig3W4LSUASOhqNynHjcanX63A6ncjlckgmk3A6najVakJmAefJGwA4ffo0jEYjFhYWYLfb5TjyfklCwul0YjQawWg0yvEMBAIIBAKw2Wyo1+vY3d3F4uIiTCYT2u02jEYjptMpEokEBoMBdnZ2kEgk0O/3hZgIBAJyP8hkMmg0Guj1etB1HZdddpm8VrvdxnA4RDQalWuO1xnvzYFAAKPRSFSER44cQb/fl78FJpNJ1DMOhwOTyQR+vx9OpxObm5tC1vt8PvT7fTgcDjSbTSHHHA6HkKEOhwO6rsNms8Futwt56fP50Gq1hJykUqrT6cBqtcLv98Nut2NnZwfz+RwGg2Ef8be0tITpdIp8Po/hcAiDwQCbzYbJZCJ/kxwOByKRiLw2yfWtrS14vV45txQUFBQUFBQUFBTuL7hgIoeqCqpogsEgNjc3ZUjMZDLY3d3dN1DSGuP3+zGfz9HpdDCdTrGwsCBydq/Xi9FohGKxiGAwKMM4SYZIJIJarQaLxYJms4n19XUZrGhtqdVqsnqaz+cRCATEskQlCWX3ZrMZS0tLyOfzsNlssvpvMBhkiO90OvD5fAgGg/KzyWSCbDYr9iu73Y5YLLZv9Z/DRyKRQKVSwWw2w8rKCm699VZ0Oh0Eg0EhjLxeL+bzuahDxuOxqHJsNhsCgYCoamKxmAyhmqZhMBjAYDBA13VR72iahlAoJCvPVHnUajWEw2GYzWaYTCYUi0VZkW80GrLiPpvNEIvFYLVasbOzI7aXfD4PXddFKdTr9UQN5ff7ZRAdDAaiwMnn84jFYqI6GY1GYrsZDAZiReOx4DBHJddeGxOVJUajUWwYmqahVqthMplA13WEQiF0u10ZEG02G4bDoSiFer0eAIiq4dJLL8VgMBA1AM+vTqcj6oZGowGj0SiDZbVaRTAYFCsGyYvBYCAEhcfjgcFgkMGPv5dIJERFBAC9Xg/5fB4HDx5ErVaTc6Zer4v6zOPxwGazoVarIRaLYXd3V2xNsVgMJpMJPp8P+Xweg8FAFA/1el1ICNoAV1ZWRHlClYnRaBQlCQAh3WazGYDzNqCNjQ2EQiG5po1GI9LpNEajkahfSHbabDaYTCYMh0Mkk0k4HA5ks1lRd/V6PVGI8Zyfz+fY2NhAMplEuVzeR4a1Wi1UKhUhAg0GAwqFgqg3qA6hdYbnkNVqFcUPj52u6+h2uwgEAnIv4/F3u92o1WoolUpCRqXTadRqNSEReX2YTCb0ej3M53MA5wlYi8WCyWSCTqcDALjkkktkW41GI77+9a8jk8nsI001TZPrdDgcQtd1xONxNJtNBINBTCYTVKtVIZmodgsGg6J04b9J3E4mE7lmnE6nWDd5j9N1HZqmCUFGtYzVakWj0cD6+rpYN/v9PjweD0KhkBDAo9EIhUIBqVQKPp8PVqsV7XYb5XIZFotFCByez+FwWJRMJA95HpEk5vW8tbWFQCAAg8Eg9+56vY7BYCDbwvfTNE0sWACESPb5fHLNOxwOUcOdO3dOzkGv14t2uw23241EIoFmsykkLMmbeDwOAAgEAphMJkJEKigoKCgoKCgoKNyfcMFEjs/nw5EjR2SInU6nYlvodrtot9vwer1IJBIolUqYzWayis78mfl8Dk3TZBV3eXlZsl6m0yk6nQ5GoxFCoRA6nY4M8rFYDOPxGMFgEGfOnEE8Hhcbis/nQyqVwubmpih7xuOxbBeJHKvVCqvVimg0io2NDfh8PhSLRWiahkgkAl3X4fP54HA4RGafzWZhsVjQ7/fRbDaxurqKVColr81BaTweIxwOo1QqIRgMitImHo+j0+nA5XLt+zwcuFwulwytXJGez+cwm80wm82oVCoytHS7XSGDXC4XXC6XDNKj0UiGJCqWgPNDDgcafu5wOCzvk06nceLECSGOOp2OZFBQMZBKpTCfz2WoHg6H6PV6OHr0KBqNhpAfVGbE43FRnTQaDclooTrIbrfD4/HIMTKZTJLR4XQ6YbFY4HA4hPzZq8DY3t6WTBxd1zEajdBoNMS2R+UVFQWz2Uy2OxaLodvtwuVyCbHTbrdFIeT3+6FpmqzKu1wuVKtVeL1eGRrL5TJ0XYfdbkcwGITBYBBlxuLiopAtAOD3++WaaDab6PV6qFarWF5ehslkQiaTEZWT0WhELpdDLBaDx+NBNpsVi144HBZSEoBcQ36/H5VKRfJShsOh2H6cTidSqRS2trZQLBYBAMvLy6IsowWo0+lgZWUFBoMBrVZLFHIk4I4fP45isYhsNotwOIxGo4FoNAoAomBqt9uSk+P3+8U6Vi6XkUqlYDQahaSk5Y45WqFQCC6XC61WSwgZqtqm0ymCwSDi8bick5FIBIVCAWfOnIHNZkM8HofX68V0OsVgMEA0GpXzjeoOWnmq1SoWFxdFdUN1HnDeDujxeETtRmtPq9USAsTn8yEWiyGXy4mNazQaCdHrcDiwurqKcrksJI7f7xcr2mw2E6LSbrfDZDJJthBtUiSLE4mE3Cfm8zny+bxkMnm9Xsnc6fV6kttFqxbvP9PpVBSQdrtdrsFqtSo5RvV6HZPJBJFIRFR2Pp9PiDeHwyFWMJfLJQQczx9a+iwWC1wuF3Z3dxEIBORcdDgccn3x3m6z2eR+6HA4hMSeTqfQdR3ValVUnel0WpSJtFQ5nU5RaFmtVsnl0XVdrLPj8RjFYhFOpxNOpxPT6VRsnYlEAp1OB/l8Hpqm4fDhwzCbzUKAdrtd6LouhKCmaUJuKigoKCgoKCgoKNxfcMFEDrMXFhcXJYMgmUyKDYpDBFU4XAkulUqyumm329HtdsWeVC6X4fV6JfOFhEK9XkcoFJKMGofDIcTN0tISut2u5KBQ4REOhwGcJy8YyszVfw52AERts9c6MpvNJFSTIapU2ni9XlitVhluW62WWJVsNhv6/T7sdjtcLhcsFgtqtRpqtRr8fr8Mu1QVhEIhbG1tIRaLwWAwoNvtolAoSDZGNBrdp/ihaqFarWJpaUlWpDudjpA6tMgMBgMhdLhiPhqNRLkwGo3gcrlkFd9ms6HT6SASiaBSqaDb7UoGEHOHjEaj/BeAqDPm8zm2t7cRDoclNFfTNCwsLGB3dxfNZlMGWGZrUAHBvBlaenhe+Hw+GXI57PO9aKNIJBJwOByiQuExZ14Jw6KHwyEWFhYwn88luHY6nSIQCGA2mwmBx8/FYZoKFSrCGPzLbCDmEVHZUq/Xsby8jGq1KuGwp06dQjQaxXg8loBlEokej0eCiRl0bTKZJMCXQbEulwuVSgVLS0tioTl+/LiEWJ88eVKyU5gVwkEYOJ+b0mg0EIvFkE6nUalUUK/XMZ1OYbFYhNwMh8OiSOAATmIuHo9L9giP016Vx2AwQCQSQSAQEGK02Wyi1WqhVCrhwIEDEvydTCZhs9lQKBSgaZqE6Oq6LrY7XdcxnU7h9XrFisNw5slkgslkIhYvqlqYD0QFYK/XE8I3lUrJsG42m+F2u0VV1Gw25fqmDSqbzUr+l8FggMFgQCQSkcDyarWKbDaLaDSKUCgk9qz5fC5KOhJDDLnmucL3ZSYWz+10Oo1isYhyuYxgMAibzYZQKISzZ8/uC9Peq2YhaQlAXr9Wq8Hr9QqBxnsHCeh4PC6ER7vdhsFgEFUl72sAxF7V6/WwsLCAYrEoqiUqgXgvYgaQzWaD1WrFcDiE0WgUC2qhUMDi4iIajQYCgYCQiFSEkYjmfZgZa9FoFNlsVsj+wWAgn5P2T5K9vPdsbm5KGPRtt92G9fV1IeB47kYiEcmwWllZQbvdFgUbs6p4TVutVhQKBcTjcdlmBQUFBQUFBQUFhfsTLvgbKodhZgwwh2bvSiitASQp9qpruAprt9slwHIwGACAZFGYzWZpY2F+BMmTo0ePQtM0lMtlAOcVD3tXhmlT4iqvxWIRhQJVP0ajUTJvWq2WWL7YbEX7GAddi8UijVyU7FssFlQqFQCQjBTafNxutyhouO0ul0tW7k0mkzTR0GrBQZH7YzabSebQYDAQ8oFNMXxNfp5arYZgMIhIJCKKHCoJksmkWMjMZrOEuNKKwhVyDnfpdBpGo1EsK7TKcHWfAzyPk9PpFMWI2WzGXXfdJSHVjUZD8iuYLbOxsSH7OR6Pi5KC+5ShpLQNDQYDlEolpFIphMNhZLNZOT94HjFvaDgcSu4KB9ZarYbLL78cw+FQQmGtVquoJWi1GA6Holo4c+aMEEQej0dUX7TT8BrgKj/VW7TJUAWSSCRgMBhkf/E8pDKMr2Wz2bC4uIjNzU2USiXZNwBEpWU0GiVriiolhkFvb2+LjY1KJ7YmeTwe5PP58xe60Qifz4dSqSTEBRULpVIJPp8P3W5XwrK73a7YjdgORKKP5xyVEtFoVAhHkhjdbhcej0dUOrR+0aozHA6FjGHLFpVm2WwWLpdLLHNGo1HIgL3Xh9VqFcILgOQJsdGIYcxWqxVOpxO7u7uw2+0ol8uSicNzmmSrx+OR1jCe98vLy6jX6wgGg3LvmE6nkkUUCATEHsdtYE7YXqsg2/aoKCS5wvY83ieCwaDcN1utlhBfJOvS6bRc68B5gnV7e3tfy5rFYpHg6Hw+j+XlZVEtUnlHBZqmaRLGTgJyOBxiPp+j2+3CbDZjNpuh3W4jlUphZ2cHKysrEg7OcHLuM16HZ86ckbByn88nhBbzlnj/0TQNuVwODocD8/kcFosFo9EIFosFbrdbMtVomyThSVKIqqh+vy/kp6Zpco/k+cb7BBVevKZJdlMhyHBnKhpLpdKF/plUUFBQUFBQUFBQuE9wwUQOcxWYt0ALTzAYxO7uLsxmsxA1DCXll36GEy8uLgppwdVlu90uOSrz+RwLCwsy5HHFdO8AR0IklUohl8vty7rodDrSinPq1ClplnI4HKjX65LDs7CwIBaawWCAVqslw3itVpPq2VqtBgCSN8MgWtaLezweqXlmaxTJIWaHDIdDqcatVCqiBLDb7fK7FosFJpMJnU5HBlTgvB2GFcxOp1P2AVUyuVxuX6YJn9fr9dDr9WSw42BE9dRgMIDJZJJQWCqeut0uYrEY/H4/ptOpWMTcbrfYaPbWaZPQabfbolDg/qcdqtPpoF6vIxAIIBQKiZoAgDSEMVSZygKuvANAIpGQ8GEGUZfLZSwuLkp2EcOHmRfEZqy9Ybg2mw3JZFIyPHg8IpEInE4nisUims0mrrjiCiG/AEgWlMlkEvvF6uqqBBbT+sHWKWYVFQoFaaeitZBEwZ133gmPxyOBy2w2Inmxvb0NXdclz8PpdKLdbkvVNABR+1AVMh6PJcA1EonI4M1mLoboDodDsUgxq4eh4Twvm80mKpWKhAQzb8ZkMomti2QR9wGvXaraBoOBKGR4rk0mE6ytraFWq0lNt9/vBwDZP2fPnkUikRC1Bi1dvOZ4rPbm5PDaicViEipOdQnJ4l6vJ7Y+knkApG2ODVvValXqqa1WKyaTiSgESbqyWYz3AwYV5/N5XHbZZQC+pQzsdDpi89J1XQiMQCAgpEWz2UQ6nZbqeQBy/6BtaDqdolQqyT2BIfF+vx/1el1q0JlnxaDscDgMm80mTXFUD5IELhQKQsbQWre4uCikIu/9DDtm7pHZbJbsGFoKzWYzXC4XTp8+LUHLzPNidhLVPzy3J5MJZrMZkskkptMparWa2A553QAQApu5Qfw7UCgUkEwmpX2KVfNLS0vY3d0VZZTFYkGpVEIsFhNV4N7zg2H5mqZJIDOvfxV2rKCgoKCgoKCgcH/DBRM5qVRKrBsMG6aqw+12w2AwwOfzoVqtIhQKodlsCgHCnATmdySTSezu7sqwxVVZAJLN4vF4UCgUYLPZEIvFsL29Lc1BgUAAp0+fRjKZRDgcRj6fF8sTrRYLCwswm83Y3d2VxqdgMCgWJLPZLK1QtPwAwOLiImq1GhqNBprNJpLJpAx2+Xwe4XAYi4uLaLfbMlBRQbE3C4jD6mQyQSaTwWg0koGI4Z7MFaF9guQDg6DL5bLsW4fDgUAggF6vJ21byWRSlBaFQgGtVkuIj0wmg1KpJNXuzNGgHSIQCMgwaLfbhRAxGo0ol8vSKNTv96XVxul0IhgMCklx6tQpHDx4ELPZDN1uVxQLtHWRTGKLFy0R/X5fFDiscmZwcqfTQSqVEutQrVYT9QJX86nu0jRNSJRAICDbyiwfKhNoy+D+5H4EztuoxuMxMpkMjEYjisWiHC+DwSDnIkO5jUYjzp07BwDyeZnDsXfwBiAWOxKGXq9XSBQOjdz//CxUftCa12q1JGsnEomgWq3C5XIBACqVCtbX12GxWJDL5cQuxYGe4bPM/qBSha1fmqbh0KFDMBgM2NjYgMfjEZUIM5T4XAaBu91u5HI5eDyefaHB/X5fWr58Ph/cbreEarfbbVFYFQoFhMNhUVyQXOx0Ouj1ekLQAeeVJrSgUSXj9/vF4sacrt3dXYTDYQkIHg6H2NjYQCwWE2Jvb1i70WjEYDAQMtpgMKBYLEolNxugqPBj4C7tj1QcBQIBaaXifWdra0t+x+/34+jRo5jP5yiXy0Jq8f5DRdnS0hI2NjaExAyHw3KusuWK1+zOzg7cbrco2nZ2dsSCxbDqYrEo6hXm8rBtkLlPzWYTJpMJx44dE+sgz+FcLodEIoFcLofFxUX4/X65x1KtRmItn88jEolgPp+Lasztdst1GA6HxUY5HA6RSCSknY+Kymg0Cl3XMRwOEQqFkM/nJeCabVW8d+fzeczncyEEw+GwqG143+G1z4yfyWQCTdOEZC4UCnKcqXoLBAKyD0le1mo1xONxIZUVFBQUFBQUFBQU7i+4YCKn2WyKxYPEBhtfqKzhoN5qteDxeLCzs4P19XX5gtzr9eD1epHNZuHz+WCxWKRlivaNXC6HSCSCRqOB5eVlaZ0hqVOpVGR4BCAERCAQgNfrFRl8IpFAu92WQYK2FrvdLraIEydOYHFxUSxU8/lcbAUcnNnUpGkaHvKQh+D06dOiTAIgobvz+RzHjh2T6l+qgMxms1Sg2+12BAIBUViwWpnkD7NRuMrOlh+r1YpyuYx6vY5YLCaD4HQ6lYYq2gFYBUzrD0kabieDS7e2tnDJJZdgd3cX/X4f8Xgc7XYb1WpVbDZerxcWi0XIGVaT03Jhs9lQKpWQTqfFesOg2PF4LKql7e1tGI1GrKysSJU6W6EYQMqa8EgkglOnTu2rdmbQKW0wzOrgKj6VFOFwGF6vV6qRSYDRVkMCh01dtPSYzWa0Wi00Gg0kEgmpKqeix+12C2FE+yBw3obC+m9avdicZLFYMBgMMJlMEAqFJNuDga085yqVigz3zHTitQJAbIZsVaPdh7lP9XodBoMBBw4cQLvdRr/fRzQaRaVSkWuU6g+qYqgEYYNRu90W0oXnTzgcxtbWligSOHwHAgEhKmq1mjSDUb1D8oyqB+ZcNZtN+Hw+xONxVCoVUUTwd3meMbyarXfT6VQsPbQW0sJDNQvDhxlgnEqlJCCc11E8HofVapUmONocaVeLRCIolUqYTCZotVoIBAJCQJP0pEqKxCAA5PN5LC4uisqG9xlmf9VqNXg8HqTTaQknp/qMOVLZbBaZTEbuY3feeSeSyeS+9ja+J+1ItVpN7qtU2GUyGWiaBofDIe9FUqXRaMDhcCAYDAqBxCB6ktus7O50OtJaxxyvBz3oQej1elIjzvyztbU1sVECgMVigc/nE0UQGwV57dJuS1Kb2TQ8Ltw/vA5IDPF+wYWBQqGA1dVVec9Wq4V+vy/3vfl8Lsdtr4qS6iqq2PheJIF47zSZTEgmk8hms/tUhAoKCgoKCgoKCgr3B1wwkcPQU9owaLNgRTNVIgwOHg6HCAQCMrSxPYiWBqPRKAGg3W5XAlsZbGmz2aTiulwu49ixY2g0GtLC5HA4sLOzA4vFgmQyKSoWt9uNSCSCb3zjGxKwSVKAGTAAJHCZgZnAt2qJh8Oh1ElzBdpqteLcuXMIBoNCWkSjUQl0dTgc2NjYEEIlkUjIQFsoFBAMBuF0OiVMczwew2q1IhgMSoUx1T25XA7lchkrKysYDAYS3jybzaTGm6qObreLVqslmRHRaBSDwUDsLtFoFCaTSbIrKpUKxuMxYrEY8vk8vF4vPB6PkCWsWKbiand3F36/X0Ko5/O5rMCzgYZ1yfw3yai9gb/9fh+bm5tYWlpCo9FAJBKBwWCQcF0qugqFgmQmMauF6iUqlth6lMvlRDXCFhzmhrAxi5+bOR8kBnle1mo1IVCWlpagaRp2dnaQyWSEDGEzEF+P5z6Jsk6ng9lsJsRJvV6XwZaKinA4jGazKcqV9fV1dDodaRKi1Ymfl9k7w+EQsVgM8/lcjjetbrwWaFVk3TzDn00mE1wuFzY2NrC0tCSDLquiaSNJpVJiHZvNZlIfzWuHrT7BYBDnzp1DIBCQEGuSO9lsFqFQSIK2t7a2pNVseXkZfr9frHiz2UwG6+l0KtcDrUuj0UjUE9PpVGqqG40Gcrmc5P+0222pQ6c9kuQgB36v1ys5OwCkJW04HErWDVukYrEYdF2XAGhaDw0GAzRNk/Bnv98vPx+NRojFYjh16hRCoZAow9jmxWuxUqlgeXlZbH3MAxoMBrDZbKhWq7Db7cjn8zh+/DjK5TJ2d3eRTCalaY3nxHg8htvtFpLG7Xaj3W6j3W4LIUwlEvO8Dh8+LPlErHFnBbfH4xH7U6FQgN/vRyQSkea2YDCInZ0dOReMRiMcDoco45hP5vV6sbW1JcfFarUimUzuI4IZbjwYDGC1WlGr1eS4kcwlcWMymaTRkER0KBRCKBRCvV5Hs9kUFRxVorwfU1HEQGcS0wwyZqsWK84Z/kwlIfOdisUiIpHID/M3VkFBQUFBQUFBQeFexwUTOfV6HQCkucbn82E2m0lrk91uR6lUknpr5rJw8OVq62w2E2XP4uIi+v2+5Dhwdbjf78ugyEpirs7TemUwGMSGsbm5ua9+V9d1+aLOyu2DBw/illtukcHH7Xaj1WrJ7+2txeZnY6MQQ0vZltNsNoWwYXYNrSrdbhfhcBidTkcsOcwloV2l0+nAZrNJvsbW1pbUX2uahlgsJtXl1WoVkUgE/X5/X/230+nEYDCQlf5Wq4VQKIRcLgcAEpi6vb0tzT26rmNpaUmUDCQSAAiZZrFYZPtnsxkOHTqEzc1NIVP6/T6q1apUdFMBw1V6KgaY22EymWQAZP4KB1FajxhASpKBdjsSN3y80+lI9TLzlqhISSaTKJfLGI1GAM4P7MxsmUwmYlWi8oMqj/l8LoHKnU5HzmFamg4ePIheryehqolEQkg1vh/bo2gjMplMQsRtbm4iFAqJTYQB0CQla7WatOg0Gg2sra2hXC6LJYyKFqvVKoMpCa69SipaT0gekuiZzWb7FElutxs7OzuiTnM4HCiVSqIkYk5OqVQStQKVM2wXos2M+6VarUo47Gg0EiXWZDIRdRYtVJPJROx/wWBQBm9N0yRofD6fSy4PQ2ipAiI5nEgk9qk33G63EBXMzCFRTFWa0WiEpmnSFMfmNlpreF2urq6iUCiI2g84n3lDZVYikUA+n5fzYWNjA6FQSCxwJKWp5GCINy1rzJ7RdV3uLQAkoHdzcxORSARutxtGo1FImXq9Ltcbw81pGwoEAkJ68L2Hw6HYlgqFglRrLy8vC8nKxjWSKCRkDAYDGo2GhBWvrKxIiLrBYJB2wnw+j6WlJWxubkou2mQyQaFQkLYth8Mh53ckEpEcHwabs2GNNfQA9hE/tN1SiTYej5FKpSTImfduVtHT6sl7BqvpT548Cb/fD5/Ph0gkgna7LQrB6XQqVjbe62j/NJvNP+jfVwUFBQUFBQUFBYUfCS6YyGF19fb2tqgPTCaTBFayyrdYLKJarcLpdMLlcslgxjarYrEoOTn88k+bQDgcFvsIB2Jd19HtdnH48GHJ57DZbKJ4mEwmsnILQLJu2BZFMoj5MVQdWK1WLC4uwmg0Ynt7W6wUzIWwWq3Y3d2FwWBANBpFv9+XlXQ2Z9G2wTpdqoto3+HwC5yX/7tcLhSLRWlWMRgMsnLfbDaxsLAAk8kkAxczepi/wappk8mEra0tpNNpGe4XFhYAQOxkg8EA1WoVmqYhGo2K3YFBwfl8XgZfu92OtbU1URpVq1Wpc9Y0DV6vVxQZtHFomiaKgtFohEQiIRXCHPJZZc7jwBYku92OXq8nQ18ymZSsGn521hgz0JRqpsXFRUSjUcnpmU6nkr3EBiBut8ViQa/XExuOw+GAyWTCeDwGcF61s7S0hEKhIAorVsozwJZKDIbCMtyYLUSJRALlcllsXMw6Gg6HqNfrkr1DK5rT6cTm5iYsFouE7vZ6PUSjURn0qb6gzY8hy2wJq9VqOHjwIEqlEqrVqhCWHo8Hbrcb6XRajgNbszqdDkKhkISC0yZWKpVw8OBBhMNhIXj4O1SAUVlBopU5WBycWd9OEpYhz3a7HZVKRbKZXC4XfD6fqESYocQmr+FwKKo0Xmvj8RiVSgVmsxmZTAbA+br4r3zlK8hkMkLyttttANjXzkZbI0mOcrksuS28v7C9iWQZSbZMJiMEJC1fJPeYjcQBX9d1yXE5deqUWLaYPcNsIRITzEziPadareLw4cOoVCqi9KFKr16vi22V5LPdbsd4PJbqcwYjO51OIbGobGG1O9sFzWYzms2m2PJSqZRkIPFaJFHL+w5tR6FQCKVSCd1uF9FoFLPZDKFQaF9AtNlsRiAQEIUNlTNUUem6LhlcVNmxIYuWQIbI8xgx74YEKhvSIpGIWChDoZDkoVmtVlFHut1uOBwO5HI5BAKB83/0vpmR1G63ceDAAXS7Xflbs7W1JQQv2+H4N0VBQUFBQUFBQUHh/oILJnJWV1dFscHmD9oqqEygisTr9YrdhlYQKncY2MqmHg5cDFBlGCcbiqxWK1ZWVvC1r31NVrz5BZvDlNPpRKFQkJpwDo5U+nBAJunAgMt8Pg+j0Yj19XUA561V4XAYm5ubIsO32+3Y2toCcL4uOp/PY319Xb7ssymJKgGj0bivApwEDJUuk8kEpVJJrGWsLmZuBLMemBfDgcPhcEi7DRVQW1tb8Hq9spJut9sBnCeYPB4PbDabrE5rmrYvxyOZTGJlZUVIjE6nIyGfDLalYoJDKVerSdzt7u6KAgD4Vo08yZpWqyVkVLPZlMwdPm82myGbzULTNPh8Phk6FxYWUK/XJZ/iyJEjkn0CnLfF8bwiUcjt47ZQoeFyuURBQLKPJA1bthhoyiad8XgsbU/j8Ri1Wg3JZFKUKiSZptMpxuMx+v2+5G9QmcTaaT6PWSxGoxGXXXaZWG6YU8PzngqDZDIppF0sFkO5XJZsHzYDUbVEpQ0tiVR/UX1x5swZqd1mnTUJA+ateL1eIQx4nfAa4L5hW9F8Pkc+nxd7n9VqRTgcxmw2QyQSwblz50RFRbtbt9uVDB3a+FqtFg4ePCgkDhVc7XZb2u5oe3O73ahUKkLCMkcKAJLJJAKBgKhQAMDv94tSiGHdFosFtVpN1Gu8ZzErBYAQY1T3kXhLJpMwGAxYXl7GxsYGrFarEBsMvKZKzmQyCcllt9sl+JwWLd4P2u02hsMhFhYWcMcddyCVSsFgMMi+oZKI9zUqkKiiCofDQpyx2rzb7YqqijlBVDPxc1I1RItdqVSSzC8SYTyXFxYW0Gg05PiMx2MMh0P8x3/8Bx7ykIfIObO3+Y+h3jabTYKas9msECmsK6cti/ZC5uvwbwLzeoLBIKxWq5DktBPyPHI4HGLRIhEeiUTknjccDqUdjwsBtNpSnUXlJevGuY9oy1RQUFBQUFBQUFC4P+GCiZxyuYzt7W0JSV1fX5cMgb01w2y+mc1miMViEhhLOxWzMrxeL3K5nKgo1tbWZMUf+FYjEINvl5aWpD6a6g7aovx+Pw4fPoyNjQ0Mh0MYDAYsLCyIfYh5IFwlZjUyALE6kXRhBg7rm61WqwwezGOgrYVKFLYm5XI59Pt9CXBlm0woFILT6cStt94Kp9MpKp3pdIrl5WWMRiPk83kAkFDfUCiEbDYrFe/M5iAZkc1mhbhhpkSz2cTKygqWlpZkOONgk8vlcOWVV0LTNLGBZbNZeL1eGbzYNMOV/UqlAl3XpR2GyqZLLrlE2rBI0BgMhn32LA74zD+hZYNqKw6RsVgMjUYDjUZDBmGbzSY19N1uV0KdDx8+jGaziel0Cq/Xi3K5DKfTKWopZo3EYjEhhVghzZV/tuEwN6Pb7cpjtHixYYz2tMFgIHY6DofMHaJ1pFgswuVyodlsSk6J2WyW3CEOu1QP7OzswOl0wu/3S807a5VDoZDk3FA10Gw2xULHSmWSEFQwUaVBMoHDfTwel0yfWq2G9fV17OzsiCrEZrMhl8vJMe73+3A6nZIvYzab4ff799Wh+3w+lMtlNBoNaX9i8DFVE2ylY54KiRyeY9PpFNvb26J+IGFRLpfFFkYCod1uS34Qs3Y49PNY7yXNgPNKs+3t7X1qrUajIZZOhuAytJuhyyQ7dV1Ho9GQhijeG0iQlEolsQoBwPb2No4ePQoAOHPmjChreD0XCgVEo1GpLed9jnY1hi3vJQ68Xi9Onz4Nr9eLxcVFCZMmKcTzdGNjQ0iRVqsl1iXmATHHhs/h/Xiv7ZWtcK1WS8ghVqjbbDaxmtGCl81m5bwNBAKSP8VriiQS9yvJqUajIWo7XddhNBqFsD958qQQqZqmYXFxUUi9VCqFUqkkCquNjQ1R8ZAc5vGifbTdbst5n0gk4Pf7xfJWKpXg8XgAnCe/acfs9XqS88WwZQUFBQUFBQUFBYX7Ey6YyNnbTNXr9cReFAwGEY/HMZlMZBgEzg9RlMZzhdvtdqNYLEo4p8fjkSDkYrEIu90uIafMxUilUmKbKJfLYr1iMwlzJti0UygU4PP5pN48k8kgl8vB5XLBZrOJisHj8WBpaQm33nqrWCCA8xYor9eL3d1dxONx9Pt9GI1GaaHhgMxhLhAISDvLcDiUoGPK9zmM8veMRiNqtZooevg+HNA59LDGudPpCGG0txGLyiPWVvv9foxGI3S7Xck12WvFoCqG5AAtLGwK4zDJRqXd3V0cPHgQlUoFKysrYjcym83Y3t5GOByWlW+qiJgDwuPIz7pX5TMYDLC+vo5yuYxqtSphxEtLSxK0yyYqHutisYgDBw7gC1/4AjKZjATVJhIJdDodhMNhGbZIXAHnQ12TySRms5mExNKyxIBlEpMkgjisapqGYrGISqWCTCYjSpVMJiPV1U6nE9lsFslkUobmWCwmSjSGgpNw3FsZnU6nhXgBILlOCwsLYj0ymUzw+/24/fbbsbCwIDaW0Wgk5AFzmfZm2ASDQWk5o6XR4XCIhWd7e1tsYK1WS5RoDPplEPXp06exvLwsapXhcCgkEgOLE4mEKIE0TcNwOBQyhaouqr1Y6c1MqXg8LqSYx+ORwG6r1SoKD9alt1otpFIptFot1Ot1HD58GOVyWZrUptMp+v2+kLRsZ6OqjHX1S0tLyGazcLvd8Pl8krlC65LNZsPZs2cBnM9kYcYOAMn4IXnG/BiSqwaDAcFgEJubmzhy5IgQkNPpFJVKBcFgUK5fbjOVWZPJBNFoVBRPtP8B5xv4stms5Ezx3G82m/Jfl8uFSCSC06dPw2q1otfrodFoIBAIIJlMigJQ0zTkcjkhi3h9855TrVZFhUOyi/d3NtRR0ddutzGZTIR06/f7aLVacl8EILbGVCol+4Gfj0RJq9WCz+fD1tYWMpmMqNx4n2WuWbPZxOrqqoSGh0IhCROnhe3QoUPI5XKiKCNRTFXX3rZB2g95PG02m+T5JBIJVCoVOa8UFBQUFBQUFBQU7k+4YCKHAz4HDJPJJF+w6/U6RqMR0uk0BoPBPovBbDaTIXE8HqNcLsNsNktALBt+0uk0dnZ2ZBDnEMwQXAbpspWJYcFms1kGNVYjs+rZarVKgDBXjTnQclB0uVxC7HAFvlar4ciRI2LFYP4HX6/f76Pb7SKVSiGXy8mgBwALCwsYDAaw2+0yvDocDuTzeSEVWONLewuJGCpCmEFDMmzvQMjwWgDS2NTtdoUoKRQKYstiAK/H45FMHO4fl8sl6gJmq/C9GA66tbUlBBAreqn4GI/HWF1dlW3jsapUKtA0DfF4XMKcqU5hUDabhZiZ0mg0hMhLpVKiful2uwiFQphOp2g2m3LesWXJ4XBIWDRbjZh5wda0brcrgyaHXr/fL59jb8uV1+tFpVJBLBaDyWRCKBSSAdbtdktLE4mw6XSKWCwm9hYSllShABBLodVqha7rEnbL7B4et0AggFqtJqQMLWwkjAaDgexHVnezEp3WO6qaaG+x2Wzo9XpCvlJ9weGX722xWJDNZoX4IGERDAaltYjNbKwhJ+Ho8/mkUcjj8aBcLksNfKVSwcbGhrROkcBgQC/zV3isJpMJarUa4vG4KKFmsxl0XZfrjg1LtBztzZSp1Wqi7GHgL+u52f7FEF6Sxazo5jlAgjSVSsFqtWI6naLX68Fms0kmE1UfrJZvt9vS6EdCeHd3V4hvZjMxAyiTyaBUKmE+n4u1igRfr9fDysqKtDAxkJnnLgn0aDQqTWe7u7uS1bW4uAhN04Q4rlarYoMlEWQwGEQlRhXV1tYWxuMxDh8+jE6nI3YkBirTekcLqd/vR6vVEpKE55Hdbpf/z+NTrVah67oo5bhfGSQdDoeRy+XQ6XTgcrkkLJ/3o3g8jo2NDbhcLrjdblEk7b0fslXPYDDAaDSKKpCkaTKZFOUOALm2GMhNyy9DoHmdknxTUFBQUFBQUFBQuD/hgokcBucyAJYrplROcKBkJoXP5wNwXjHQarUQiUSQz+exvLwsK+dsp2J2A1fjZ7MZDAYD0um0ZM2QvNlLWnCwZpgrMxMYUJlOp+HxeGAymcROROXLwYMHpRac+Sn8Uj+fz6XGORwOy0BL6wlDS5kV0+v1YLfbpXqYKppOpyOqF/5sNBpJxg9wPniTAzjDSGlN0zQNa2trYps4cOAASqWSvD5VRhxmuTrOgZb10Ts7OxIsXKvVcPnll6Pb7aJSqcDpdEq9MC0rACSbhKvlzNdh+xVzgUajkQToGo1GLC0tie1kbz13MBhEoVAQ4sxoNCKXyyEajYrNgpXnJBJIdJBkW1hYwOnTpwFAcjzYnFWr1YRoYpg2Bz1mXzBXqVwuSz0yQ61JbFksFqkp73a70mDGZhzaX4DzpBItO7ShjcdjrK2tiW2lVquh2WxibW1N7Hsc/mnHi0QiEobs8XjE5haLxdBut4WIYS3yeDyG2WyGz+eDpmlIJBLY2dnB4uIiTpw4IbYmqtv8fj/i8TiazSbq9bqoHOr1OhYWFuS55XIZt99+O2KxmOSjUDFCAoDXKa912hBJni0uLop6ZDabYXV1VXKsut3uPnvWYDDA8vIyOp2OhARTTVKpVOQ6XlxcBACxylFZ4vP5sLOzg2KxCLPZjFQqJeei0+lEp9OR48mQaIZKd7tdlMtlpFIpIbtCoRDOnj2LAwcOiBKPtdVUbrjdbiG7wuEwtra25Dpjw5bVakW/38fRo0eFxKBCiOcf28ZMJhPOnTuH9fV12O12LC4uYmNjQ8g/EhV7Q4LD4TBuvfVWDAYDrK2tCcm+s7ODyWSCVColFtRIJCIB1jyvhsMhfD6fqKXY5haPx3HrrbciGo3C6/XizJkziMfj0kZGaySVcmw96/V6KBaLiMVi8rNmswmDwYClpSWxy+3s7MButyOTyUiGGhvefD4fFhcX5fzY3d3F4uKi2NACgYAQ8Mx2ooqLwfIkl2id4j15PB6jWCwimUyKPZQB14FAQGrdea+m7TYQCKBYLAo5p6CgoKCgoKCgoHB/wQUTOWyC4QC6u7sr7VUHDhyQ5qCzZ8/C6XTCaDRKnsJgMBDVDnNeqGgYDAbweDyiQKHyhEMVv7TTosJQU34hpzKD0nsO/H6/H/l8HmfPnpXVaWbGMPuDigCSErQn+f1+VCoVeS9axvx+v4R3WiwWCd11OByIRCKwWCwSlEkSIRwOi4XIYDBIwCozQ/r9PoLBoNgUqCSgeoLWH9qwAoGADITdbldq3pvNJqLRKILBoAxs/LwcwJmNwbpkAGJ3oiqJKoBCoQCHwyEBvGzisdvt6Pf7qNVq8Hg8MsjxmFEBQxUFPzNzeNgAxsyWZrMpSqFarYZIJIJgMCg18MxXGg6HqFQqcLvdohiiVY4DP9UdPp9PKriZTzKZTKTZiXkvJOjYrsZsjkKhIMMcB3pm8fj9fsnVMBqN0iyUyWSEUGSl9HQ6RSKRQK/Xw2g0Qr1eRyQSQb1eF8KQNhPWyzPImnYRWnDYSkRVD993Pp+jXq8jFovBaDRKexCVcyQeeR6x1pmKCYaTV6tV+Hw+LC8vYzAYoNPpIJfLwe12y3ENh8OibmN9/GAwEGIGgFiNJpOJ2MfOnDkjx5tWGz6XwcM8/g6HA2azGel0GuVyWYhOr9crrXhUrDHcOpFIiMrGaDSK5ZAtbCRHSULzfpFKpfBf//Vfcj2Xy2UhMKn8Ycgz1UHMqNF1Hb1eT3KKqBQkKeZwOHDq1Cmsra3JcaL1jNe12+2WvB7aq2q1GnRdh9frBQBpnKJSJBQKodVqwWw2i2plMBggmUxKUDcDzJeXl8WGZDKZRDkYCATEHutyuaT+nGHaZ8+eFWsU76+83+m6LucKiU+fz4dUKoWdnR05BtxnPIahUEiId5Jj3W4X8/kciURCPgsJ7Gg0KkRtrVbDaDTCwYMHxToaiUREeTmfz/cRi2yeI/nY7XZht9vlPh4OhzEajYRwZTMc1Um8F9CuyEUJBQUFBQUFBQUFhfsLvi9Fjs1mQ6vVkoDcfD4Pq9UKs9mMAwcOyMrquXPn0Ol0ZPWWqoIrrrgCOzs7UosLQAZXto6wbSifz0tWR6VSgc1mk5YcKkXC4bDktrBqmdktdrtd7CW0nVA9omkaJpMJFhYWpC0FwL5VWgajkoBxuVzy2ansIeFAAqhYLAppoOs6fD6f1OQ2m034fD757ByAuZJPZVCz2ZTMHA6SrPpdWlrCeDzG5uamtNKwopmBwxyMORyGQiE0m02pAmaIKBUqDCaltYuEiM1mEyUOM1c4MK2urqJQKMggFo/H4Xa7hdhgaxKbpUg27CU72BzGzIp+v4/V1VUJv2X1cSKREHUF82ssFgu2trawtLQkx585JwyVJYnFHBqPxyNWkmKxKOdROp2G3++XemkqmWil4QDOVXkGutLWtDdst9PpIBqNimKHAyuzjmhxo2XDZDIhmUxiZ2dH1DPNZlOyQdh2RKULrTkMzLVYLKKQYUNQJBLBaDTCZDLBcDiUhjRmsDCbhvYVBsLSPsWBm+G00WgUxWIRXq8X29vbQsxarVYJ/6UFkAG7vEa2trZgMplw6NAhlEolZLNZsVkxeJchybQUAudtSbQgWa1WbG1tiQrN6XRieXlZLDskXHjuU1kxm83k+rXZbGI3q1arQhZbLBY89KEPFUsis1Li8Th2dnYwm83EWsN9wwBg2qqYH1Qul5FIJFAoFNDpdJBOp5HP5+F0OrG2tiZWsr1WQZfLJZbLSqUirW5WqxUGgwG1Wk0UIsw6On36tGRQBQIBtNttaXljVbnH45Frk6QT869ITE4mEyEPmZFE21SpVJIa8Wq1ui+0+9ChQ6hWq5JBxnOI916eD2azGaFQCHfeeSeSyaRsm9PpRLVahdPplIwbKvR6vR6i0agQUQye3htubDAYkEqlpFmNDWbj8VjuZdxektS09Q2HQ8l2ovWR5w/PYX6mUqkk5BX/PigoKCgoKPw4w+h0wuT1wuT1At+0KsvP7HaYvF4YvmmbVlBQ+NHjgomchYUFIRM4TLD6uN/v49y5cwDOD7rr6+uyCt7pdJDJZODz+ZDL5aSafDAYiN2j1WqJ7J21xiaTCbu7uxLKyRVg2ioMBoMMs1RZ0GLCvIh2uy2r9ouLiyL3t9lski3DYY8ZKMxsITHi8/lktZ6ZGp1OBwcPHpSGoVKphGazKSG6JJFY+cs8Gda07w1CBiCDhdFoxOrqquT6MOeBAavZbFYsBYcOHRJ7DIdt1jHTcuN2uzGZTLC4uAir1SoDF1uOVldXJQSW1jTmHlEJQ3UR64ITiYSEujJPhHXtXI2n/YekC1UQzJtptVpYX1+Hy+WSumfWIFPxksvlkEqloGkaKpWK5OcA5/MtAoEAbr31VqlZ93q9GA6H2NrakqwXbg+HYealkABjiG6z2YTRaBT1FPNZeKyWl5cxmUz2Db/cV/wfFQEkc4Dz9rRcLgcAovRioxVzR4bDoeQBUVEwGo2kyttms0ngM602jUYDkUhEjnWz2ZTtYAZMOp1GrVbD2tqaVKYD51vRNjY2sLS0JNkyDLmNRqMolUpSob431JdWHxIFtVpNyIa9qh6v1yu2qmg0KsePSiqGhPP8YWMZVRKdTgeRSARGo1HaqxKJBBwOBwaDgbRoVSoVrK6uynEhWWgwGBCLxeQaN5vNaLfbcLlcKJVKQiawve7cuXPw+XzSTkelEff7aDQSRWClUhHiOhKJiHqGig0qo2g1W11dldwbkpO0/0SjUTm/nE6nELdUpgQCAQmVLpfLYqFLJpNoNBpCUng8HlSrVSFoaBdcX1+X+1Wz2USlUkE4HIbP55MWOgaX7yVzS6USlpeXxd4ZiUSktYrvQSJob16R0+kUFR9tnLyuqBjiPZdqHYPBgGg0KnlCtGHSkqnrOtrttijZSESNRiM5n6lKZHsYVWzA+bBzv98PANLCxgWAQqEAg8GAZrMpQecAZNvC4bDcC5W1SkFBQUHhxxlGux146KUo/vLl2P7tY9j+7WPAg49984cm4MpLUP2lB2H7t4+h+YyfgOH40Yu7wQoKPya4YCKH9plwOLyvIadarQp5weGYeTKj0UiGkslkAr/fL3kbNpsNzWZTVuMLhQI0TZPBgVkW9XpdXmsymaBSqWA8HiMcDot0nquujUZDQmppL4nFYlJ/DZz/Qs9w0mAwKNJ8EkOsKGfuSblcxsLCgihaSG5omib2qJ2dHaytrSESiaBSqaBQKIgNiTknbNZyOp3Y3NzEoUOHxA4yHo/R6/Xg9/vFSsIMHOa0sAmGFrR8Pi8Dpt/vh8PhEPLHYrGgWq3uC4WltQSANB6Vy2VR4tC6ApxXX9HmxvpuDjRsdmLFtMPhEMWO2+0WWwsVAaFQSCquaflaXl4W9VE6nZa8DNbET6dTsbjkcjmpmR4MBmKbonWG1g4qpjRNw8rKyj41Bi0l/X4f7XZbVCE8B3u9npBfDGtlFhDVG1R18DzRdV3IO4bgapoGv98v+9xms+1TNQyHQ7jdbiHVGHi7sLAg9rput4vZbCbhuZqmyfaRUDEajULuMGS82+1id3dXSIzRaITFxUWx4ZAQJCFTLpelkYiqknw+L01ntNww96XX6yESiUg7EXOJQqEQMpmM7Fur1SoB4VTYkNTkeUKFG7NnqNziOUTrF3Np2OxFkrDRaCCZTCKfzwOAnOfr6+sYj8fY3d1Fs9lEJBKRzCSq2nZ3dxEMBiVQnflDwWAQdrsdOzs7Qt70+30kEgm02234fD4JYuc53Gw2pda60+lgMBjA6/UiFApJHXy/35fjWa/XMRgMEIvFJHeGir+1tTWxmo5GI9luWhaB83XZtGvm83lRIlmtVgCQ88VmsyGbze6rFGdOEUlufjZmEu3u7op1LxAIiBWSYeVswCoUCshkMhiNRmJb47nCoG5aQWnJJAFDEplWLhKptNzxOJDM5HUVi8WwubmJ6XS6ryWQVj/eO5mzxTB3fm4SPyTGO50OkskkOp2OqHcASFbb3nB5ANLEqKCgoKCg8OMIg8+L3M+6MfF/67H81R4Yjh/F+HE/gdxjPBjEv7nAsmRA7jE+zK960MXZWAWFHyNcMJHD2tvd3V1YLBYJ3QyFQhiNRtJGZTAY4PV6ccstt2BtbQ3BYBC1Wk0yY2q1mthe2HLCwcLhcIhlg2G5zFsh0ePz+eDz+UTpwrYckgwkVvjFvt1ui9oHgJATrNflgDeZTIRU4kq82+0WAmM0GiESiWA6nUrOSLfbxebmpqzeMiuDg8BkMpEhglYWt9stw/6JEyfgdDoRiUSQSCTQarUwnU73BXpSzULrA7OC9qp2SOpccsklEhacSCRQq9VEkcRtIGlDOw8/M61wJpNJAlKtVityuZyswtP2YLFYJBOE+Tv8TKzGpnLIbDaj2WyiWCxK/gRbvEg+xWIxGah5blAFwHOEtdcc4GjXS6VSGA6H2NnZweHDh3H69GlUq1Wsrq6K0oHKMK/XK/k2nU5H/stacLZb0TZH6xRr0VutFpaWlmCz2VCr1RCLxcRKFI/H0Wg08PWvfx2pVEoUA+VyWYZGKnnC4TCKxSLC4TCazaaQeWzoIVlRLBYly6XdbstgrWkarFYrksmk5A3RCseBeDQaSYgtlQoAEAwGJQPJaDSi1WpJThQ/P49lPB5HqVSCz+cT4oYNWa1WC0eOHMFwOESxWISu6xKeC0CeQ2vjeDwWspdV9iQnk8kkTpw4gdXVVQlbpr2Paj0SocPhUNRMJA5ofaHFMJ/PS3gwcL6NKpfLweVyIZ1OY3d3V7KL2GZFaxztTwwN5vEBIIHtiUQCX//617G2tgar1YpisShE23Q6leyZXC4Hj8cjbUsAJLOKrWE+nw/b29uiKppOp3Jt8fri69LC2Ww2xcYGQEia0WgkFfU89lTjdTodUSTSfkhVpNPphN1uR7VaBQC4XC4UCgUhd3kczWaz1Hknk0lUq1WxgfGa2Zt1Q3KMjXPM4GHrHZu/+N4keBiCr2kaGo0GFhcX4XQ6RRFFgpVB6MzScTqdOHPmDLxeL3Rdl3wnhvMzf4fWWJ/PB4fDIcpCi8WCc+fOYW1tTfLf+HMFBQUFBQWFb2FuAUZRJ3pJ891+ppuAflJZrBQUftS4YCJnMpnsUzJUq1UZuiuVCpLJpAxAXCFlnTZX16lS6Xa78oWelhtWN3e7XSEvGFjLGl/mS3BY9Hg8kr9A6wxXbNlmQgUOVSVsKGJF8d4AWVankxjSNA3BYFBsLePxWLazUCggEonA7/fvsx5Fo1EhMNjMxdrx8XiMSqUiQ7/b7Yau69LoQwKDNoN2u42VlRUUi0UJ4qXVi+okl8slq+msNOfKdyAQkJye+XwuCgVmt7A1B4AMaiSULBYLbr/9dgkfplqAodJ2ux2j0Qi9Xk/ybviZSAbouo4zZ84glUrJ4E3LCwes0WiEc+fOic2CGUmsRjebzej3+2IhYngvm3qY1cFsmKNHj0qQKwcxqqhoy+CwzFV72spIkrndbjmnqMRgFtL29rbYkUKhkISrUg3GFqhisSjKJirSeP4WCgUsLi6KFY5WDo/HI3ZCj8cjpBj3L4k2DqbcJr72eDzGYDBAJBKRivS9QeF+vx/NZhPj8ViskgyvZgvdXuLVbDZLXTm39dChQygWi3A6ndja2sLCwgK2trYQCASE/GMTEFvQSDzw2MXjcSE/qEYjwWe32yUcncHkzWZTcrmYRVWpVHDo0CHJEnK73VJJzeF+7/XHz0nbzXA4FAUYK72DwSAuvfRSnDhxAsB5km9xcVFymKhqog1uMBjIfYzXAckkkiD8PLzOme/FWvXZbCbKPk3TxA42Ho+FtLDb7ZKrdfnllwuRy23s9/sAIPuH5Lbb7cb29jY6nQ6OHTsmNiTeY9jyRwXLYDDA0tISNjc3hUDq9/tSe0/VTblcRr/fRyqVkrDzWq2Go0ePol6vCynFuneLxSJtdVS80OK3srKCdrst1qh6vS7NYPyMfI1YLIbpdIp+vy8WvVgsBq/Xi1wuh36/j1gsJhY83ldJgvHY0OLHcHSHw4GNjQ0Eg0EcOHBAVFs8DoVC4Yf7K6ugoKCgoKCgoKBwL+OCiZxQKIRisSgWk4MHDyKbzcoQQMUNK4tprWBAq67rOHXqlAzQDFOldJ6V30ajESsrK2IR4uo2V6LtdruoZNgcE41GpQoYOG+fovWLORQkL2hFYHAq67ZdLpcodajo4bDNlWIA0lzEdh673S52EY/HI6G8HPYTiYQMPhz+7XY7CoUCnE4nXC6XZN0YjUaxPTHDolQqSdYIs1CsViscDoeEmu4Na6YK5cCBAzJ0kXBi4wtzIDhgUc3EnBMO+CQXarWarJwzV4LNMRz0WN3OYFi+B1fWDQYDDh48iI2NDdmXrE9nPhEVDL1eT6xUVADM53MsLS1hMBgAgKgE+NkrlYpkHHFQXlxclAGeYa0WiwVGoxHZbBb1eh3pdBpms1kIKq/Xi16vt6/S/dSpU3jQgx4Ej8eDcrm8L5sknU5jY2NDrIPMNuGx5TFYXFzEXXfdJaG1tOEw5JU1zLx+uF8NBoMQWx6PB8ViEYFAAOFwGPV6XXJ12u22BNTuDZkGzg/7e62O8/lc7GocrKmsoHWMCgXm+LAafjgcYmVlBSaTCcViEb1eTwiq4XCI8XiM2WyGRCIhldvnzp3DgQMH0Ol05BykjZAE49bWFur1uqhOQqEQjEajkG+0ZVksFqlyJwFkNpvRarUAAGfPnkUkEhECmYHotB9RKcdcF7fbDYvFIuq1vWQkW9XY3sYa8b0tYJ1OR857Xt+DwUBqvHVdF6UTSUNaU2knZI7L3hY/p9MpQej8/2ztCgQC0HUdu7u7Qr4x7JvHk6QRlWEkyXg/sdlskinEzCCXyyXh1x6PR85Rkr4krJaWloQ8IgEdDAYle4mfFYBkAo1GI2mFYjtfJBJBNpuVfC1acsPhMFqtllz/vAeTaCcxs7OzI+Swy+VCIBAQ0oyqzlwuhwc96EHS3sXsrmQyiVwuJ5ZGKgepWKKCbK/FSkFBQUFB4ccSmgbjZP9DzpIOx5dOwTzU7/58HTCP5vfNtiko/BjjgokchgUXi0UsLy/jzJkzyGQyUnNst9slDJZht1RBsAkqnU4LibK5uSkr5bRsrK2tAQBOnz4tg2QymUStVoPf75cME7ZCGQwGJBIJ3HnnnRiNRojFYtIOxRXnVColjSnM/mg0GjLUMCi5Wq2i1+vh4MGDmE6nOH36tAQGU5nA7BOTySTZJwAQi8XEatVsNiUQlbYnj8cjihSHwyGKEqvVina7jXw+j9XVVVHLLCws7FMB9ft92XeFQmFfiHC/35fjYDKZUCgUsL6+jlwuJ4qGyWQiKgqn04lwOCzWpdOnTyOTyeyzNVFVlUwmJfCWAbYchDiMpdNpUVLlcjlEo1Gsra2JCogr9q1WC+PxGEtLSygWi4hEIuh2u6jX6xKaOhqNUK1Wsb6+LgoRVm8zp4UKmEQigdFohEqlIoPvuXPnYDabsbKyIufiZDIRBRYberrdrtTAc4An0cEg4dlsJoqR5eVlfOMb30C/38f6+jq63a6oMRikSwVSo9EQ5RUzo+LxOAqFApaWlkStQMIuGo1iPp8LIdput2Ww93q92NraEssUM2342UKhkITBsvEIgNilSJiSeOh2u9B1HcvLyxK8vbe5iKST3+9HPB6Xfa7ruthwKpWKVEaTTOK+Y+XzdDrFzs4OfD6fZNcwhJkKEDaF6bou1q5+vw+32y3E4Gw2EwKAWT8c0C0Wi1jeQqEQhsMhAEgOCgkKPrfdbss5xrp0viYArK+vi9LP7/eLEoiV4YPBAO12G1deeSWKxaKoZHidLy0tYWdnRz4bw9kZ6Mz3Z2sYiYdOp4NSqYTpdCoWvXa7jWazKcqaarWK5eVlCQcnecLMq9lsJnXljUYDnU4HVqtVrEPdbhfb29vyHBJjVEqSsIlGozhz5oyEbE8mE8nIIjnLVq3JZIJ8Pi9h8y6XC5qmYXd3V0g4Eo1utxulUgmxWGyfVWw+n0uVfaVSEUKQJAptUGxci0QiKBaLEvRMcpD15Py8AKQxcT6fC+FFQpzkHgOwS6USLr/8cui6jtFoJJXrVPOQDFNQUFBQUPhxhFarY/ETFYTu1OR/kffdinm3C//f3AJ3bj+ZY5oAjr//2kXaWgWFHx9cMJGzvb0ttiPWHVN5Q/n/fD7Hzs6OBPDu7u4imUwiFovhzJkzEhZrt9v3tVDR6sE6YQagcgXbbreLCoahxVy5v+uuuyQwlxYAEjUejwfZbFYyRJjRQ+sHyaHpdIrpdIpEIoF8Pi+DRCQSQblclkBTkgibm5sywDD3ptvtSvPRXkKHK9PD4VCIAVYfs+EnEAjgq1/9qigJqMRgyCqrpElekXCg1YAWCYaFMlSXldC0s9HeQQUHlRrM4tB1HePxGOl0GqFQCK1WCydPnkQsFhOVwtmzZ3Hw4EFRONAaw+dsbm6K4iEQCCAYDCKbzSIYDMqgb7PZxM5ULpcRDodlMKXNgSQIbUFU7zCImCG4zNdwu91C1pFgqtVqGA6HaDQaoigKBAJiv5lMJnL+sYWJTWy9Xk/OO56DnU4H7XYb8XgcsVhMLEkk+WjVonWKzV+0gW1ubkpDEjNz2NLD854h0lRjMR/G4/FgPp/vswdxG2lZpLIrkUjAbDaLPYnbx3Mum83u2x8MNA4EAvuq3HkdWK1WsVT6fD70ej2cOnVKqqGpdqOig+cKrXdU1VD9FI/Hcfr0aUSjUSEAGYrLPCReMw6HQ+rbqYBhaC8Dg6m8Ac7boyaTCQqFguTE0EbGa55KG16z1WpV3osNRlTncV/zHsJrl0N/t9uV3B02M1E5QttTJBJBIBAQC9he9QdVSdznRqNRbD+8rmezmQQrU5G4uLgo28W2r263K+HizHuhEi0QCIg6j/ZVEsUul0tUaLymLRaLWNVoUeU+JFFO8onWQl5TDA3em/XD/UGlEJVUPp8PpVJJAqN5PGu1mljF5vM5qtUqTCYTfD6fqC332tVIiDMDymg0wu/3S8g4iXur1YpWqyX3xkgkArfbLfYpnmMGgwF+vx+RSEQsXgoKCgoKCj+u0E6fg/PjX5X/zb+Z06dPJwh/4iQyH2/AUdax9LE6Fv6hCuj3oNRRUFC4V3HBRI6u6zh48CAGgwFarZaoAbji2u12ce7cORw/fhy1Wg1OpxPFYlGaoJLJpKzo7s3Q2d3dxXQ6RTAYFGvS3kHGarWK2iIQCEgmDyt/LRYLnE4nYrEY7rzzTlitVng8HrRaLXQ6Hclm4Cq/yWQSe1Q0GpVgWFpcZrOZrBzvbd9hTS3tS8wf4Wr73vBRqgtIFGiahsXFRVSrVWiaJgRNNpuVjIy1tTUUCgUZ8uPxuOR8MC8oEAggEomg0WggnU6jWCwikUjIyjeVKwyGXllZwfb2tgzjzJiZzWZYW1sT+w0byUhSAECtVsN8PpfmIw5wmUwG+XwerVZLBl2z2SzWqr0tWBzkSQ5Q0REIBKRVanFxEZ1OByaTSQKhAUjmD7Nn7Ha71BCXSiUJpY1Go3A6naK6opWDRBGriZmBxJDivTYUn88nK/qRSARf+cpXpDWN5zLrvllp32g0YLPZJEzb4XAgm81K7gnVBW63W+w6JC41TZMGIKfTCYPBgGw2C4fDIYMjM4EYLL65uQmPx4PV1VVsbW2JMoIDOe1Xfr8ftVoNmqbB4/FIuxrblvZakZhPRfsWs414DbF5jORFqVSSY0t1Az8L7wt7M2VowVtYWMBwOJQ6eKrHSKg5HA4hR2q1mljNqECiRY9h04PBAI1GA6lUSpRtGxsbMJlMki/EKm2n04lcLidWL4PBgHq9jvl8jnA4jOFwCIvFgkAgICQUG9Wo/onFYpJFNZlMEIvFRMnDPK5wOCz3F25vOp1GtVqVIPhgMIhUKoU777xTsoxIBrM+fm+NeCKREMsg83n8fj9cLte+Onoq/RKJBLLZLNxuN3w+H/L5vBA/JEbz+Tzi8bgQ5cPhUHKxyuUyjhw5ArPZLJXnwWBQ7uEWi0WUQgDEOsYgbbZNaZqG8XgMs9ks6hyee9VqVUh1kpasBacah4o0ko0kTTc2NoSMHI/HkmPGc5Hh1Lqui+IskUjA6XQin88jFArB7/ejUqnIuc/sNhJDw+FQ7Iq9Xg+lUkna/BQUFBQUFBTuDq3VxvzOU4j8v69BO3Ea2l1nL/YmKSj8WOCCv6GORiNpQ2JFNoN3uZpLOTqHSa6IctWYAwBXZLnK6/f7sbm5KSveJpNJapq73S6Gw6G0S3FwLpVKcDgcaLfbouQBgGQyiXPnzomagEQJV4kTiYSEKVOFwTBZhsUGAgEZ7Nl0wsYmAFIv3Ov1ZEjKZrOyQm21WvcFC8/nc1nlDQQCYpdaW1vbZ/kgocRh0+/3SwsNVQ4cwBikysdLpRK2t7cRDofFSmQymYRoY3uMwWDAbDYDcL5Bh/aSvcM7rVUMHCVhtbCwILYkn88neUlut1vUB6zd5vFnZbDNZpMWpmq1Cr/fL6TC3ipjrtoDEDKMdeKDwQDBYFDIufF4jFwuJ0NYMplENpuVOnZmMzHzg3YdZuYwa0PTNFGQmM1mpNNpUTmMx2NpGWIWByuWx+MxQqEQbDYbzp07J0RWt9tFsViEy+WSAZ85LQwKd7vdUlcPQKrU+T/aZ4rFIrxeL9bX11GtVqUBiOGttBuSNGLIMNUm0WhUslB4fTHE12azyfmZTqexubmJ0WiETCaDkydPYnFxUWqnSfSQwOB1TYKNhNldd90lSpvpdIoDBw6gWCxiOp3C4XCgVCoJucPHDAYDyuWyWGPYILW0tIRarSZ2TYvFInalyWSCcrksjUNut1u2kyqQeDwuYd7FYlFC1XmNAectVcx/GY/HaLVaSKVSYpkCzufLUDXERjsGgi8vL0s4NG1q0+lUKsL31pbT4uX3+8X61Wg0RIXH62dv+Dvb8nicmAHDjKjhcCgZWCSMG40Gzp07J4o/Xdel3Yz3NCpxIpEITp06hZWVFSG1aFlrNBpy3dP+l06n0Ww2pRVwa2tLyDiSXLy/9Ho9Oe/5eXjfnM1mopxhPhPfezQaCZliMBgQDAbR6/Vgs9mEwOG9nX8nmO/Fe9K353mxfaperyOTyWA2mwmpzHuC1WpFPB4X5d1wOJTrXUFBQUFBQeG7Q//m338FBYX7BhdM5NB+YzAYpJJ4MBjIl2RN07CwsIBarSbBmgwWZlsNVQgcBDi0k0igBQU430h18uRJqfymXYe5HBwI4vG4rOQvLi7i1KlTWFpakgHq7NmzSCQS+2rDGb7JdihmMDCrg9kdtOTstYmRRGEmCO0+y8vLcDgcuOOOO0T9A0BIIw6tDGxmqOiBAwdQr9cRDAZlwDt79qzkX3BgBIBms4mFhQUEg0Fph2FoM20G/X5fhksqlyqViqgOmF/CxhngW7kaVJY0m02pHeZquNvtxsbGBmKxmKgrqEpqt9tYXV0Viw7bY5g/w+GIyhQ2gXk8Hmm/ok2GldOpVAo+nw9OpxPZbBY2m21fDg2PG5VI4/FY8pFyuRzi8ThSqZSEZTscDrEmseqedgta2mgB2dzclGBb5nnwONJywhapcrmMyWSCRCIhlp1oNCqWqnw+LyReMBgEALHSzOdzyYxKpVKSvcKcGqpUSL7QpkcbHS1w9Xp9XzsPVSq0WjHriecKiQUO9iRO2LZGpczJkyeFjI1Go2IdOnPmjLxXr9eTFrXhcIhQKITd3V3E43EhPzlE7+7uCjnDwZyqKVqlmIlCi9Ti4iKy2Szm87koZPZaXhjOHIvFsLW1hfl8jmg0KscXgIQiz+dzaJqG+XwuJCszmjRNE7VLt9uVEFy+LtVEZrNZlGrj8Vga5WgnJVk6GAwkN4b14CQFvV4vSqUSms0mUqkUTp8+jVQqJSQ2A6ZZ4c3PzOwt2g2bzabcfzRNk2r6TCYjGUwMCuZxorWV2WS0d+XzeQkW9nq9UjnPWnFWxVP9ZzKZ9jV18bwrlUowm81CsjO7yefzyX2VuUi00FUqFYTDYfh8PoRCIdx5553IZDJiuWIAO1Vq/LzRaBRmsxknT54UlVE4HBbbJ3O6VlZWMBgM5HOGQiEhgFlRzswjXnsMe7bZbIjFYj/En1gFBQUFBYUfL5jjMZRf+DDgoZcCajFEQeFHhgsmcmgB6Xa7kt/CZqThcIj19XXJULBarfsqe5vNJkwmkwxNyWQS1WoVwWAQwWBQBuh2u43d3V3JtvF/s1qZBAtDNqlqYO4Kq7lZM80Br9/vY3FxURQBs9lMWo8cDoeoUKrVKhqNhigJqLjh6jizabg6e/bsWRk8ut0uxuOx5LhQFUJlTzQaxXA4lPrzTqeDwWCAhYUFaQciIUX7UiKRQKlUksGExAcJMbZzMXOHGSnhcFgIrkqlIuGmRqNRMk6MRiMuv/xynDhxAhsbG4hEIvB6vVhaWsItt9wilhOj0YhUKiW5HgxepVqGRAeP18mTJ0WlQwKPKgrWtjO3hSqfXC4Ht9stgxObj2h7oo1pMBggkUiIxc3n82FnZ0cCY2ezmVhjjEYjfD4fNjY20Gw2EYvFYLVaUa/Xsby8LE1A4/EYHo8H0WgUOzs7cvzK5TKWlpZEubQ3ALrRaIiViLYsqjsYskvShIMjW3b4majG4ZBMCxjVOhzWW62W2BmZ+0TlFnOQ2HLE7JBWqyXXh8fjgdVqRaVSkUwZkjelUgmZTEbyiUh0MOS1Wq2i3W4jk8kgFAoJUUpFUyqVEhKRNkaG6w4GA0SjUZRKJei6vk+d5/P5cPLkSSE9qPqiCoMNT8lkUkiWer2OtbU1yeDidjBXqNVqwe12iwqGn7HX64lyg69LiwwJ5L3WNJIopVIJ4XAYtVpNbJ5LS0tiD6L6hiqSRCKBwWAgBJTX60UwGJQ8pGKxiFQqhX6/L/ckZoKZTCacPn0aD3nIQ/Cf//mfYumkPajT6SASiUhd9mw2E+KEde8k4qrVqlxbzFiKx+OiguK5SFKa4fD9fh/RaFQa2Xiv0nVdLEsk53kdMHeGgci6routrFwuy/6mHbHZbMr93WazyX2I94ZQKCQqnVqthmPHjgnpxXspw82XlpYAQEK4d3Z2cPjwYWxsbCAQCKDVasFut0vdPdWQRqMR7XZbrHOXXnopxuMxqtWqbIvNZkO/3xer6erqqryXgoKCgoKCwveGeWkRu89axcQP5K5xAw8+drE3SUHhfywumMjhwABAAkL3KhbOnTsnQzyl7bSLMKjYZDLts2OwqcTtdmM8Hu8jOxKJhOSNmM1mZLPZfQNrMpkUsgWAkEgcFpgFQ7KDTT8c3DhI1ut1+Hw+kc+bzWbs7OwgFouJ7ajZbMLlcklNOq1RvV5PBhjaCRYXFyWPhgMjbRvMZuHnT6VS0tpCMoGWoX6/j8suuwydTgf5fF6yZBiubDab0e/3MRwOpSWMyqhKpSKr8qznpa2GyiDmX9D2w+wfZqKw3pf5MQwA5jGjQoqERCwWk9XsVCqFfD6PXC63TwnBvBMeN+YP0VrFQF+HwyFWGw5mlUoFuq5LE83hw4dhsVhw6tQpNBoNrK2tiUrKarXKajrPx3a7jY2NDRw5cgThcFiyjRi46nA4RMGysLAgqh2GU7tcLiGxqCgj6UclCcm+22+/HZlMBsB5C1S/30e73Zahn+Sm3W5HPB6HxWIRgpHNXKzwrlQqYh1cWFjAeDwWqyGtKjabDaPRCF6vF/V6HZ1OR2xtAOR8ZF08j6XFYkGr1UImk0GtVpPA5Xg8LmTIZDIRlQYr66kG6nQ6YvdJpVKinKC6jiodZilRebaysiJ14bTZUS1Sr9dFKWGz2VAoFPYFTqfTaZRKJbHdUe3R6/WEEGbtOjNPAAgZMhwOYTQaEQqFMBqNhPhiIDTVY91uVyycrBUniUgyoN1uo9vtIpFIAABcLpdkKlHpEo1GcccddyAQCEidutvtlnsog9o9Hg8qlYrkWVFxVigUhDBcWlrCcDiUzB2n0ykNTiTveL5S3WixWNBoNBAKhYSEZ5MZw3+ZZ0YCGwAMBoM0wrGC3el0yn2Iih5aPEmKkTCjwm48HqNcLsPr9QrRNpvNEIlEsLm5idXVVcks63Q6mEwmqNVqKBQKWFtbEyUTr0ESpHvtTi6XS/KIeE93Op0IhULY2tqC2WyG1+uV0OlutytEt6Zp2NjYQDgcRr/fFzskr0cAYglWUFBQUFBQ+O4YrUQwdX3zHwagfon7om6PgsL/ZHxfYcc2m02IE1pBmHXT7/fRarUQCASE8DGZTNJqRLk+G4vYhFKr1US9wtBW5qZMp1NkMhlsbm7C6/XKl3Sqc1gV2+/3ZTsZAtvr9YQU4RAVDoeF6CkWixLy6vV6hTyghYctRFSI1Go1bG5uIhQKYWFhQSq1g8GgDNUAxB7D393d3YXJZJKK7U6nIxksHC5JBLndbsm2iMViyOfzMmhxH3J4Z36Ey+USNUkul4PD4RAyjCQMW8UAIBKJ4Otf/7rkfaRSKRkqASAej+Ouu+6C0+lEIpFAu91GoVCQFWvauDiM0RbDNqr5fI5cLgefzydBqcwoYu04w0aZydPv94Vw4lBOawf3C3B+GGczzc7ODtxutxAwVH2Ew2FRaJHco0KIzVP9fh/hcFgCWNvtthAhtHRwJV/TNJhMpn3nrNfr3TewlkolABDS4PDhw6hWqxiPxyiVSlhfX5cqetpaSHQwo4lEIG1CJOp6vR6azSauvPJK3HHHHWI3YwZOPp+Xtik2LzHXyWAwiLqI2UadTkeGZ9p2qtUqQqEQAMg2syp9PB7vq7HmfYDny3w+h8/nE0KUyhRmsYxGI1xyySWoVquiZKFFiSQw7S60LTL7ptlsiuomHo8jl8tB0zSxuvh8PlHxRSIRsTiReFhYWJDX2FtJbrVasbOzI+SRzWYTwoakJUPUSRpwP3OfGAwGZDIZ9Ho9ydZhpTWtYSS5fD4fqtUqAoGA5BjRjrezsyMh2Nw2ZsRw/zKzp1QqSYZRu91GpVKRjC5WiDPUmFa7VqslSkM+zqYmtmHNZjMsLCyg1WpJwPTW1hYGgwFWVlaEzGYAMO1w0WhUjgGvzXg8LhYz5lqlUilsbm7C7/cjEAggHA6jWCzKfZj3OhIuzMCiitLr9YoFk+e11WpFPp9HIpFAv98XO6DVapUsrmaziePHj2Nrawt2u12sguPxGGtra3KuLywsYGtrS0hV5g/l83m43W5pRFNQUFBQUFD47jD/2y0InTjf9miv6Yj89e0XeYsUFP7n4oKJnGq1CoPBIE0imqZJzgkHN4fDgVarhdlshnA4jNFoJNkzjUYDxWIRgUBAbFGpVEqUFrVaTVZDScQwYySZTIq1oNFoyEozBw8O37S8cDWVkv18Pi9ZDQwcZoV3Op2WwVHTNOzu7sLn8yGZTKJSqYiFZnFxEU6nEydPnhSyiJlB7XZbhlQqRtxut2RI0KJQKpWkhpoZG2azGUajUQKkqRZgixYrfN1ut6gNqKBhOGoikUCtVoPJZJIAabfbjd3dXVEecbWZ20zCrVKpyGdh3ToHbAZKM5vG6/WiUCjIcEV1DwNqab1h3sxkMkG325XgWIa2AhA7TD6fh8FgkFYzWoW4T9n2BECIFzYxFQoFRCIRyb8hUVMul+U9uNLPcwqAWJg4PAKQ7B+Go1KBxpBt5vL0+30MBgNUq1VEo1H0+33E43Gx5ui6jvl8LhYU1j53u10hnvbWyDMXidkqDP8dj8ewWq3QdR2JRAL//d//jWAwKLki3NcMpOYxms1mkuFDRQ3/7XQ6ZV+NRiMhNR0Oh2RPsVa7VCrh6NGjsu+YFzMYDCRAnMeAmS7My2Jrk8lkQiaTwfb2NsxmszQ3TSYTuR5YZc3rgERBp9PBwsKCnEtskWKwbb/fx5kzZ+B2uyXYm8eRypl2u71PcePxeLCxsQGHwyEkIa2VwWBQwpRZ0007FJVXzAErFosYjUZS787A63q9LucTq9hp9SMBQRVjtVoVhRezkgBIexPP5UgkgmazKcHH8XgcvV4PKysrmM/naLVaovrjecFsGrfbLceTtru9ocJGoxGxWEysRGxn4/5gFhMr1XkOx2Ix/Pd//7dcW9FoVLJ9yuWyZFbpui62NFoaO50O6vU6AoEAnE6nkIK0MpKw4TVF8qlWq0nGFBvaotEoOp2OhO3z+LHq3GKxiJqMWTnFYhEmk2kf2cx8KAByf9zbaqUUOQoKCgoKChcIXYfrH/4b6X8dIP6xDcz3LLYrKCjcu7hgIocDbDgclhaUhYUF1Ot1afvp9XpIpVLSQsNBgqvFtHisrq5ic3MTjUYD8XhcFBe9Xk+k8IFAYN+AzpVk2kgYAmy1WlEoFJBMJoW42GszYi4Ghx2GbjIvotVqod/vy6BI+X+n08Ha2prYGqj+YcVtsVhEJpMR64fRaESxWBQLmdFoFBKCIb6DwQB2ux3RaBTNZlMIFlox+N9qtYpUKgWPx4OzZ89KM0utVsNwOEQ6nZYcFpvNhjNnzogdiIQLSTe2K1FJYDKZ4HK5ZACk9YcDMzNaDAaD1MyTpGDDFom80WiEcrmMUCgkZIvBYBD7HBunWq0W2u22HAu2TLF62Ov1SlYMg44LhYK0gtEqwRwXDmskFbhfSVRRpWSxWGT/cihkts90OkU6nZbXoOVjPp+LqgE4X8PeaDRw6aWXYjKZSN4HVVHxeFya1ziE770m3G43ut0uMpnMPmUDAFHQMF+HDUMcMieTiVwH3H7uX4Ylc5jmuc1h2Gw2SxscVWAMvaWKxOVyidXv7NmzWFhYgN1ul3BtkqvMdWHw62QykfwRr9cr6g9m3uwN5CUpQKKT1kRmL/GcovKJIcbNZlNylpi7QwIol8vB5XJhdXVVyFKePwxRJlmXTCZRKBSkZWsvmaLrutwjNE0TdZ7JZEKj0YDVapXAaf73jjvuwOHDh2Gz2cSuRoUO7U4mkwkbGxuS1cKwdgYf93o9ZDIZ7O7uihpndXUVjUYDrVYLHo9HqtRpJSK5w0YuABLSDEBanKjqOXPmjBBEJMfZ6mW327G7uwsAQvzxHKMqkQH0wPkmQDYGmkwmVCoVLC0tiRqS5JvT6dx3r2a+Dok2hhWTpKR1zWg0yj2UBMzetikSTKPRCIVCQRSRzCYzGo2w2WwAAK/XK4Hp8/lcWv9oV2RlOq/14XAoikXeU2ezmdj2GFqtoKCgoKCgcGHQZzMY/uM2aOXKxd4UAIDBZsP00Vdg+OQrMXzylZhdfRwmv08FMSs84HHBRA6DbwGIXYXDr8fjkf9yJZZqEw5HJBEYYBuPx8V6wFwWhrtyAGRTyu7urtiRGBrLbdrc3BR1wHQ6RbVaFXsHh36v1yvDEN+bOTBUdXg8HlH1UFFRKBQQjUYlhJT1zwzupe2CVex8nJ+dJAgreEm00BpAfLuyhWoFKmxsNhvK5TJWV1dRKpVQr9dF3cGsEQ7lbKUKh8OSX0NShbkSJMS4Os7waQYLk+BIp9M4deqUKEE48Oxt4RqNRmLDYHMTSQU2wVSrVQkapsKJ+RokRXiumEwmeW/ur4MHD6JUKmFnZ0dW/gHIdni9Xpw+fVosbHvDgmm7YJU7g09Ho5GERzM0m5k/ACQziOHFVCe5XC5RJHU6HZRKJbGxcdsZvA1AAr4ZnEu1VLvdRiqVwsbGBgaDAS677DIAkAprBoWTMKENxO12C0HI8G+Xy4Xt7W1RKpGwYVhzLBaD0+kUtRlfn6HPJpNJiIq9ob0MLKcCp91ui2WwXq9LuDbzWEiKUMEDQEKomZW1sbEhKiYO1Kyi7nQ66HQ6Qvqy9p7WISo8AKDdbouqhYovtpQxF4b10SSmuM+pxnM6naK+GY/HsNvtaLfbEtbL0OButyuWQr5WvV7H4uKiWEFpQcvlcphOpwgGg3C73fB4PPvei41OVqsVd911FxYWFuB0OpHL5WC327GwsIBqtSqkKhVHzH5iJhYtdAwcbjQaGI/HQowC57OcmD9mNBqh6zqCwSC2trbgcrmEkI3FYqIy2hvCnkql0Gq10Gq1kEgk5P5AVRT302AwEPUSAMn8YRA8Lbn1en2f9Yr3ZABSiU4Siec+baPMlOK28jWYMcage7PZLMeQLVuZTAaNRkPUh+FweN/9hkQeG8hoSaRqi+eygoKCgoKCwnnoD7sM2iN/AtojfwKmowcv9uZ8RxidTkyuOoZh2Iyp04ip04hB3ILm4w/DeOmhi715Cgo/FC6YyGHAKgCpj2XNtMfjQS6XA3CeULDZbLBYLOh0OkKecOU8Ho+LLYT1u7ThcOVd13WYzWbJ4WGGDFe0adPJZrOIx+Oo1+vo9XpYWFiQpqdGoyH1tVQQMBeCAxlVNsy1MJlMknNCawMr1SORiKgMaN1hyxTJJpfLJQ1PHGKokGAQaCQSQaFQwHA4FHtRLBbD9vY2hsMhEomEqANoF+FARxuLxWIRaw/zczqdDlwul6z4U2lktVqRyWTg9XrRbrdRKpUkqDidTot6itYms9ksCgaTyYR0Oi31wFtbW7LqzgwTbgetPRzoGPbM4Nput4uFhQVZQefgz1Ds6XQqqh0SFbTKMezX7/eL6okEF4+Z0WiUiut4PI7RaCQ2JYPBgEKhAKfTKdsyHA7FptLtdkUpNRqNRM1UKBTg8XgkeJf7k5ax0WiEgwcPYj6fS0U2SbBAICDZP6wu5/DM/cuMJp/Ph+l0Kgo1q9WKra0tse3wXAsEAmIPYrMSyUTuD24/9y+zfVgvT5KLmUYkXvL5PHq9HiKRiBCxzCQBgHQ6jeFwKCGxvH54bjNviQG6DA6eTCaiauPnYaA1h3mGGlOxEwgE5LOwKYnDOwOtSeDwczebTRQKBSwvL4tVEACy2azYO6lKo+qM1kUAooxhi1y325VsJ74WLZCj0UgqrdvttpBRqVRKjhWVL+VyWVRKNptNyM3t7W2kUilRnAEQlVsqlUKn00EwGESv15PcJNqrut2u5PEwoJrELhVYhw4dkrwd1sMzfJrKKpJAJJhcLtc+sq/VasHhcCCRSCCfz0tYOi2SJJ94LvFc7vf7qNVqWFhYkFa5arUqCieqXXj/JxnmcDj27QdmmzGjrN1uIxQKSU6azWZDJBIR6yOJaQCSb0TCneQyW+fG47GE0ZMMD4VCQhrm83m5LknKKigoKCicVzfMrj6O3tMfAhhNF3tzFO5jmFeW0HjeTyL/SBcKV9lRuMqO3ONDMB67f5Ii4586gmHUco8/66161Tms8IDGBX9DtVqtombgKixtF/V6HYcPH5bGJA6BHNoMBoPka2iaJu1Duq5je3tbcm40TcPm5qZ8ua/VaojFYvD5fCiXy6J28Hg8uOWWW7C6uip2jL1tN5ubm7BYLPB6vWg2m1KHTELIZrMhm83C6XRKDfDeoFgSGrquo91uY2FhAWfOnIHZbEYsFpNwX6o4aOfp9XrY2dlBOp2GyWRCr9cT4sJqte7LWuHqsclkkuwJWmOo8tF1XbIr2u22kApcoWZI7/r6OnZ3d2GxWMTGwxwQALjrrruk8pqvPx6Psb29LVYEi8WCdDotWRokUEiQaJqG1dVVbGxsSHBrLBbD+vo6KpUKdnd34Xa7UavVMBqNkEwm4XQ6JUjX4XDg3LlzkoMSiUTEKtJqtdDtdoUUo32DVohgMIhutwuTyYTFxUVUq1Xs7Ozss0z5/X7k83lRNjkcDjQaDcm+YC4Ig7nj8TjOnj0rlc5suqKiR9d1uN1uqdYm+UDVRq1WQygUwmAwEMLC5/OhUqmg2WxKhTvzZ7rdrthI2IDF36FCiVaTbrcrio5cLgeLxSK5SfV6XYbNcDgsr01rHy0pPN8WFhaE2CFhShsX7Uz8DFTjRKNRyV9hHT1JvHA4LK1H+XxesnMMBoMECvO8Zw7Q4uIiBoOBZEBRocRBnaSYz+cTO5zX60WtVoPdbkcikYDZbEa5XBai6OjRo/JZisUiksmk/H+fzycKFLvdLpalQ4cOSUsbm5aA88qVQCCAU6dOwePxIB6PS+i2pmmIRqP7CBtN02Cz2aT2niRBoVCQe+Xhw4eFsHU4HMhms1heXkalUkGv10MymYTFYkGxWBSrndvtFvtTu92WumzeLxk+DEACfcvlstjUPB6PqMioHBoMBnI9su6b9+dqtSoEJoOcd3d3RVFGRU8ul0Mmk5HMqZMnT8Ln84n1y2AwwO12Q9M0ye5i9TgDkJnlVC6XhRwxGAyitKI1jJYyfl7eU8LhMJxOpygwG42GEIdsLqNtkoHSXEzY2wDn8Xhw5513wuFwyN8k2ktJGvEYs42PuVQKCgoKP64wHV6H8dJDMBw/itz/Po7iw2xoHjKh8wsPhvGbeW8KPwYwGNA/GEE/ZYC+h//Q7ED2SUEYv/md+/6Eifc7EzUzmwG48uh9uDUKCvcuLpjIYSCkzWZDpVIRpYvT6ZRWmEajAQBChnCQY2aHzWbD9va2hMT6/X4AkNYprrhWKhUJ0JzP56jX64hGo5Klw0H3zjvvlFVuTdPExmI0GkVJw1pcbisDgx0OB0wmE2w2G3Z3d+FwONBut+W5tILYbDaxlHHI8ng8ophhMOt8PkcsFoPb7cbZs2eFSOIgv7m5KZYDWorcbrdU5nL1ndkn0WhUBvBSqbSvLtnpdIrFp9VqoVQqSVgx7W3MIWG1Oi0eVBVQEcLA3W63KwHDJCYsFgva7baEjHa7XaysrAhBw5BU5k1QXURFhNlsRiQSwdbWFqbTqRxztmox12I8HksoarPZlBX3fr+P0Wgk+9pms4nSaWFhQex8VClYrVY4HA5RO/n9fmSzWbGUUAlmt9sl/HcymYi1otFoSF4L1T5UWNEiZzKZ5JjS3sPf5+fh0EqSjsoXhg97vV40Gg30+30sLy+j3W6LdYQ2QAZRRyIRNBoNUYTRfsJ68L2tbLQuEpqmCaFD2x/DW5mTZLfbEQ6HpY6cqgvW07P9ZzqdIplMolwuS14WQ7x3dnaQSqWE2GBrE1UPHIR5n6CyzmAwIBQKwWw2Yz6fi2pqNBphZ2dHlDlU4HDQptWN78W8ItqxSNYx1Lvb7QqBo+u63FdozaOi6qEPfaiQoQDkeJdKJXg8HkSjUSFxaU/qdDoIh8OS+zSbzaQiHYDktdCO5vP5AEDCy3d3d2E0GlEoFLC+vo6zZ8/KdkUiEbTbbSwuLkqw+2g0kv1I1Y7L5YLP58PZs2eF7Oa1C5xv9+J91GAwCFHHfKPJZCKE67Fjx3DixAnM53NYLBax0lEl43A4hFglIWW321Gv11GtVuFyuSQoOBQKwel0YmNjQ9Q6JF1IKvZ6PVkYYDg+c5vY9EZistFoIJlMCnlmNBoRj8fFTsvPm0gkJC+J4cl+vx/RaBR33nmnqB7L5bJcH7y3M8uH9/TBYCBKIQUFBYUfRxiOH0X2yRFknxTEzuN90M0Avhkt0l4zwhiPXtTtU7jvYLBaUfmJe1a34IEYN2MAdJWTo/AAxgUTOQwPrdVqYhvhF3jWaLMG2Wg0it2IqgaG4nJ1m0QBB4pKpYKVlRWUy2X4fD4J6OUXaearkOThSj4tEsyGYK7HeDzG6uqqtK3QJkGbCf9ts9mEhOKQw0GFgwGJg0KhIENIKpWS7eNwsr29LQG9zFbxeDw4deoUrFYrLr/8ciE9dnd3oWka4vE4hsMhGo0GHA6HVIaziQg4v/puNpvlZ+12W/YBh41IJILNzU3ouo5MJiMBvgwmpU2MihxWp1MBsrq6KgHSDCe1WCyirmCgMNuQ+P+3t7eRSCQQi8WE1NpLqLBynZYOl8uFdDqNra0ttNttaY5huw4rshOJhNhqaJci0cYGoVarJfazSCQi7UkkCyqVigyP4/FYSCkO6ACkMYvqJFZFUwVFZQ/fczwey3GhYoLnPwCxa21vb8Nms4nKie1Qk8kEbrdbQnepYDOZTKJUIEHg9/uFaNpLwJCso9KDKiyfzyd2GeYoMQyayh1WfDOQdjAYIJfL4dixY6jVajhz5gyi0SjsdjuKxaKEMdvtdiGtTCYTYrGYVKvn83nM53M0m8192VAkt4LBoCgvnE4narWaWA3r9bqEOpOQsdlsCAQCQlR1Oh0A3yI/dnZ24Pf7YbfbxQaXzWbx/7P3p7GWLfh1H7bOPM/zfMcaX/djD6TIDkzJERLYEm1K+qCYdgQbcWLHjh0gjGIgCDJ8SBAjRiAgATJBARJAjiFQtkJJSaRAiZxI4dDdfN3vvZpu3encM8/zPObD7bV4LntgdbO7i++9/QcKZNerunefffbZdf9rr/VbR0dHcLlcaLfbsNlssFqtajvi+857SavV0r2qUCjAYrEIys37ndVqldB1fX2N/X6vqCFdW/F4HOVyWWKZ0+kEcF8jT3cKHU+sY89ms+h2uzCbzYI45/N5vH79Wo1PdJBQHByPx4jH47i5uZGTjzGjeDwuIDjbwnjNMpK5WCzkAhyNRnJQ8b1ilI08o9lshkQioUgU/26324XT6RQInfGr/X4voS+VSqFWq0koTKVSgrsHg0GxlBjXWywWqFQqODk5QbFYlBDN9ik292WzWVxfX0u4arfbuo/x/rharRT5LRaLOie8VwSDQYnPfK/4PpHbxur5brcrRpUxxhhjzBdxLI9OUf4vf0+8+UH/fQFgtf65HpMxxvw4Y5398H/DrYs9zN9+/XM8GmOM+enOOws5jI2Q19JsNuHz+cRI4NNePjklAJaCht1ul/By6AJhZS2fslJoAPDgqS6XeLvdjna7Db/fj5OTE/R6PUFJ2Y7FH77JidlsNhJgyEFxOBy4vLwUsJNL/M3NjUCd6XRatd3kQfCpdKvVQjgcljjCZie6kei6GI1GODk5UcSjVCrpqTxBsk6nUywJunPa7TaazSYsFgsePXqEyWSCfr8vKCwANQFxqWXjDRkwiUQCLpdL7oRSqQSbzaZFudls4ujoCPv9Xks6XS9c4A9bpCjQMTYTCoWQzWZRqVQkSJB1wVhOp9NBPp/Ho0eP1ObTarUEiuV7RQ5RLBZTRTu5MmTfmM1mpFIp2O12LZQE/HLpJwOJEFNGLTabjZrN8vm8Yk4ul0vnYTKZqHHK5XKpKYexj36/r1p6XlNms1kuLUZx6BIjQJbCi8lkUmSHjUPNZhPHx8eqoiacmWIP+TeXl5fY7XZaVvn1WON8d3cnQZExMcYbDyuoGfOjEGexWOB0OnFzcyPmCCufKTKSYcKYICGysVgMV1dX+PDDD3F7e4t8Po/xeIxer4f1ei2Hz2q1Egtpt9vh/PxcQttgMFAdt9/vF1Q4HA4LSszPNLlFT58+RalUkghMkY1/ho1sdGAxrsV7idlsxnQ6RaVSUawHuIdnU+QCIGcUgcCBQACNRkOuocViobger71QKIS7uzu5kKLRKLxerxrL3G43+v2+rhMK5BcXFwJms22JYvgh/Jdgbr43AHQt0900Ho8lcFG4slqtug4ZhyODaDAY6NhCoZCiipPJRPwcMmsonPPaowBEIZNuMYqPq9VK7Wf8LBKm3Gq1EIlEkMlk5Maj+8bv9yvmRRZSu93G0dER6vU6JpOJGFp+vx+z2UwNVGw9+/KXv4zpdIr5fC5hxu/3o16vK9aZy+Uklt3e3or/Re4POVbGGGOMMV/IsVp+pIiT+wdtbCrVn+8xGfPeZr/eIHDzg4UR390e+9Xq53xEf/y4v1X8of/NU5piv/7Td8yf97FmM+j827/yA39ZQqH3fXifqfmx6sfJ86hWqxJxDtuM6OCw2WxaKvlDdyAQQCwWU9V1q3VfSccmGi7/ZHZwAaRw4Xa7xVDY7XZahPf7vZaVwWCgJ86MGESjUbhcLpTLZTWdBAIBwW7fvHmD/X6PfD7/oJ2HkRjCUUOhkOIMVqtV0FgKT/w9ChGz2QztdhvAvWDj9/vRbrflmuDvEx47Ho91fKFQSNyf4XCoSl8ugXQBuN1uxVvW6zVOTk5wcXGB9XqtpZCOGMbOWKvN18Rjurm5QSqVehAlopuiUqnAbrfrvY7FYuKTUGyIRqOYz+eqqna73ZjP5wLLXl9fw+Fw4OzsTOKI2WxGs9mE0+lEOp1GuVzWIprNZiV40InCSBgdF2yAorhBwYLL+2g0EsOHopTD4cBkMtHyTQHObDYLBpvNZtWu5nK54PP5cHFxgUgkIqGN1waFPoKbD2unGc3rdrtyYtHNRp4IBU1GyShsMKoDQJG1cDiMYrGohh++vw6HQyJGNpuVS4uuKqfTiU8++QShUEiLOl1V5LF4PB7F18rlshqpKGYEg0G9v6vVColEApVKRVE6u92O/X6P0WikymdGAwm6poONohjZN8vlUkIY36fZbAa32y2u02QyQT6fl9DFc0VBLRqNYjqdotVqyRnC98vhcCCVSqHb7aLb7WK73SKVSulzREYXxRkKUQQyW61WhMNhtFotCV9c8umcInCYwvJisZALiCwlMpAo9PI+dxh3CoVCqNfraDQacua4XC4BmCmWM6rHayAWi0lcp9AcDAYRDocxGAwEibbZbPD5fGg2m4jH42g2m0gkEoLHLxYLiWAU3CeTCQCoRSyVSqFQKOD169dqifJ4PLpH8f5GkHM0GgUAOQ2dTqeEy9lsJqi40+mUK7NSqcDn80ng3+12mE6n+jyaTCY5oprN5gN4/CEkmuJns9lELpcT581qtSIYDOL29lZNcIPBAFdXVzg9PQWAB9FFY4wxxpgv4uyLFfj/6OK+AxLfXCH/n9xh+/ry/RyYMe9ndlsEf/sTBC93MB8YsXzFPcL/1xfY/ylkyu36ffhu5zCv9/o983qPwOUU+48MN87PbUwmWIIB1P+738Cb/zCO1G8Uf+CvN3/jBJ1/61dgsv4QBdmYB/POQs5h7bfb7Vb1OKHHXN6tVqtao/b7vaCY6/Ua3/3ud+H1euF0OsXbYXTn9vYWx8fHiEajij6x9YcWf/5fclMSiYSeoI7HY9VVn56eCkjLxhqr1ar4FAGdk8kEJycniEQimM/nYkUwqtTv9xXv6ff7DyIRBGRuNhstBpVKRdEB1vOyeYuLNV09drsdtVoNmUxGDTGr7ynZXHhTqZRcDWwJIlcjGAzqdTmdzgcgXS5kbElihIKNLaVSCaPRSHXdhJPe3t5iPB7j+PhYEOtQKIRIJIJCoYC7uzsJABSrGLnirNdrLeV0rJCBxEgXQcdsgaJzhsvao0ePUCqVVI9NngnfJ0Z06AKiEEIODAHbANQ+w4WSIkYkElFjGVukAoEAvF6v4kyssmYkg3EOiizL5VILNHC/qBYKBTVTsVqZ79N+v0e5XFaEw2QyKaL4+vVrnJ2dIR6PC+S83+8xm80kKNHhAAD5fF6xLzaHcflm+xndJXQYsImN4t1gMJAQSDHQ5/MhnU4r4saFudVqoVAoAIDamvL5vBrSCNOm44NCJKHfFPi4TB9GL3e7HdLptJxkh64lilZutxv1eh3n5+cSJQnF5j2JYHBGYoD7qBvdN7FYTAs/IzRkO9FtSCGWCzy/HkHtrLIn7+nm5kYuxU6no1p1iiW8zsmpYbsUeVV0tvD6IbiaUVWKZ+QuscFtOp3KSTgYDB7UbPN7XF5eotfr4ezsTAD1L3/5y7qPbzYbrFYrRR/pGjyEaZdKJcTjcUSjUdTrdTkrTSaThEWHw6H7+XQ6xe3treKHrHVn1I0RWboOCVv3+/1iI/n9fqTTaVSrVbk51+u1BE6+t6PRSO4a/j7vPVarVQ11hO0z0kt3JgWcRqOBbreLs7MzRCIRfPvb35bQvdls5DYyxhhjjPmizW42Q+B6Bm/pfgl21/ZI/84C9n/0bcOJ8wWd3WwG39/+feT/5gUCVzuEX28R/I+/CXMkBNNXnsP0led/qmq995sNTL/7MXx/77vwVpfwlpfw/d0/wP5bnwK77fs+vC/MLH7tF7H9T33I/loRT7ONH/rnnh3VkPiNOzT+nV8yGsXeYd5ZyOEyW6vVYLFYkMvlMBwOcXR0hLdv3z4ArdrtdsWhGDEwmUxyKNBuf3p6CpvNhuvra4RCIfFryAaheMQfpLl48slso9GQG6fRaKi6GYDYJQAUrWDFOHkPZLj4fD68fv1aLgpCVSng+P1+xGIx1Ot1wYrJkODS7Xa7tdjsdjukUin4/X6Bj9fr9YMmHsZ6EokEWq2WXjvFot1uJzcQBanZbKan3WyuCofD6PV6CAaDiniRfcJIEFksdLHEYjEtkQSxZrNZuN1u1Go1xaq4qPd6PbRaLSSTSYl2m81GcFAu+7vdTgJTu92Wo4RwVvI0CoUCLi4u5OjitZJKpTCbzeTAorMgmUzCZrOh2Wxiu93KbeX3+1XJbjKZ0Gq1VBVOoY8sHQBIp9M6L6yubzQaODk5wWazQbVa1XXG97Pb7Qr6Sj7OaDSSq6fb7SKbzUqsJDyVDiFyX7iMUzgAoAan6XQqh4/FYoHdbpfAQwcBHR50Z5Arxc8mW6h2ux1yuRxarZZq14PBoISVyWSC4+NjVZPPZjPFr4LBICwWC5rNptra5vO5llnyTzabjURTNpARBs0oEeG8u91OohTBxLwOCeslD8ZkMqk5jtcGG64O4dRsXSMwud/vy4XldDrh9XolxLE9jJynaDT6gElERxTbzwA8cIskEglMJhM1GZnNZgl4FAcpCNlsNkUAyVkhoJj3IwpxZAIFg0E0Gg3FA00mkz63zWZTnwN+5oLBoERMvl42ZhGEzoasVCqFu7s7CWwU+ciqYrSK9xqKLHTgLJdLPHr0SI4+grhZab7b7RTppNBNgQS4bwMrFAoPgNSMcPG942eTAgsdmxaLRe8VhcL1eo3hcKjPCIHv5JJRyGNkkw1edMDx3yXG4drttkD2FK8B6GuyeY9tZMYYY4wxX8j5vU8Q+dvfQf4fjhH/rZew/JOP3vcRGfO+Z7/HttOF/z/5PXj+zu/Dms+g8utZlH4t8L1fYey/8eH7PsoHs18uYf7/fAfmf/od7Deb9304X6hZ/sVfxPLf6cFm2cJs2v+xf95s2iP5l+/Q/Td/6edwdJ/teWch59BmT1eJw+HAxx9/jGfPnqnJh+DgTqcDh8MhZgeBpp1OB7VaDc+fP1eT0iHXgTEXRmk8Hg9ub2+RTCbVJJJMJsVu6HQ6WC6XciRYLBb0+33FKhjBePTokf48Fw2yLrrdLjKZDBaLBYrFolwo2+0W5+fnaLfbmE6nsFgs2G63YoZks1lxTsrlMtLpNCKRCADIFbP53s0imUyiVqupZYULL59eM2rB+AZFhlAohEAggGq1KiGIS4zH48GrV69wcnKiJ98Ee5JBwifasVgMLpcL3W5XtcIAxCWpVCqYTqeIRqM6f6VSCfv9HlarVa6CXq+nRWq9XuPo6EhcEo/Ho+iTyWTC0dGR/rzVahWU9erqCn6/X3XJrVYL6/ValcF0MpH/c3Fxgel0ikKhoKWtXq/D4/EgGAyqxplA2MViIcGEbp7T01P8s3/2z5DJZOB2u1EsFtFqtfDhhx9iOp2i3W7D6/UiHA5rqSczhAvwZDKRq4JOMwCYz+eqLmfsK5PJKMbDa6VcLsPlcqFWqyGVSinCQyDsdDrFaDRSyxsAuZW45NIhxkhfMBjEcDjEfr9HNBpVNIgsFafTiVarhe12i3Q6Lehxr9dTlJBCDR12ACSseDwexb7IQ6GDhM4bvse73U5xFtZF87wwrkgnicPhEGi3Xq9L2KALiTyYu7s7ibVc0M1ms5Zxn8+npf3TTz9FPp+XGEW3ERvSKAzys8PFvlQqPXD20UkG3HNTcrmcXIh2u13RQ8avOp2OmuR4n6MLjPe+YDCo65xuG4KqGS2kABaJRCR0ZbNZVd3b7Xa0Wq0HIiWvt3A4rOuQIuNisQAAsY/8fj9ubm7ktIpEIvp88BqnEM7IF5vxXC4XRqMRjo+P4XA4UK/XkclkBJN2OBy4vr7WPZtuwGazKW4NcC+mUkDkfczlcqFarYrftd/vdQ/z+XxyzZBztN/v9TUzmYycN4TITyYTZDIZ9Ho9fOlLX1Lslg8MXC6XmDonJydyX5KNRVHz7u7ugbvPGGOMMeaLOrvFAvtvfYrt9xiNxhjDseayKP3VLNb+P/y9vQWo/jkPdn/2K4Dxb+gXesxuN5pftyHqnv5Yf89q3iH0r1Qw+au//DM6ss/HvLOQwyhSNpvVE2xGdfjDO2tmyUyhvT2TyWA0GqHZbMqF8vbtW7x69QpHR0eCn9JRMBgMcHNzg2w2C7/fj2AwKIGF8M9oNCreAq30JpMJXq8XNpsN7XZbrBO6MMig6HQ6cnWsVis9abdYLMhkMnKPxGIxuY3YVEUBYzab4fr6Wq+P7A86ShgfKxQKsNvtit/MZjN86Utfwne+850HUGeyPmKxmMDD5IWQBUShwePxaPlkvfPp6SlGo5GWRrvdrgpitiURhsy2GbZUEZTKmAYbr7bbrQQyLqQ+nw+NRgObzQaRSETMHi7a5NYQVsrIFI+fwNxwOCwXBVuuTCaTWowYrzt0NwCQiESGBkHNdAtwEWTtPF0DZCO1221UKhV84xvfwHa7lUPKbrcjk8ng5cuXerrP64JCAM8LnTeM9yyXS/FKDlk/iURC0GEKD4cRRZ/PBwDodrtyTDx58kQ12RTUDsG9FPQYF2Eb0cuXLxGNRlVZTecFXQnj8VhV04QHs0Kd4F06NyaTCc7OzlCv19XMRScGm4fYoMZo5Xq9RrPZFJ+EwhAdK/ys8j1k2xAFQC7RFGk7nQ72+72ENEK0D/k+dKhRTMrn84rbHMKj3W634lTBYFDMIkbRHj9+DJPJhJubG4kCmUwGpVIJAOSsoqDEiKbL5RKU2mKxoFar6Xh7vR7Oz891LorFIjKZDFKpFK6urnRfPT09lTBC4Y2OpEgkgru7OzgcDlxdXSGVSuFLX/oSLi4u5KBZLpcIBAJwu90CQz958gRv3rzBdrtFPp/HcDhUJHC9XosbNhgM1LZHqLnNZlNN+nA4hMlkwunpKV6+fClR/fT0VJFNr9crJxEjZe12W9B3h8OBfr+PRCIhbpXdblelPQVoOjufP3+uex6dddVqFcfHx3A6nRIEyU8aDodyJ/HfG3Kx+v0+lsulAN10PfGeRVcmry9Cktn8RVeQzfZDqlaNMcYYY4wx5gs+zX8hh63j+39/bwGq/5wL5u/9PGbMF3P2z05w+s/f/kR/12HZYOMwhMAfNe8s5NBuTys/l+RQKIRGo4FqtYpMJgOr1SrGAmM91WoVVqsVuVxOEZzDul66dxhdIeuAP4yvViscHx8Llskn62TG0IUB3McB+DSei04ymcTd3Z2eAPf7fSSTSVn7KdTYbDb4/X6MRiMt+aenpwLJVqtVxONxteoA97XXpVJJ7iKCfBmnoVMBABKJBEKhEG5ubpBMJjEYDBQdcLvdaj0iu4XwVEaa+P0pQnFhyuVyqsxmpbPf75czgULNeDwWX4T8nXK5rNgB3RObzUZfj0s2GSR0wRSLRS3ohAjv93ukUilBp7lYMYbTbDaRzWYfQHYZM6vVatjtdnjy5Il4JGazGb1eDycnJwLVMmpzyF4ifNfhcKBSqQi8ulwukUgkFO0B7mucQ6EQarXag2ppsnHIxjh0KRwKL8PhUIIX3TEUNPl7JycnqNVqmE6ncj7x2jg6OsJ6vcb19TUymYw+R/v9Hh6PR2IIId4UAPkaeF2TsZJMJtXWxtgj43/z+RyvXr1CJBIRfNzhcKgKnM6fPwoYj0QigunSjcVIlMfjQavVQqvVkgA7mUywXq8Vrdlut3LHEfLs9XrlpDgERFssFr0nFOB4XLxGotGomo/m87nibbxGGK9zu924uLiAz+eTEMU6dN6LyL+azWYYDAbIZrO4u7tTBfhqtUI8HserV69gt9uRy+VQLpcl5Pl8PjXkUTRzOBxoNpsSi+x2u96jfD6PWq0m4SIUCiGRSODq6grZbFZiw2630zUM3Iu7dBSaTCacnJygXC5LcAHuBbF4PC43Cz8bhPS6XC79OXLBeA0w8krXGoHMq9UKXq9XoOV+v6/vabFYsFgs0Gg05JriZ6XdbstV1Gg09Hd9Pp/iSxRA6VLLZDICczebzQfiGBvOKILSObTZbJDP5/H27Vt8+umnyOVycmUx4sc4JAXsarWKaDSKZDKJbreL8/NzlMtlnZfpdCqHKV9Tt9vVPYHn0RhjjDHGGGOMMcaYdxyTCRf/Tecf/+d+xEz+0gjWVPKndECfv3lnIYd13Yfxil6vh/F4rB+k9/v9A4bEYaVsIpEQVJOWeFbQsgGGNcVcfrlYEmC5WCxwfHysCA2By3wC7/V6cXFxgaOjIwyHQ0ynU5ydnUmA2O/3uLu706Lqcrng9/tRrVYVVWEkodVqIRaLqbI5nU5juVxqSWDUbLVaKUIRi8X0NJ3wUTpcGBniss82oHw+j+VyidFohGg0Cr/fD4/Hg08//VRNQmyrOqwSZySGdbp+vx/lchmBQADhcBiz2Qz1el21yTc3Nzg6OkIikcDt7a1iG6yyZuTk5OQE4/EYsVgMDodD0GibzYb5fK7zyEpvp9MpSHQsFkOtVsPp6Sn6/T4ACPxL98xkMlE7Ed+nXq+Hx48fY7fbYTgcwufzwWazodFoiCdDwYK174Sj7vd7OQ3ojGi326qFn81muLq6EtMjEAjIRbbb7XTcqVQKq9UK4/EYw+EQ9XpdUaVwOAyn04lqtYrT01PVEXc6HYzHY+TzedjtdphMJsGyCYOmK2U4HIp/FAqF5EhiSxkbs16+fIlCoSAhg3Gb7Xar64jv/93dHabTqbg1jx49wre//W04HA65LdLpNK6urnT98lzt93sxRSaTiYSNRCIBq9Wq65NOnVarJS6V2+3G0dERbm9v5YLjZz0Wi0m8oyODLCUyX1arFUqlEp4+far7BnlEH330kRwyTqdT17nX6xWPipEoul8IVF6v18jn84rYsLYagNxMvNe4XC44HA7c3t4im82KbdTv97Hb7dTgRRFjMpnAbrdjNBrJ9UfBhDGlSCQix4jT6ZSjJxqNIhgMSsQil4ixLkbxhsOhKr/dbjcikQjC4bAA0rvdTi41Rp56vR5OT0/FvGJE1efzyXHU6/VwfHws1yGFOEbS2NJEeDlBxax4j8ViEkgphvMeR/YN45ObzQaZTAbX19eKi1KAodDEc0uxha+Pr3UwGCjiZTabJfzQHdhsNhEMBnFycoJOpyPOVKVSUSMWRSOCqClyut1uORcjkQjK5TJisZicltFoFKvVSgLmaDTSvcwYY4wxxhhjjPnxZvvhGayZ9Ps+DGPex5jMiKWGf6IvcRzpAXbDGf3D5p2FnOFwiHg8rifIh3XQfFpKngerqbm4s/HE4XDAarXK/UH3AOuVB4MBhsOhGoQOq6L5NJ1PZ3e7Hfx+P8LhsKp1Z7MZ4vG4ALEmkwnFYlGLicPhUIMQ4yYulwsWi0VPb+kmYtSHEZqLiwtYrdYHUY7xeKwGJIpXfO2bzUZLJmMohJrudrsHiwIjMnTxmEwm5HI5MWsIE00mk4rNcCknL4YLFqNHZHXsdjvBWhuNhtw43W5XMYntdovj42PMZjMxSehO4fHRPUOnznK5lJiyWCxUL00HA0WZzWajJ+DpdBq9Xk9NVaPvZa3pHmJMiLEuvm+HgFtydpLJpOJ4LpdLsS7GNxaLBWKxmL7OcrlUvIdQ7vF4DIvFIucTRRIugjxf3W4XwWBQjWrD4RCJRAIulwsul0vnptfrydUQDoflXGP0gy4UMmno5GEUj8f88uVLCX90sdHFQqhuNBrFbDYTd+bs7Ay/+7u/K7dGOn3/jyYZH+SSEMJssdyT4JvNJo6OjhAOh2Gz2eQkYpMU3VSMmmWzWUynU9TrdcTjcXi9Xr2PFOEYVwEgvg6FLIqCuVwONzc34lF9+umniMfjev8BSLgh7Jpfx+/3w+l04vb2VhBq1lLXajXY7Xb4/X74/X5st1tcXl7C4/EAgJZ5ti/FYjG0Wi34fD6Mx2M1Uu33e4nG5HCt12vE4/EHbX1sGdtut7p2JpMJkskkQqGQojwUtSqVioRBnntyvAhEpqMPgK7LWCyma4WMLr//PpDO88/GqlAoJLh6pVLR/QCAnGkUPCg+8l7Mz34wGNT1vF6vEQgEsFqt4HA41NzH10vAeKvVEmOIkaqjoyNV0PPeYTKZ4Pf78erVK0WYCJqvVqtqc2MDIBvXKLR6vV5YLBaxoMgrCgQCEpuHwyHMZrOEeZPJpGYuClAejwfn5+dYLpeKgR7yqJrNpphAxhhjjDHGGGPMD5gfxa41AZU/70Ht149giUZ+bodkjDFflHlnISeZTKqCmByYZDKpxYzslEAgIPaL1WqVm8br9WrBo219OBzCZrOh1+shHo+L12Cz2cRuYVuU1+sVY4RLjsViQb1eVySK34NMGS4tjUYDuVxOVd0ErJIRQ1YOIzOMR9CV0Gg08Gf+zJ+RkEReCZt62HjCVitGGPb7vZ6cx2IxHB8fYzAYoFKpIBAIIJPJSLw6FDAYrdlsNnrC7/V6YbVaEYvFVLlNIYGummQyiXK5rCjUdDrVcuJwOBAOh8UHSiaTOodc+ObzOebzOaLRqBgZbOshl4VL7ePHj1GpVAS4Zm1yr9fTnyc0loBoRuTI/vF4PGKlkFVTr9cRiUQk1jAyxIgN3V0XFxe6fngMjB/RIUDQr8PhwHa71UJJN1E0Gn0gTFHIGY1GOD09RSAQQLPZRCgUwsuXLwX5JUiZ/Bm6h7LZrFwOXPZZO77f7x+wpQiarVarMJvNsNvtEgmcTqdArxQl6RKggMZoDlvNer0eCoWCAMbT6VSi1snJiQTSt2/fKkpDhwV5VFzMa7WaGCJ2u12wcX7uWTfd6/UAQO1S+Xwet7e3mEwmePr0qT7bjBCSP0W3TalU0jVy2M7Fmmlet3TeJZNJLBYLiSp+v18NXoQDJ5NJtRsdQtC73S7i8bjq0i0WC05PTzEYDBTToqBGcZcxSfK3stks6vW6HFmTyQTz+RxHR0coFouYTqfI5XJYr9eq4maj3XK5RDQaBQC0223dV6fTKZ49e4YXL15guVwiHA7LmUK3Cx17FG9tNptaBBlbHY/H+PKXv4z9fo9Xr15J8Hz27BmAe7cNgc38GhQ1Du/PDocD7XYbwWAQVqtVIhG5SHd3dxJ9yEai2EPeFa813osPGT4A5K5KJBKwWCzodDriCU0mEyQSCaTTadzd3an5qlwu656w2WxgsVhwdHSEfr+PSqUiMZhiTzweh8ViQalUeuC2o2gUCAQkbo7HYxwfH0sk52fn6OgIl5eXRrTKGGOMMcYYY37IxP/OK9gHP/rPLCNA7V99/HM5HmOM+SLNOws5+/1eTSwAxFGgU4bxCAIwW62WxA6yBu7u7rDdbvH8+XPBaQeDgZ78TyYThMNhDIdDLVGLxUL122wSopuAME4+kbVYLGg0GgiHw2JycPkrl8uKaPR6PYGUq9Wqlm+2YvHpOWu0E4mEKmjZKMXab8Y68vm8QL2HvBuXyyVWh9ls1pJHqDMjFGyFMplMqFar8Hg8qkBnbIvwWboPWOPsdrvR6XQkTJ2dneHu7g6ZTEaRJuDe4UBH0mQyQaFQQLFYlAiTTCYRCAS0CJO1E4vFBCmlkMY6ZApeBCgfcmv4fymW8etRyKI7gk1O2+1WrgQKgJPJRFwZCmez2UyihdvtFig2m82iWCzi7OxMzWlcok9PTyUWUUTkdc33is4Wr9eLq6srzOdzuN1uRZd4/GzmInz6zZs3Em8cDofEDroa2MxDJhPr6HntEnrM80e3jcvlQq/XU5zxsCVquVwKwE1+Sq1WkwvI5XKh3+/j0aNHiux0Oh0UCgWMx2PF73j9NZtNNQnlcjkUi0W5yfh+h8Nh8W92ux1arZaOlw4mj8eDbDaLSqUihxkFE76/jKocHR0pvsMIDD8jzWYTz58/R7Va1TlizI7tTABgs9kEHGa0JxgMyhFyd3enJitGdADIwQdArBu6c9gsxVjnarUSi4cCGCONbA17/Pgxms0mdrsdMpkMXrx4IeGW94RDNw5ZMdvtFpVKBWdnZ/pew+FQDhreX8mgYiSQdfCEwfM+5Ha74XA4UK1WEQqF9NkmIJpcocPI3unpqQRjiqij0UgRQDLCXr9+jWw2q/Y/xmHdbrfu27FY7MH7MhwOMRwO4fF4JP6RK8UmLlbTbzYb/f8UcHid8f0nVJqiY7/fRzgc1msMhUKYz+dq6OPntd/vI5fL6d8vAHIGLZdLdDodvbYXL14IVh4Oh8WpMsYYY4wxxhhjHs52METmt66R/N0lIi+2P/zP2X+OB2WMMV+QeWch5+3bt0gkEnA6nbDZbGK2sGWFdcEXFxcYjUaqhWbk5zBuMp1O1dDSbre17JAlQdGgXq9juVyiUCioyekQuswoEYUg8ka4JC+XS8VjGFlgNIpLLx0OhM3e3d3B5/Oh2WzqOAAgHA7rySwXk/V6jWQyifF4LLYGa5TpLCBLJxgMSgwC7t1KfK1sidntduj3+3A6naohpqCx3+/lfCDrgvGOxWIhrhCf3lMcKJfLOveHrU4A1PQyHA4RiUTQ6XQkUlgsFi1ldK2wQjkej6PRaCCRSODly5c4Pj4WdPn29lawUC549XpdTqjhcIhQKIR2u41AICCxL5FIqFqdbTSHgh3dSJvNRjXJbMFiC810OpU45/F4tNQ6HA589NFHyOfz4p4QJBwOh3Uu4/E4zGazomoAEI/HcX19Ldg2eUuEtI7HYzx58kTX9HK5RDqdxmq1Qq1Wg8vlkng3GAxwdHSEm5sbuQeePHmC+XwuSOt2u1X0h4ttr9dDOp1WUxN5Sul0Gre3t2g0GojH44IxEyZOVxJjNITHPn/+HK9evVLkJxgMql2r2+2qvYiNVv1+X01XPp8PdrsdtVoNT548kRuEUSryayi40VXidrvlduOxMGJGSDbr7hkPnM/nEhLb7TYWi4X4VmQQuVwu8bMIQyYg2+v1olqtYrlcIhQKKYJFUZU8pP1+j9PTU7x48QKhUAjRaBSNRgONRgOxWExRJ0ajACCTySAUCsmlx1ggo6cEgfO+Q4gzAcZOpxNPnjzBxcWF3isKRul0WsIhhR+/3y+BhfE1OqvInOp0OgKBp1IpuUvoZuR9q1arYT6fw2w2Ix6PY7lcKjbFOB9F6kQigWaziXQ6jclkItcgAMVKKUzSQUjXGc9zLBZTvImV7XQs0iFHIZ8tVna7Xc4hHjsjpeFwGJFIRHwhh8OhiCTPL2Ny5BAdHx9jMpmIw8ZoLc8Bj4OuJ6vVinq9LgegMcYYY4wxxhjzg2fTaML2j/8Anr//Bz9SzDHmCza7LRx/M/wn+hL9hQvY7n5KB/T5m3cWchKJBG5ubiQK0AXCWNJ+v8doNEIsFpMbgMIJK5M9Ho8qYNvttppL7HY7er2elg7CKGmJB6DIUq1WQywWQ7FYRD6fV2vSer1GLpeD2+1Gq9XCZDJRTS6hn3QAtdttuRgikYgcKu12G/l8XnEgr9eLWCyG6XSKbreLcrmM9XotQYQAU1ZcO51OlMtlxSPa7bYYLa9fv5b7JJFIoNFooNVq4cmTJ1gul+j1egiFQloKo9GoGCKM7xzGNdgIxSfM5PGQWQFAT8T3+72+F90cHo8H7XZbbgh+jUQiIYfDer0W9Jnn0Waz4e3bt3jy5IniP6xIHo1GirvE43E5plKplOJDnU5H7B6+L3RRkJdx2PzFamCr1YpyuYyjoyNst1s0m01Bp1OpFBaLBW5ubpBKpeD3+wXYZRsSYxY8rng8DgBatPk6yL9hzXuj0ZBYmUgk5K46rELmuaaIQ3YSr51D7lKv18PR0RE6nQ7W67VEKb72eDwuaCuvJb5OOg02mw0AKJrEZToWiyk6xPYxq9Uq7g3ff77+TCaDbreLq6srLBYLJJNJ+Hw+XF1dwev1IpvN4vr6Gl/5ylewXq/1uTSbzUgkEmi32/rfbJ9KJpNot9vIZDISNHlOKR5QuCBXhRXyiUQCiURCLh3WjV9dXeHk5ARmsxnBYFDXkMViwWg00meQMGsAcoxEIhFdb4xFbTYbCbi5XA7j8VgOtnA4jGKxKDbPmzdvxOxiZJKRnENOEUVefg6Ae7cX3UHValWQ8EAggJcvXyqiSOAz76MUMfr9vsRwupGcTqcieuTNzOdzsazoAKRIzHgcRUuKI8Fg8EFMlE1bnU4HkUgE+/0ei8UC1WpVMc18Pq+/R1dgr9dDpVKReE6BlWw0CjSz2UzXKiObdNDwdZLv0+/35fDr9Xp6SLBcLmGxWBS7JSOs3+9ju92i2+3C6XQiFosJrk0H5XA4lDjIzxBb4oB7ob5SqSCZTMJqteoebDa/8z+RxhhjjDHGGPOFnv1mA+/lEJbF9/83/50h8HDMTicsj07vfyXi7/twfqbjKU//RH9/839JYFOp/pSO5vM37/xTqsfj0Q/EXJ4IO2ajC5f+aDQquGy73RbDYr/fo16vy2bPZZ5xKbYIPXv2TFEQgmyn0/sL4cMPP0Sn04HZbJbrI5lMYrVa4dWrV4oaABB/hZBLAHC5XGJLMO5SKpX09Ji1vIyX0K0zm83w6NEjiUiNRkNV63wyzzYqMj1yuRyGwyGur6/l/OCTfeB++R+Px1itVmqJoiPl7du3YnMcnk9yIrrdLiqVCpbLpZqY2HZDUWgymeD8/ByLxQLNZlORmMNlkZyX+XyOQqGAZrOpJdLpdCqesdlsMJ1O4fP5BFjm+350dIRWq6WaYTZl8evw3KxWKz3ZZ4Qkk8lgPp+jVqtJ6GILDcUNgmqXy6UEDbYPkaVCp1Kz2RRbxmQy4fb2Fna7XS6FdrutyBqPhXwPsnrYzNXpdFRVzYasVCqF6XQqITKdTsNms6HVaim2MhqNUCgU0Ol0BPEG7sHCvPb5elwuFwaDgWqpq9Uqut2uHFGMimy3W7l26LAg14RRrl6vh0gkomhSNBpFsVhEKBRSHMhqtco1dRh7ZIMQa8u5/OfzeQmZkUhE54+MFnKVLBYLMpmMYMP9fl+sJt4r+L7w/WO0idfZZrNBrVbDyckJAoGAmrIIe6YjiaIbl2xGZAj2Jv+Fx0Fo97Nnz3T9M0JFN5LH44HX60W5XFZbmMlkQjAYlPuK0Sev1yt+C1lCjG3y3FGcGwwGiuYRIk0RptlswuVywW63Y7FYSHzjZ4XRoclk8kCA8fl8cDqdcgESkl0qldSoxfeBx//1r38dL1680D2RAiQFLd6LDt2L5DIRHnzIJmMkkTFWCoGLxUK8sWazKdaX0+lENBqVkEigO78Gzw2Flnq9/oCxdQjf9ng8AqozZsZr3+/3o91uK+oLANvtVo6gaDSKi4sLXeuBQEAweJPJpGve7Xbj8vJSTiNjjDHGGGOMMeaPn92LN8j9dhOx726AHeDoA4W/14f/73/8vg/tT82YgwH0vx5D/+sxbE5S7/twfqZjvq7i4veOfqK/+/Img+g/rf3xf/ALPO8s5HCRZCsUf1inC2O/32sRubq6Qjwel1tiMpmg1+spbtVsNiUA8YkzI1i9Xk/8g0wmg91uJ6GBSy8XDLp2GOsgRJdLncViwXQ61Q/twB+CagFoWeETZofDIaGJkZBD2KfL5ZIoQ9YOz0swGMR4PFa19Xw+1yLPBZrRJTay0L5PcGi5XFZjSzqdVuyJ3JBoNAqXy4VyuazqcrKABoMBTk5OBJdlWxF5FIxybTYbhEIhNeVwwe73+6ozp1OEwpTdbpcYkUqlYLfbVXXOpZRxCi5rBKBmMhmdK4fD8aCqnE6Ofr+P/X6PaDSKVqsF4P4JeblcVowvFAppuWZkiI4kMldOTk7UEvX06VPc3t7i5OREQNbVaoXpdCqRZTwe69o7bBtjg1AkEhEI1+PxoN/vS6ihY2c+n6u1igLQdrvF9fW14i2MEhGWfH19/X2vZbVaiZkUi8VweXmp6BlwH8XLZrNa6sfjMWw2GwKBAOr1utwQbrcbTqdTcbHlcolIJIJoNIqXL18im80iHo/j5uYG2+0Wy+VSDi2+vmQyieVyqaWZThIKqMvlUiIORQer1So3Dt9rHg+vqclkosgbI5WPHj0CAAkAjHuxJtvpdCKbzerr0sHHv0OI+Xg8xm630+I/Ho8FqmY7GJ1O+/1e0ahvfetbqoIfj8c4PT2FyWSSUMHln4BiilJ05vG9I3+JUPHNZqPvt16v8ezZM+z3exSLRUU30+m0YoN0T8ViMbx9+1a8JUa0CA+vVCq6B/r9frhcLiwWC0wmEwSDQfT7fTx79gzj8Viixmg0UjzQbDZLjDv8bDPCyveZ0b1eryeHHKNys9lMwk+9XpeDjTwzp9Op+0ogEMBgMJALyev1Ih6Pw+Px4OrqSueaESwCrzebDW5vb/HkyRM5nngvIwuI97TlcilxE4CidnwwQBck74mEhzMSzKga7+PkQDkcDgl8xhhjjDHGGGPMu8327TWc/7c/QOF/+RES//tvY/fxa+wWP8Cm8wWd/XoN63wP0w6wjj7f52Xb7+P0f/wR3v7O0Y/1914V03j6m5fY3N79bA7sczI/lm+cQOBEIiFoJQUT1tOyipaMFy6+XMYYR9psNhI6Op0O5vO5xCA+8R0Oh+j3+4jFYrDb7bLWm0wmhMNhiSOtVgsWiwXHx8cAoCpocnUo0vAJOJ/8kj+TTqe1DHk8Hi0ty+US+/1eAF/GkLhcs6WJcQ+6aQhL5ZN8l8uF8XgMp9Mp98Ahk4HHTDGBtd6RSAQul0vsFbqK+H2azSYajYbOKfkZFAd6vR5arRYCgQDOz89xc3MDj8ejRiUuT1yCisUiZrOZ3CmMXjCywmXHZDJhMpk8cBPQFcTrgq+pWCxKRKP40el0dC7X67U4Qvv9Xg1T5XIZ2Wz2wRLJFiSeh1KpJHYMnT+xWAzBYBDlcllRKEYwWFk8Go2QyWSwWCzkXGK7F8HE1WpV8F0ul3ztyWRS19N2u4XdbteC+OrVK9jtdiQSCSyXS8GNo9EodrudxL7pdCrIcCKRwO3tLVwul5q1crmclu5D5wib1CgkjEYjRCIReL1e3N7e6toxmUzw+XwCabOq2+v1Sujg542V4ozn0TERiURQLBYRjUblGGGbUqfTkRiZSCQwm81wdXUlpw8/R41GQ41NjPA4HA4xaygAUvw8OTlBtVqVi4tcmmQyiUwmIzGUIs719bXcFIFAQE6c+XwucaFcLgO4hxpTiHC5XGi1WjrPdBoyJkkxk64vnjM6hZLJpOJfdHxw+acQkEgkVA+/2WwkshBkfnNzo/cLgNxDFMa+/OUvw2QywWaz4fXr15jNZjg6OpLDcLvdyrXG87Ver3F5eYkPPvgAi8VCYHW2TTHatVwuMZlMsNvtEI/HYbfbdT/kfavX6yGXy8kRSa4NGVjpdFpNf+Tv8Nwyftbv93F+fo7Xr18rhsvPscfjQSKR0D2A13Kv11N1PO+HvGczekv48WAwUDSScS3eoxmz47EwyklAdyAQQKPR0PW4Xq/R6XSQTqdRLpeRTCYlChljjDHGGGOMMT/G7LbYLRbYr1fv+0j+1M2224P/9+4Q/E4H25cX7/twfuazXy5x+j/75J2dOS/fZvH0f9LGdjD82R7Y52DeWchZr9eYTqeYz+diNPCpOx0ITqdTMEoCQ/nU3mazSaxh8xHdPX6/Xz9oM25FseTs7Azb7Vbw28ViobYXLigejwenp6eo1+uKOgBQzIjuAT795lNYLtEE/Lrdbh33arVCIBCQgygajaoCe7fb4eLiAh6PBycnJ+h0OvD5fGJG0KVAJ5HNZsPp6akiT+SsVCoVxUAYtwoGgxKV6JSwWq1icdAVxWgXRSrWWa/Xa4RCIVSrVZhMJjx69AjFYvHBU+VSqSR3jtVqVSxruVwKQssFjdGK5XKJ4+NjXF5eYj6fI5fLwWQyySHEGnM2HLHSGMCDBWo8HiORSMjp0ev15DRwOBx68h6NRnF3d4dcLqfFdjqdwmQywev1qiGIT9rpDKLrJxAIYDabYTqdivdhNptVYc4Kaca26EqiCOnz+RTjslgs2Gw2EnUGg4HadoLBIAaDAYLBoODbVqtV526/3z+AvA6Hw+8TBhgDY5SObWS9Xk+L8263Q6/XQyKRwGq1gslk0vVCUZQQa0ZvJpOJwON0ddzd3WG1WkkkGI1GaDQaSCaTAujabDYBe3e7HcrlsgC1+Xwe8/lcYlmr1RLsNpvNYrVaCcA7n8+RSqUwGo20DLMpiS41AnPJfiJoltfmcrlELBbDYDBQ9Aq4jz76/X70ej19piuVikSNo6MjRQd5L5jP51gsFnJ8sHVvv9/j6OhItdiLxQLxeFxR0rdv3+LDDz+UY2u1WqkFi9dMJpMRh4nuGIfDIQh3u91GMplUSxXdh4wqAhAzyO/3A4AcMBQRY7EYbm9vkUgkYLfbFY8iL8ZmsyGZTGK/36PRaACAKr8J1eb1SFfNbrdDo9EQQNpqtarpjQ6nYDAIv9+PRqOBxWLxAGDPxjc6ylKpFHw+HxqNhlyVFOZSqZTYUqFQCKPRSKIhADk7Kd66XC5Fv7rdLhKJhPhF/LeGgGaKT2zO4n2XjkWKhmRdAVCMlMIjuTn1eh2hUAh2ux3T6VQOTmOMMcYYY4wxxpifxmzqDWwvrt73YfzcZjed4vw/fIXdf8uPt/U4BgvXg/8+39hQ7IXx8iaDZ/+jEjbF0ns60s/WvLOQ02q1BDq22+1q42GLFSuSu92uFlr+sM3qbEacyJ6gi2I2m6nph0wMAjMp/BCWyVYfNiAlEgm5Sex2u75eNBrFdruFyWR60KDEFiva+VlDnEgkYDKZBA/l0s/jZtyGcSpWexOWSaeD3W5HsViEzWZ7UA18cXGh10iOC+M7bNcCIHcHW5K4ELHe1+/3482bNwgEAvD7/XC73ZjNZlqoASh2wdebSCQwGo3gdDoxnU51Pnm+COblE3kCbN1uN2w2myCi0+kUmUwG6/Ua4/H4gaBBFxWdUoxkHB0daXnn90un01gul4rLkX/R7XYxGAzkoAHuG7NMJpPYIeRwbLdbMWX2+73eIzqd6FRKJpMSCfkk/rAOng6vu7s7xONxPfk3mUwP4hisQee5qNVqcq+Q8cSldzAY6Dyz7p1V1jz35EsxCkf3lc/nw3a7lcBD1xfhz4wrmc1m3NzcYLfbwePxIBAIIJlM4tWrV4q2cEmdTCY4OTlBvV6X48RqtaLVamG322n5p8vL7/fDbrejWq0inU6LgdLtdhVz4XtOgdbj8QhWPhgM5JpjJIkCHpu1KHhR8HG5XBK6VqsVHj16BLPZjEqlImg3HTpk5lxdXeH8/BydTkeiMIUtxvUIKh4Oh+j1ehI7Dt02g8FAwl4+n4fT6USn09HnIJvN4ubmRm4Z1tyHQiE1xvX7/QecFoqvvEdSTKZwvd1u9Z7u93uJEbxvDQYDzGYzOQfZENhsNhEIBHSfZYRpsVgoInVycoKrqyvxu8gKWq1Wcv1RrPH7/XqdFIuTyaSckGyzIxOJ7CjeZ8mQImB4uVwqskghm3yqq6srHB0dwW63S0xutVrwer2Cey8WC3z1q19Fo9GQ0BuNRgFA7ClylgiuLhQKKJfL+reI9wm21/HfE8bNXC4XGo2G4qrNZlPX7nK5FAi91WrJqWOMMcYYY4wxxnw2x3qUx/4bH+qX+YMn7/uQvpCzHQyxfXmB49/4GNb/VQQ3//hYv8Z/K4PMX3mJR/+1b2PTaL7vQ/3MzDsLOfl8Xgut0+nE6ekput0uer0e5vO52p8oKhzWy5L3wprbZDKJUqmkOnO32y33AyNVw+EQ2WxWjBxa5huNBjwej566zmYzhEIhLa9sySGXJJ/PCzLb7XbVgMSoCn+wp7AAAPP5XO1GXq9XMSuCZhlr4AJPpwmrbelQoBjCpZF8EUaF6AYIBoPikVxeXsLr9eopscVi0RN8AGg0GvjSl74kvgOdBQAUc3I4HIqFMU7CauujoyMsl0vFxebzuaCpfr8fFosFPp9PjhIue2Rt8Mn9+fk5drud4moUv1jDHI/HFX1grIzL683NDQKBgDgvjAExvsWmmng8roWyWCwiFotp8VwsFkin03I1ERzMY2B8hYBoQn0pmrjdbi38AFR5zpgGxRrGfsgiOazO9nq9qn8/bAfabDbIZDJy0DBaQ1fAdDpVZIVRIPKdyPhhXPEQwssYHJueTCYT6vU6MpkMlsul4LvhcBi9Xk+Q6kgkojhdIBBQexZZOPP5XBBcukwoxBBivlqtJOBSZF2v11iv12oU6vf7sFqtitAwakR4NaHCrBpn+1e/30c4HMb19TVsNpsqr81mMx49eqRrF4AcUcA9r4mRx/l8jlAopLYqOlQYTazVaojH4/ocUlyjqMGqe4p+dJnEYjEBhfk6B4MBjo+P5b4DoM89G/O8Xq+uL8J02Yi02Wyw2WxwdnaGZrOJ0WikenI67QKBgL4nHSVsiSN4mp97j8eD3W6HbrcLm82me0S/31c9OwAJz4FAQCIcHXx0PaZSKQlxdMHxPsXP3mq1QjweF9+KAjzdfe12W2DoyWQip954PEa324XdbkcsFtO/B06nE4PBAIVCQTE5xk8p0E6nU92P6BqiK6nX6yGVSkmwN5lM6Pf7EtR4b47H4wJdW61WsaZY705RnA40unkMIccYY4wxxhhjPptjScRR/ZezqPx5zx/++gthmD98+r4P7Qs9jv/7t5D7n/6OfoX+T7/7vg/pMznvLOTY7XbE43E1zXS7XbUf0Wlhs9n0A/x8Pld0odPpiAWRzWblGvB4PKhWq1iv1wgEAshkMthsNnIysLaWroZWq4WzszPs93vUajWcn5/rKTIdQIFAQBBci8Wiamy26my3W0UdAoEAgsGgljnW7Pp8Ptzc3CAcDqNWq6mO1+/3izlBHsV2u8VoNMJ4PEY0GlWNMJk3XHJYJ14oFBQLY9ymXq/Dbrer1t1kMqHVaqFWqyEUCiGdTuPi4gKnp6fw+/2o1+uK+3DBZQyCSwc5FeQWAdBilUql1AjG6A4XdS6KrFGm24qLJV1SXNb4BJxsIAoZFEoIUY1Go6jVahIguNDy7y8WC9TrdTXoUETLZDK4u7uTA4wLfqFQEEyWix0X89VqBY/HoyjaV77yFTSbTVxdXQlgulwudR7o+InH45hMJlpgrVarHBSxWAz9fl8ix9HRkdqsgHsBgI4Ktg3FYjHxgJxOJ9rtNgqFAjweD1qtlgDFdKjx2mRTGF0D6XQakUgE19fXaLfbyOfzqNVqyOVySCaTagsj24XNWz6fT8DaarWqa51uFbomttut2DO/+qu/ilevXgGA3jOPx4NMJqMoD9kshAjXajX0+33BuCmEsJWMfB3GiXgMbB5jDIzCTK/XE+yXAkY2m5XwuNlsJBaRZRIIBBSjozOqXq9jPB4LQHx4HxkOh8jn8wJX93o9xbAYbZpOp4qsHR8fK1JFAWEwGCAWiwGABMF0Oo3BYID1eg273S5OmM1mQ7lcloNts9lgNBqJycT7ZSwW0/2o1+vJ5cMoGxukAoGAnDbb7RbBYBBerxfT6VSCN6NbyWRS96p0Oq0q9sOIGJlI7XYbXq8XPp9P1y6h6XxP6GoDICGPMOVOpyMRmgyudrutY+R9GYBE3E6ng3A4LDcgWWX83JjNZrVTEXI8mUzkSqK7k1G20WgkB854PBYEng8DgsEgGo2G4rLBYFBCdTweFxDb6XRKoDPGGGOMMcYYYz57Y3I5sQo8/L2tAxif+WGy2d/PQRljzE9p3lnImU6nqoplhIHwY/JZKpWKgKiEffIpMJ/UEz5rMplUd8ulYTabafmnqEAIaiwWU6SG0ODxeCxrP5/ccrl2u91Ip9NqHOp2u1gsFuJA0H0zHo/FPqEjiFEVwjUpXtntdjF3uLA2Gg08fvxYUFY6IAioJR+EoFM26dBVwPgBHSVc8kOhkBafzWaDR48eodVq6fu3Wi0xeYLBoBwufE92u52WnsFggHq9jnw+r/NHRwIjRxSoBoOBKuN5/jebjeq2I5GIlj4u5KwFByB3A4U1ABgOhzpff7T1h3EGLl9/NHrU7XYB3IsK4XBY1xIjX+FwWMIfnRn8PnRazWYzFAoF5PN5OYQcDocArIx/kJGxWCzgcrnULMTrhdfOdDoV0JnCk8vlkrDEBbVarSKfz4s34vF40G634XA44HQ6JUrsdjvVvVNMIG/E7/ejVCopbrbZbCTu8LoC7l1k5BkRZDwejyVEMSLCpdjlcqFWq+l4c7kcBoMBarUa0uk02u02ms0mHj16pOuN7xuB1m63W44jOosI5qZLjkIoG7nW67WudV4Xh01gZCBZLBZ0u13VeRPma7VaEY/HUSwW4XQ6EY/H9Z7bbDa5s9jIRofNycmJrieLxYJoNCrwNF1dm80G0WgUt7e3AnMz/sb3nBE51oAPh0OMx2PFjg6B3X6/X2ylZrOpmN94PNY9hhXaFCYojjGaCfyhAMtKejpY6HAi9yedTsthslqtkMvlUC6XBSZ3OBxyvNAtRcGULja+Z3w/fD6fRF3gvrVrNpshmUwqxsjPWrvdlmuL0ORutwuXy6UKcn42+JpyuRwAqKFvu93qmuB7xGvAZrOh3W5L+GPEkJEp8ncoFDEmS24To2qHTXsej0ciIp1XdCk6HA5FA40xxhhjjDHGmM/P9J5ZYImG3/dhGGPMn2jeWchxuVwwmUxot9tafrkQVqtVmM1m5HI5rNdr+Hw+wVn9fr/s+eSfBAIBPVFnRGU6nYqjwqWKizqXMqvVinQ6jXq9jk6ng2QyKecHY10EDWezWTWSsG2IP9hziWb8q91ua8FmpTqXOAoZ8XgcrVYL6XRaws5oNMLx8bHcPYwPmM1mJBIJXF5eaiFhFW69Xkc0GkUqldJimEwm5X4wm82qZQcg+CcXSDb2HEbD6Agi8yUcDsPhcGA6naJer8vZ8vHHH8Ptdit6ttlskM/nFWXikp9IJASyvri4EGyXgFPWtAP3ld+LxQKl0j2Uyu12q8WH4Fk6LRihCwaDetL/9u1bVRLzuPhnKOB5vV5cX18/WNjo8qD7i800hNSS4UEnDyNxg8FAsNuTkxPsdjtBsBmBIa8llUphu92i2WyqUYisEAoqhPCyypl8G4pKrD82mUxysnDRJp+HwuR0OsVoNEIsFnsgQLpcLjV00RHl8/nkLiCYmE1a8/kc+XxebJWLiwudm+12q2p3AFriZ7MZnE6nREQ6KuguYVsQa9MJ4ubrjcViqp1frVYSy2w22wOGERvMotGoHGkUf/ja4vG4YmYAJD5SBKDDZjqd6vvw/kE2EB0djNe9ffsWHo9HsTqKM/F4XABfOlx4bBQfvF4v2u22HFfNZhOr1QpHR0eoVqtibvH4eV54f+O9kqBtHl+pVBKkN5/PqyXtMK5KwDddWwRUUwR3u91ymjB6RYGVUOB+v6/4Jh2ChMoHg0F9Lb4/FJQZuWP06Pb2FqlUClarFY1GQ/dw3ivIyWL8KRaLqaFqMplIhHE6nYjFYnC73RJM6bJirHY6nQIAksmk4liHrYUU6ofDIZrNJvb7PZLJpMR0t9utdjgKWYFAQJFSfh2CtGezGbLZLDqdDpbLpc4L3wdjjDHGGGOMMebzM5YlsN9s3/dhGGPMn2jeWcgZDocYDAZIJBLYbDZ68gtANa+Mt3DhZuwom83i7du3gqoyVhQMBjGZTNDv97HZbFRJHAwGFdNqt9sCXgYCARSLRSyXS/3QzShCt9tFoVCQG+OTTz5BLBYTJNRkMkkEAu4XyaurK32f2WyGTqejiEUgENDSVCgUUK/XBb8dj8dotVr44IMP5PShu4CuDrIj6G7w+/0PALDdblf10IyA8O/HYjFMp1PVe9O90+12cXx8LMGBT8sZkwqHwxiPxxgOh4pvkLdjNpvVdkUeBN0HjH5Uq1XFi7gYHx0dwWw2IxKJ4O7uTtXWgUBAIN1EIiFng9VqhcViUTMYBRUCXAFIULFarSgUChI5GFkhzJVtPbvdTqIKXU5kJ/F8U/CiE6JcLktQKRaLEvROT0/lWrq8vEQymRSYuFAoYDKZqCmIMRXycaxWqxZlRgnZjMRo2mw2k5vh0KHBhZsOArYGpdNpNBoNtFotZLNZVT0zjudyucT7IGiZ8S+KRQDkPgkEAkgkEri6upJYFg6HEQgEtNBSUKJzgsymzWaDdDqt2BWZN3SQ8PNKALnZbEapVBJzh+8hv9dhlTzjhrlcDjabTeIuBYpDp1yv14PL5UIkEkGr1ZIAwxhRu92WyJRIJPT5Oz09xXA4RDwex8XFBQKBANxut5xl0+kUw+EQZrNZrpd6vY5YLCbHC2HPfr9fsTuKy4yLsTns8vIS2WwWFosFkUhEnCNek3RznJ6eAgAuLy9Vvz6ZTCTehkIhMYx2ux1isRhWqxU6nY7em1AoJCYPm83ogozH4/rfbIGj+xGABKHRaIT9fo/BYIAPPvhAsU5+/sgQI2+G0N/RaIRer4dnz56hXC6rjY1iM98junYIWWbtPdvOotEo2u22/v1gXfjp6SkGgwG63S4ePXqE4XCIxWIh2DFdSHQArddr1Ot1BAIB/RqPx3JcxuNxidNs+2KjFblOvMf2ej0JaLwPhMNhmM1mdLtdZDIZuFwPmxWMMcYYY4wxxpjPxuzHE7ha+we/Z50Bud9uYWu4bo35jM87Czl0xNCeTxbMdrsVx4JuHAIiCZxtNptaUofD+054NonwqTsjQOFwWA6BQqEgWz3FCHIuSqUSwuEwqtUqIpGI7PRciAgmJQCYtcGMK3EB3O12AO7FKNrv+/0+xuOxmBKtVkucneVyiXQ6jWg0KpYIAcoul0v2f7JSXr9+Da/XK+An62wZyaFwwKf+XKLYArbZbJDNZgVcJnD6MFZA9sloNFKs7ZALQngzK91ZS03Wz3Q6RSAQQC6XU0SKT9/tdjtubm6w3W5Vcz4ej+XGcjgcKJfL8Pv9SKfTKJfL2Gw2ODo6wuXlJQqFghYsun4o9LH5iMITIxVkuLjdbhwfH2O5XOLu7k5P+wmGpVjExZnXYLFYxIcffiiB7OTkBDc3N/B4PIqFEKhaLBZRKBQAALVaTZEztkat1+sHQsyheymZTMpRQfgsr7fpdIovfelLWK1WKBaLavQCAIvFonYnXitk1Ww2G8FoK5UKlsulYmwUSciD6Xa7cssQSMuIEdk7i8VCDgm73Y7RaCQRgZ9DOjTm87n+W6/Xw5MnT9BqtTAYDORQO+SfuN1udDodbDYbOTIo7obDYYlVPNd0W9ABwnjkbrcTaPkwfsnr4xBwzM8rr226Xnw+nxg+dNMweuRyuRTH8nq9ijKSVeV2u+H3++UmosuJ7rBAICCmEY+B90Obzaaq8bu7O4mKvFf6/X7c3Nzg5OREguZ2u9W9L5FIiHG0Xq+RyWTUVMeWLcYGeW8hBJs8G34Oya2q1+uw2Wy6l1AQZSRstVqh3W7DYrHIRcnPE+NNFMAYTUwkEnKlsSr+MCpJZxQF8OFwiH6/j0KhAIfDgZubG0UKj4+PUalUsFgsMBqNYLFYUCgUBN5mPJWQe5vNJmGH98VcLqdzQWfXixcvEAqFxFWLxWKwWq0oFouYz+fIZrMwm80SHClKOxwOuabi8biEcd6v6Q4yxhhjjDHGGGM+W7Pt9pD4z67g6AOmzf2v7N/7YlV/G/P5nXcWcthyQnYDlyY+uWc9NSM04/FYy+bZ2RlMJpPcMYzwdDodwX0ZG6KDw+fz4erqSq0qrDKnm4CxGlru+YR1tVohn8+j2+2iVqtht9vh6OhIS2Mul0OtVlNcqlarYb1eI5vN6sk7xSo20BQKBUVDdrvdg3Ydi8WC0WgkmCsdJHRwnJ+fK+LEc8ZWpOVyiWq1img0qljUdDpFMpnEZDIRO6jf78NiseDo6AiNRgMmkwnxeByvX79GKBQS4ycYDKqOmM4AABLeyCth61Kn00EkEpGDgkDWp0+fIhKJyEFQr9cRDocFbiUMlMIczz2XJUah4vE46vW6ljDGKjKZjJbQ0WikRYm16RQyer2erpFIJAK73S7ho9PpSGwLh8PodDrI5/MYDofweDwwmUzIZDLY7XaCLBPqenNzA7/fr2u50WjIycNK4nq9LrGMkb1QKKRrmzGe+XwOh8OBq6srRbKcTqcih2Ts8P0gyDkajaq2mtclOSqE/PIaIRSaMa1+v4/1eo1CoSAHFnlIFOUAYLlc6r3lok5BlBybcDgsDlMymUStVsN0OkUkEkGpVBLgm4suRQqfz4dWqyUHRTAYhN1uV1U1I44UFex2u4Q08ogCgQBmsxmsVqvgu4vFQsDaRCIB4N4RQpGLoler1UIkEpHLie9TLpfD7e0tzs7OFOUJBAJoNBpyKPG9BgCHwyHY8Gg0QiaTQb/fl6C53W5RLBbxta99TdXw3W4XZrNZPCsAePnyJZ49eyYXFs8TXxOZYbwWeI/rdrsSdQlzpijncrmQyWRQrVYFLednl7B4CiYUuelS3O12OkdWqxV+v18uuHa7jUgkglAoJKFuPp8jlUopSjafz+XQ4TW/3+8RCATUlgXgQdsahUTyzchH4++x1v729lYiqMlkwnq9xnA41D2UMU6+561WC/l8HpFIBG/evFHdOGNndrsdXq8X4XBYMHIKXTxmuhUZ4ePnIxKJSKSfzWaYTCaIx+NyfZG7Y4wxxhhjjDHGfDZn224j8b/7JnL/0f2v7eXN+z4kY4z5qcyPJeSMx2PU63XFOlinS5EEgICsfAJPmK3dbhf7IJPJYDaboVKpIBqNKqYSCAQwGAzg9XrV1kJRhE/7O52O6nK5NNLZ0ul0cHZ2JreJz+eD3+9HtVp90EjFp97T6RTPnz/Hfr9HtVoVm4d8BMYvGHexWCwYDAZqaVoul+JEsGGLYoXZbFachLDgYDD4gJ/BZazX66nFiU+BnU4n6vW6hIButytxhy4UupnY8EVGRbPZVMxkNpupPYkxIYpt5O4wpsTYztXVFY6Pj1XV6/F4FCehIMHlbrPZiBvj8XgkNDF6QZ4LgcbT6RT9fh+pVAqtVgubzQa/+Iu/qL8HQELMcrlEp9PBYrGQw4ZRM0bF3G43isUi7Ha7/u7d3R0AaGknOJpVyBQ1Dlu8GA3y+XxyLCWTSb3nh3BiQokpFNTrdex2O4RCIcRiMez3e7m+yBJJJpMYDoe4vb1VTMbv96u2eTgcyn3E18P3Zb/fYzqdysWTyWQEMw6FQlqYHQ6Hqr3ZOMbFnvBgj8cjgYog3EePHmG1WmG326mSmS6exWKhz/QhNJZsn3a7jVAohH6/L96OzWbTtZdKpTCZTB5EsshhYtvcfD5HNBrFfD6X0EqwOJ0lo9FIMSM6bvjeut1uDAYDCZ+stg4Gg1gsFgJkE8RNQWM2m0l88Hq9inLxtVF0Ojo6Qq1WQ7fb1X1vtVpJQCEXh04ul8slVw1dSXTUsYY9Go3KWXLo/iFImuJEs9lEOBxWnI98pu12i/F4jGw2q7YnAo63262Eot1uJycWY4GBQAD9fh+NRkNOMboK2+02ptMpTk5O0Gq19D4QkE4xi21X6/UaZ2dnctVRFOScnJzI/cL2Krosk8nkg89pr9fDcDjE+fk53r59C6fTiWg0qmYripAAJHjyuqeLk2IQ3xu+fn5P8pfG4zHS6bRE3t1uh0AgoH9XPB6PhMfD12OMMcYYY4wxxnz2Zr/Z6Jcxxnxe5p2FnOVyqVak9Xqt5YZiBiuub25uxIXJZDJ6As1KWZPJhHK5jFwuJ2cHn+r2+31EIhFxP7bbLeLxuCq3+bR1uVyKy0Lg6Xw+l8DhcDgQDAYRDAbVwEJBg40pNpsNJycnaDabgoXy77La22w2y+3BlpnlcqmFmlwGMjgOn+rSEVKr1QBAtb/kx7RaLbhcLni9XtWlkx1BgPF+vxfglBBhxgJevnyJDz74QE6ocDiM29tbxSeKxSJMJhOSySTa7TbG4zGePXuGer2O7XaLSCQCl8uFbreLwWCAbDaLq6srsVBmsxkcDocWYDoruHjzSTdbyxhDSqfTKJVKisAxakHhpV6vK36z2+3kyuHSDkA8IsY7eK7JSer1enKVUGDj62QjEc8VlzougYPBADabDavVCmdnZ2oU4nvKuCAAiUMEdk8mE/FceC7IzwgGgyiVSrp2gT8UpACg0WggEAjA6XRKLLq5ucHTp0+x3+9VFU9RbLVaqZHM6XSKz3J0dKSlfb1e6/Pl8/kEMQ4GgxIq6HC4vr6GxWJBOp1+EEcix+rQsXHYFrdYLPS5t1qtciKR52IymRR/YssVIzMU8B49eoT1ei0GyWQyQblcxna7hc1mE3CW1xn5QGwYGo/Huh4ajQZisRhCoRAajQZGo5Fcfd1uVzFEXktmsxnD4VBiht1uRyqVktjc7XYVV2Schp/TRCKBVquFu7s78bH4vRivYuSNsGzez6xWK25vb1ULTnE6GAzi6dOnKJfLqFQq+uw2Gg2kUimJQnQqMc7J70mWFu9hbKvq9/uqm7+4uJDr7bDlr9frqTKd7rdgMChXIQHyZO+w6YxtVrvdTuypyWSCWCwmJ1MikVAUjp/D1WqFVqslxyPbCik80j3Hr82YU6lUQjAYFByfjqL9fq97Ktv2CHlmBJIMoGw2i3K5jHg8rlp6RlVzuZyESYo8nU4H8Xgc0WhUrB9C2w3YsTHGGGOMMcYYY4wxf9rmnYWcQ3jrfn8PjWKLCNklhPPyKfRwONRCxXpkr9cLu92O6+trRKNRuR3W67W4KIxZFYtFjEYjpFIpwXwJNgagJ89kRJBjwif8ZDqQTdLv91Gr1bQw0THCJYBMCy6Cy+USy+VSzSlc+Pl0mhEYLhCr1UoxMDpPPB4PAOjv8un70dER7u7uBFe2Wq06D3zincvlUK1WMZ1OYbVadS6q1SpisRg2mw2GwyFSqZQ4FXyth4smWR4UKgqFAobDob53JBLB27dvYbPZkM1mMZ1OMRgMBJwNBoO4vb19wMeo1Wri8PT7fWSzWdTrdQSDQRwdHeHm5gaj0egBLJbvb6PRwHw+x+PHj9Hv91GpVPALv/AL4qdwsQXuAdrn5+d4/fo15vO52pdms5kicwRLswKZ7ynjb6wUXi6XcgIRAEsHFgUEXs90AcRiMR2T3+9XVTXjTIx68TqzWCxibwyHQ+x2uwdgYlZWs0q61+vpGuO1PZ1OEQqFtDCTM8NlnO8Do2CJRELiGR1JLpdLAhSjNh6PR4KOxWJRtInsHTp26AJiextdIPzMk6PidDolYEUiETU3rVYrPHnyRKynXq+naB1Fn81mo89hpVLRuSMEnPEuihUUlQiHns/nODs7w2az0efC4/Hg8vJSMTpGthg1KxQKcraxnYnnlfcwMmf4PekU7HQ64vPw+G02G5LJJC4uLhSbs9vtYrmw2ppuRJ5buuco0haLRQD3nC7edygO8Z5KAY0CBqHVdKmFQiFcXl6iWCzi/PxcAnMwGESlUtHX3+128Pv9cus0Gg19BoB7CDyjhy6XS6Brin9kcT1//hwXFxcSlngPJbB+uVwiGo3qXLNWfLfboV6v4/T0FM1mE8A9LNnr9UrAJIuJzXVut1vOGzrGeL7ppjpktPl8PhSLRRwfHz/gsjEmRffbfr/X5zgSiUgY5LVMx5vVav2x/lE1xhhjjDHGGGOMMcaYn/W8s5BDMWGz2WCz2ahJilBV8mP8fr8YA2z9IdxzsVgI1MuYw6HowcWu3+9rEVgul1qgAGgZYuWwxWJRLIHLZyKRwHq9FriUjVKHrBYCP7mMskmn1WrJcRGPx+WMoIAznU7h8XiwXC71mrlI8ek5ALlUqtUqcrkcPB4PLBYL7Ha72DsUxdjwRRAv21z4uvgEnosl3QoUz1arFWazGTKZjI6LsZPNZqNoGs9fo9EQ44PHwP++3W5Rq9WQyWR0HqrVquqZe70eMpmMHBYA9B673W6USiWJUAAUv6GDhpEhsj7IuKGDo1wu49GjR/B4PIrFjEYjxSDYhMUYDSuMR6MRHA4HYrGY2pIokrjdbvFo6Lygo8XhcMiNw3gFIz90n/Hr05HR7XZVZUyhwmw2o91uI5lMKsJWLBaRSCQQj8dxfX0tjozJZNJnibX3fL+WyyX8fr8WWjZxMY5jNpvlaqJgwOvzsMGIzKTFYoFKpYLj42N9FhwOB0ajEdrtNvL5PCaTCS4uLnB6eionxHa7RTabVVUzRQRGWxgPowuGDT+EH1Nooihjt9slNmYyGbFPBoOB4kij0UiRHrpxGOeyWq26fihEsUqeC7rVatXyz9p2cnYOgeeRSATX19d6v+l+IcCZvBpeIxTDxuOxnHdkhjFC53a7sdvtMBqNJIIy2jMYDNSwZbfbUavV8PjxY7kL+fmfTCao1+sSsFit7XK54Ha7YbPZ1A5Ft1UsFlO0LxAIwO/3o9VqPYgW8T7G+8F4PFaciTFJCi68lxAazKY4Ms0eP36MYDCI73znOwiHwwiFQoKr1+t1LJdLnJ2dybkXi8UwGo3UGMVIJwVmNrwtl0vxvQ5dNnS+AVA1+9nZGfr9vgTLyWQi+DUAdDod8a8qlQpOTk7QaDTg8/nkimu1WoraUszjvbbZbMLtdgsizwcHxhhjjDHGGGPMZ3tMVitMDgdgtrzvQ/n8jckEk8OhX/heKsGYn938WEIOnzgvFgv90E8mhsViEX/jxYsXMJlMggGTn8M/Q9cMRQyTyYRCoYBWq6UllQ1KhCbP53M91U2lUqjX6/D5fLBYLA9cLJPJRBybxWIhl8JhrS6FocVigVAopDpoHi/hyaVSSdGeZDKJxWIhtwhdClySqtWqHDVcMoPBII6Pj1Eul7WwMHaxWCwkyHQ6HT1d5rJJ4SGbzaJSqagpq9frYT6fw+v16rwwzsMWLjqIDgUD4P5pO50hdEexbYZV4vv9Xg4oAoUZbeByxCfqjAvtdjuUSiU8ffoUq9UKd3d3CAQCmM/nKJfLWvbIE0mn02qrstlsqpve7/f4pV/6JVxeXmoZm8/nitCRiUMmE50JdGvwaT0XQnKLKPoxNkJXx+3trWITjx8/RrVaRavVQjqdFscIgISeXq+HdDqNbDarxbTZbCIejysS02w2tfx7vV44HA40Gg0xcvb7PaLRKMbjsaJUhAnXajWcnp5iPB6rEpvA8Gg0imQyqfN52FxF8YFV1hRsPvroIzx79kyOBUKrGdXj4utwOMRkubm5UVyKy/50OpXgwMW83+/D7XYrekgXBSM/bNL69NNPkclkFFekCJpIJCQKW61WXFxcyNXFr8s2NIoMsVgMjUYDmUwG4/EYk8lEoi2dOeSq8PojSyYYDCraNJ/PEQwGJTDSMbVer1VxTuC2xWJBPp9Xi9tkMkG1WpXzjuKNzWZTfI/3jMMYVqFQQLValfuFzqVD8YnXAllZFDJ4/IxVMm7UaDTkciEkejKZIJlMKmba7/clJvV6PXz44Yfo9XqKxVL8Zb16q9XCBx98gFqtpvs8cC/IHh0doV6vq3EQuBeGKfTyGhgMBnLZ8H7v8/nUxBUMBgXlpuDHa6PT6UiUZuMhI3m8f/BeGgqFFEMj28Zms0mYmc/n+NrXvoarqyvFEwkE52tiS6HD4cBgMMBoNMIHH3ygWBaFXGOMMcYYY4wx5rM9lkgYnX/jF1H661/D9K98/V5sMOanNvtvfIjSX/+afi3/wtcNwexnPD8WI4c/CLO1yuFwSJgYj8doNpsC47pcLrEdGMGiQEEoaLfbRb/fl2jDH/IJIZ1Op8jn8xgMBmqyYd05XRxchljvTZv/arXSEjEej+VaSKfT6PV68Hg8amchC2I2mwG4j5FVKhUxPsjZ4FN4LspsqQL+sL1ltVohlUrBarWi0+lgPp/rKXCj0VAzTSKRQLFYRDqdlvskEokIzMoFCoAEHqfTiX6/j0QioeWFgOLRaIRQKCTHEQC0223Y7XY1JLXbbQD4vqgG2SHpdFpxj8vLS8U14vE4YrGYILSsCaYwRlfTixcvkM1mEQ6H8fbtW9jtdvFaGJGg+2Q4HIo7Atw/bTeZTBiNRhiNRmrgohuBT8sZV+I5u7u7w+PHjwVRJXdnMBgoAsglkGDmZrOpRd3j8WA6ncJsNsPhcKgBaTweS+gAIOcQYz2Mi/HrsPp5OBwiHA5L4Gy325jNZigUCoKqrtdrsUe22y0KhYLcVsvlEqVSCZPJRPEjt9uN5XIp3g0ru7n42+12sVrIozKbzXj27JliKK1WS1FAihfkupjNZgl0Xq9XjUt0FvHvrFYrRQTpWGK7ElvRstms3BgEetdqNbFwKLby3vDkyRNcXFwgGAzqfTkUJ/h3rq6uYLPZ1LJF4YD8G8YiCUMvl8uKH1G4JOx3sVjo888mM97T6LqjAMQGMwDiwrBRjbXjjBfZbDbFCelkoaPQ6/Xq+7MVi6ID2UX1el0sMbbybTYbJBIJtNttsYSm0ymCwSD2+z06nY7A0fy88R5J16HD4RB0+Fvf+hbC4bCa1ejsO2Qj0XlGcYji92Aw0J+l8NJsNnF8fIxqtYpUKoVoNCqgda1Wk+jsdrvRarV0fPy8k12UTqdxe3urmCodOWzS6nQ6GI1GiEaj+lxTMCVUmpyzeDyOzWajzyrbsnq9nsR/Mp5yuRzMZrPiZOl0WvdaQsfJzDHGGGOMMcYYYz6bY3Y60fpLjzFLmQAz0HtqgSUcet+H9bma+q+475WF7/1qfcUGs8v5vg/rcz3vLOTs93uEQiGEQiGJBr1eT08sGR3iMs2n2W/evFGMwO/34/r6WmDa5XIpOOl8PpfFHrjn2TBmkUgkVJ3NRYKsEMZLVqsVGo0Gjo+PxW0hB4cxlFarJR4NW3ooGrCxhIshAPFc+ASZkRe6KQ5FKcYzIpEIvvvd74pLwrrik5MTuYS4SIRCIQyHQ6xWK7jdbtzd3cFkMiGdTgukbLFYcHZ2BqfTicVigWq1KjBwKBRSfe/R0RFGoxEqlQrW6zVyuZyegFNI4XFGIhF0Op0HT/gpLnBx4Z9Jp9NYLpfodrt6vTabTSIXG4MAIBaL4erqSpwRnhvGnGKxmIQnXguHDhLyYbxer2qvGa9KJBJq8/J4PMjlciiXywK5zmYz8Y8ajQasViuSyaSAzNPpFOfn53KcOBwOMZqazaaEBgo5BCJXKhUMh0Mkk0ldB4TXlkolVbuTvWM2m3VNceEk/4aOjXq9DpPJJC5JqVSSU8Xv9yOTychhxKXc6/Xi448/1vs6Ho8VC6EAQTHR4/HA5XKpKYnvPT87BGezsvr169fI5/NiQ00mE4RCIUwmE4kp4/FY56jX68FischZRqGE8UWKMJvNBplMRiDl0Wgkl1Kz2VSUjjXxm81GIhlr6iuVCsbjMbxerwSEQCAgDhFBxRSpGD0iyJliDD/HZrNZomYikcBkMpFrJZVKqTGJjg4AqFarePr0Kex2OxqNBhwOh+rXd7ud3DgEbfO9ZqMXI1FXV1dwOBxyfLG+vdPpoFQqIRQK4fj4GIvFAhcXF3j69Km4SOv1Wp8hj8eDWq0Gr9eLx48fo91uS5gqlUoSjhghpRgL3LOz2AzVbrcVTbNarej3+3JA8RzytZKzc3iPPnQX5fN53N3dCfRORxkjbnQzMnJ22EDV6XQQCAQQjUYF8qZgPZ1OMZ1OBVOmA2c+n8sNdxi9XSwWcos5nU7E43E1qiUSCbx8+VItYplMRo2EdByyPc/j8UhI5ufYGGOMMcYYY4z5bI7Jbscs/TDq0/0vHr2fgzHGmJ/SvLOQQzhmv98HcA8v5qJNaK7D4cBwOEQoFFJsh3EiigHBYFBLFRtFVqsVYrEYms2mgLgEZNJ5Q0GES+p+v5fbpNfrKTJD9gUdIOPxGMfHx2ogmU6n2O12iEajsFgsmE6n4obM53MUCgW02205EZLJJCaTiZwUdCIA0AJZrVZxfn4usCZdRQAkAPDp7mKxQDKZVFvNfD5Hv9/Hy5cvcXJyAofDgdlspoXzzZs3imrt93s8fvxYzUeMHTgcDsUXWOm7XC7lLqAQ8N3vfhfHx8dYrVYIh8Pi+3B5J1eDHA+e63A4rMpvgk/JCqLgweM4dHuMx2M5hOigIYSZyxWFj8VigUKhoEjTeDxWw47T6US321VkbDQaodvtKgLW7XYRiUS0eFJEoBuDDpNDQDBFrFarpQpofp1Wq4XHjx8rmkc2x3q9FpOEThiykSqViv4s28boGCMIOxKJYLVaIRqNKiZEAYMiG10PjILQ8cba6JubG3i9XoFkJ5OJznUoFFIMxGQyoVqtIhqNwu12iyeVz+fRbDbFi+J5ePv2LUKhkIQGno9DFxXb4U5PT7FYLB4AyvmZo4gRi8UA4AHwmVwjnlc2BQGQEPTkyRP8/u//vmI3dAZRYGDzUjqd1n9n0xxdf3RpkP1EUaPVaun3Dpk3ZrNZ55JA5V6vp/hXJpNBtVoVkHy1Wuk9ouuIMVM66ugkpFuNMSiCe20224P7Fd06hDsXCgX83u/9HlKplK4Dt9uteB7/DmNRbP2Lx+OwWCzodDqKZhIWTl7MoRCUTqflpopEImLtkN/EKFM8Hn8AA6dLiyIp3WkAdF2Ew2GMx2NF3ygYMVJYKBSw2+3Q7XYVc6M7jPeVPwqKZgNVIBB4EMXi3+G9jALb1dWVIPnL5VLwYjKlIpEIttstEomE4p/VahX7/R65XO776tSNMcYYY4wxxpjP3uyWS/juHj6Yify/i+/nYIwx5qc07yzkAPc1zHTcsMaXMShGUxjjINQ1GAwq1rFer7W8s31oNBrh1atXsNvtyOfzagLiE/ZYLKblkEvZdrsVZ6TdbmMymSAej6uGud/vC3TKJ6yEaLLhiDEYPgVmHTFdMHQsdDodCS61Wk3uinA4jA8++ADFYhHdbldxgul0isePH6vKmUJKIBBQXIsLJ5dvh8MhAanX64kbMplMFNeg44nNUFwqGc2gC4Kig8fjUfyk2+3CYrHgww8/RLFY1JJ0eMwWiwXHx8fodDpicPB9ohOg0+nAZDIhHo/rv81mM0W5+ISe9d1svalUKnoPPB6PhIHJZALgnr+UzWYxn8/hdDpRr9eRTCbhdDoVteJiT8aMw+EQFJdgX55vNlUNBgO1Om23W5TLZQD3oN7dbof9fo9YLPagvYaurHq9jnQ6jclkgqOjI5jNZoGHd7sdbm9v8eTJE1QqFS3zdJsQ7EygN2He6/UakUhEggcB4JFIRG6iWq2GWCymv08g63A4VI07AC2cdLMwFkOBheBeQrKdTqfiUWxaozvj6OhIcSabzSbYdjgcVu06BRUKkgQCU9BlnIjv86FIw+poMnr4eYnH42g0Gg/EotvbW5yenqJSqSiOxJhWq9XC6empXv9wOFR70WEsiss6Qb5kpDidToTDYdzc3Ci6ZDab4XQ64Xa7kUqlcHV1Jcg1mUFs0bNYLPD5fOLzUMTi/YLOuM1mg8FggEgkIiGg1Wohm81iMpnos261WiWmkSHUarX0mc3n89jv99hsNg8YLqvVCplMRpXvhMzTbciImtPpVDvUYrFAKpXCq1ev4Pf71RJ2cXGBaDSq80znXa1Ww/n5uZxJbPj69NNP9dmkIB4MBnFxcSGnD+8Hk8kEuVxO7rFGoyERrlar4fLyErFYDNlsFnd3dzg5OdHro/MmEAjouqfATRGJ0VkK+3woQEcf3Zo8frqMeE9NJBLodDr6muQrMTb2+vVrbLdbiZLGGGOMMcYYY8xnc/bLJcJ/9wV8d3u463sc/8c1bDu9931Yn48xW2B2u4EDw5N5BaR+d4nd94wNxvxs5p2FHLJnGPlh+xCfmHL54Q/lm80GPp8P3W73AViXT0X9fj/6/T56vR6ePn2qGEir1cLZ2ZkqocvlshZSwjxZf+zz+eB0OlWLyxYmAm5p8WfciDwSLkDJZFKiE0UCij273Q75fB7APWvG7Xbjq1/9KkqlkhZlVuImEglxL2jJZ30uG5Y2m43aTxh1ofsGgAQkclXIwWAVLpkOZrNZyzUXwEAgoCiZ3++Hx+NBvV5XLObk5EQ8mefPn6PRaGA4HCpCUyqVEA6H4XA4JL7Q7ZDL5TAcDlGpVPDo0SOBqskW4tN88nIYr9vv94jH44K52u12OREuLi6QTCbl1GKMi5wiimVWqxWBQAChUAi1Wk0RtF6vJzGOrgS2Zq1WKwyHQzidTqTTadhsNhSLRbE2GJdjjMzr9QpWS+g1l+RisajrmdcS3REulwuVSgW5XE4MFbJFKAaxKYjnlQ4Gt9uNzWajiBedYoQcE1LdbDblICK7JplMotlsotPpIJvNqgqeQgKdKbPZDE+ePFFMZbfbKabDuAxFELrsyBqhm42xPTZmUdCjAEcBrVaryblFWPT19bXYPQR/e71eXF1dSexgXfVsNlNUi4Bigmj53rJBaLfbIRwOY7PZqC2L0Ze7uzs5xghgpzuIrCyKaYPBQJBdxosYEeM5JAeqVCqpwpq/dwgOpxCVyWRQr9dVoc7zSUGa1e2M0NFpaDKZFNkLBAJijRG8zO81m82wWq2QSCRUO052F9kyi8VCkcZEIoGPP/4Yq9UKz58/l/BCFyU/DwRF0wXYarUEn9/v9/p+FDXIPKM4st/vJVDf3t4qXsW6+VwuJzgz3X65XA6pVEqfk2g0iouLC0UqCYAnR+fq6gqFQgHD4RCBQEDONd4Der0ezs/PBYCnqE2BkrHW9XoNk8kEr9eLt2/fIhaL6XXQ+ZfJZDCbzQRrPmRlGWOMMcYYY4wxn80xOewIvRwh+p0RtgEPzI+O3/chfebH8ugUi7/4Ndz95i9g+z0cjmUJZP6j34f1//UHgBFP/5nOOws5NpvtAQSVbhXGWQBIZCDQlMsU68pzuZxs9wR9ss6ZrgabzYbZbIbr62scHR2pZYdcjc1mI6cJ66SfPn0q1wq/xna7xdXVlZYmNgtRTHG73ahWq2raYjU4l7NQKIRGo6GntGTszGYziQls/iHwNBqNCshL147FYkG328VkMpGgwXNzfX2NVqslvgVrjBlvarVaijMw5kBBgBwIsjjo9DCZTIhEInC73ahUKmpNCgaDenL+6NEjsVKurq5wfn4Om82Gfr8vho7L5YLX68VwOMRwOEQ8Hldzz263F5xjXQABAABJREFUk2OKSxFBsFxMCSi12Ww63+v1GrVaDel0WvEKVhJvt1vFMfb7/QPHwm63kwOA7qxDnsZhlIuV81zEW60WUqmUYi5sKqOzpVgsCtqdyWSw3+/VKESxcDAYIBqNKhY0n89V802HGZ0or169kiPBarVK+Nlut3A4HHA6nSiVSoqx0O3CBdvr9aLXu39CQPdSs9nUOTyMxQ0GA0X5yAnhNWexWHBzcyNBczQa4fT0FL1eT866fr+vunY6PqxWKwAIgM3XVq1WVbkeiURgNpsxn88xGAwQCoXkVIpGo3jx4gXOzs4wGAzUbkRWDwWbUCikzyLZLP1+H0+ePBE8mDG7xWIBq9UqOPbd3Z2EvGQyiZubGzlq2IRlMpnU4gXcx33oCuHnnW10vGbo7ur3+wgEArDb7RiPx2g0Gnj06BGWy6UapebzOer1OgqFgsQT1tKTfUUBjgLhcDiE3+9XvIg8H5fLhWw2i16vp9p3VrZPJhPc3t6KJXXYpmc2mxGJRFS/XqvV0O/3BVunu4awbK/XKy6Yy+VSHHIwGOg+W6vVxJeazWbYbDaIRCISm3a7ncTSyWQCt9stwRaAxORSqYRMJqM2qcViIW4N2WG851gsFmQyGbUNUpgaDAZot9vwer2CYcdiMfh8Ph0nrz2LxSLHjd/v12eIn3sAqr0fDoeKig6HQ6RSKbl0GB+MRCK6JtlyZYwxxhhjjDHGfDbH7Haj9ZcfofQXAve/fi2Au385AvzSl973oX2mp/Vn42j/gvWBG2dnBfa//MH7O6gv0LyzkMNFkYBWOli4ILGdqtPpIJVKqRGK1niyFrhgMxbBp+JsFQKAXC6HeDyOu7s7OYAYnTmMBdlsNtjtdsznc4zHY0VR6AY6rB4vlUpqADKbzXIOtFotNV7d3d0BAPL5vCCb4XBYDiA+jd9ut4qUORwOiUHA/RN7chkYsSDvpFAoaIm/vr7G8fExotGoFs67uzv4fD41WRWLRUUm+GSfgFK2JXG5YlU3nS3hcFiCidVqlXvEbDbj7u5O72M8HsdwOES73X6wZGUyGVxdXcHj8eDJkydq02HrDqvDySKhK4ocHIvlvm6O9eT9fh/pdFqtSx6PB36/X4IgF3wKUwTE0pljsVgQiUT0VL9cLiOVSul6opNnPB5ju90iEolgNptpuWM7l8vlUvNQtVrFZDJRXIeOMb/fD+C+nvj29haJREIOFrfbLdcII1YnJyeKP1ksFglJo9FI7UGs865UKnj69KkaffL5PBqNBrbbLXK5nPglFKsIq04kEmg2m4rGnZ2dPYgJptNpAFDUcLvdCjgdi8XkiGDMjNcNRT5Coq+urpBKpZBKpSQmplIpCQcOhwO9Xg8vXryAz+dDPp+XqMqWoel0ik6nA7/frxYvOoJisZjcOpPJRMIHW79KpZIg1OTLxGIxDIdDlMtlCYOHDC5G6hjpY2SKsU5+TpxOJ3K5nFxldPIkEgnc3d2pTY5xsWazCafTiW984xv49NNP1XLGe9mTJ09QLpclVrPufrlcKqqTTCYRDocB3EfMXr9+jd1uJ2cLzwP/PJ03h3Bv3hspngKQm2W5XGI6nQo4z0ax2Wwmlw9jeIzrUYTL5XJoNBoPIMIUAl0uF4LBIDqdDj755BOxd8LhMNrtNqLRqCKzq9UKs9kMX/va1ySWezwegcXPz8+x2+107HQYsrGQEGleX/F4HL1eTy5FwsRrtRrG47GEHzpoeK3zPs2oFeNWvPaWyyUGg4HYWrx3A9D9zOPxYLPZ4OrqSt/XEHKMMcYYY4wx5rM97d/4ELPkQ9jx3gpU/rwPlsdn7+moPtuz++e+glnc9H2/v7cAk5zrPRzRF2/eWcipVqv3f+F7MNfFYgG/36+nwXQd0F1hMpnkNnE6nQ8qxGOxGKLRKN6+fYvJZCIgMG3zxWJRtv9wOKyWHzo42HBF5wqdJ3wqTGGDSwfdIuVyGYFAQLGH4XAIr9erZYaQU4fDgel0ikAgoBgJY0BcyHO5nNwCfK3hcPhBtKrX68FkMslJRJAoXQwUDcLhMK6vr3F+fi5wMwD9GQCCPhN+S3YNoyZc0p1OJ3q9no5hv9+LxfNHWTpkA7GenFGNyWQiFo/X69VrB6Ca7Gg0ivl8rvcPgNqrCPul8GS325FIJPDmzRvYbDbkcjkA964PQmUZiaCgwZgdYw2E5RKy7PV65eSKx+NyREQiETgcDoxGIzUTeTwetVdtt1s0m02JC7lcDpeXl1qYzWazRCVCXgn+pdsjk8lo0X/y5Al6vR7q9bocOoR682sxMsXzQ1cNeUiE4FIIOD4+RqPRELOJbqNIJCIWFJuU6Fy4vr6G1WrFbrfTUs/PGh0vbIpiXCmdTisOQxdVNptFv98X14hOkc1mo/ao5XKJXC6nli5CvCmc2Gw2ZLNZueQYxaOQwDhZNpsV94X3jmazKXcNr0UKlwRCU6AcDAZ4/fq1xClCjf1+vxwfANBoNOSqWywWGI/HyGQyOjcUNqfTKRaLhVxXz549kzuMbjO6iHw+n2DnfO/oeDn8zFxfX0v0s9lsePbsmVwhrPemGMo4z3q9Rrfbxfn5uT6rpVIJFotFLKlKpQK3241AIIBarSZoOQHvrOMmB4xNTIxu8vNJYDcdaIzajcdj3e+z2azef8YH/6go43A4UK/XJbiS20RBhi195OUkEgmkUil861vfgs1mEyOJoOpIJCL3nclkwtXVFbLZrJxC5EotFgvdl4PBoJxGJpMJq9VKMUKPxyORl+Iloc2HTDSXyyXH3nK5lIBtjDHGGGOMMcZ8NsdyfoJl6PsFB+BezBl+GAXMlp/zUX32Z2cz/1AlYR4xw5pK/nwP6As47yzk8Akr63XZODUcDpHL5VCr1bTw5XI5VCoVnJ2d4c2bNzCbzaqBDYfDD+IpfJIajUax3W5RLBZlx+dT61QqJUgr3T3tdltNVM+ePYPH41FtcDAYFGODrAk6aZrNJhqNhqJUhN2yFYVMGDa/sG3J5XLJQeJwOCQQBYNBiSt8Qp3P51GpVAT3BO6dL1xK2EBEYWq1WonlwHpuglkZ4aLLJRKJSFxi+06r1ZJowyfKg8FAT7Sz2SyWyyXq9bqiMWQecaEE7uMnh5XdhUIBb968wc3NjdwvkUgEm81GvBsu6jzuQqGgp/8ulwt2u11sELvdrrgSgarpdFoOLUJF6axigw/dPdfX1/jSl74k6DDjShTT+LSeDgk2rbGdiPGz5XKJ8XiMbDaLdrstXg5wD2wmiJscjWw2C5PJpIgVBR2Hw4E3b97ovHs8HvR6PV0XkUhESyXBuFyup9MpIpGIFlYKooPBAIFAAMFgEG/fvpWowZYnAKowp9A5nU7VNMVGr0gkArvdrjp5CoiEU9tsNi2r4XBY8F46fAg9JuCawF9Op9MR64ZwckbJ2MDECnWKozabTQIpcC/OlctlxdQYUbJYLBIap9MpksmkINBkYrlcLnQ6HS3dVqtVkG+KbhSdnz17pvd2uVwiGAyiXC7ruolEIhiPxwJ8+/1+xYcI1uW1tFwuBRemuEHGCz+vh7wrt9ut82CxWFAqlSQuHHJbDmNj4XBYDB3G8RhVZKSIEcvFYoHHjx/rmqLg7ff7EQwGMR6P0el0VGM/m80eMMm8Xi+ur6/h9/vh9/sV4WQUik7D5XKJJ0+eoNvtSgByOp1qzSKkfrVaodPp4PT0FC6XS/dFCvVsDVutVnj9+jU+/PBD7HY7Xc8UMev1OjKZjLhskUgEoVAI9Xpdn1FGvsgLCgaDcobRFUenIEXFRCKBZDIp8Dkjb3RQ8l5Btw+FXGOMMcYYY4wx5rM589MItj/CIDI4M8NkMYScn+bYpnvsRuP3fRif+3lnIcfv9+PRo0cYjUZa8AAItBsIBMSbmM1mWtq4vPIHb8Zy2CpDkGq5XJajJRQK4ebmBgC04EajUUFRU6mUgLxutxv1el1Qzm63K54DF8ZAICBnCF0LXDSDwaCWlUajoSfgABCLxTCdThGPxwUrJluGVesUQSg2lctljEYjZLNZOTuOjo6wWCxgs9kESp7P5wKOUhzh02Xygyga1Wo1/Tm2LRHk22q15Kbp9/uCj1JAMZlMcLlcOk4KLzc3NxIbuPCzDptRMS7ZABRH6fV6SCaTErh4XrfbrcS6ZDKJ+XyuinTylQKBgGp/eY4uLi5wfHwMp9Op+uXLy0ucnJwo8vby5Us4HA45C1arFSqVCqLRKJbLpYQxttV0u11F5wg7pnjhdDoFmWYFN4HCfF/8fj/G47HEHl7TrDWn24rOHNbXk8VBgYgNOp1OB7vdDsFgUO1tfL1k9DCKSIGA7I/pdCrQs8vl0jVwfHyMZrOJer2uqNjbt29xenqKSCSiGuybmxs8evTowXt5COOmCMNmHy7zs9kM4/EYZ2dnWC6XiiF1u13Y7XZ8/etfR7fbFUiakbiTkxO0220JagSib7db+P1+VCoVAFD7mNfrRbVahd/vlzDJJrsnT56IybVcLnV97vd7NJtNPHv2TPGtXq+HQCCgCB2v+/l8Lq4WcO8C49I+Go3kRonFYnC73Xj16hVSqRSazab4TWazGeVyGVarFYVCAcC9kEXwcK1Wk4ASi8Uk6DC+xfd8tVrJyVcul+H3+3FycoJer4dmswm73S4Q82azwWq1EsQ9m81KuE4mk7i7u1PDnNfrRSgUEux6MpmgVqupRY4NT2xJYzS12+0iFArBZrOhVqvpvhaNRnXtAJAIRYcb7wV0QFGYoasqk8mg1+tJCCdwm5Xvbrdbrj5ymehkotOQguKhW43RRgASosxms9hP/NwwZheJRBAOhyVqLxYLiXw8106n8wFM/pBhRQC8Ea0yxhhjjDHGmM/uOBtTmFc//L+763tgf5+GMH/wBKavf6BflkT853SUP7+xZtLo/+u/8gN/WYKBd/469tYUlsX3/75pCwSv5tgZPz/9zOedhRw+ebfZbPD7/ar25g/Yg8FAT2atVqus8nQp8Gk+owPj8RiBQAButxutVksCBsWP09NT1Go1RRaA++WPjBTa3cnIITh5Op1ivV5rEaZTgE9tyZmw2+3YbDZ4+/YtHj9+jLu7O8TjcUQiEf3Az9fCxivGvrj8x2IxeL1eNJtNtFotAPfiD10gXCz5ZByAjs1sNiORSAjoSfaP1+vFdDpFvV7HbrdTpIhV73Sq0G3AJ/cUnxhD4YLE+mCKSHxP+D7w/DQaDdhsNiQSCVQqFT1x32w2SKfTioZZLBYBfUOhENLptOq1ufBx+WS0h/wPLvZ0T1D8GgwGYnQ0m02Bck0mEzabDZLJpOJ7dHOxiYg12hQ6+v0+7HY70um0BBRCk7no8rzH43GYTCZFmlarlaJJBDybzWYBYhnHoDBF/kYgEMBiscDt7a0qo1erlY4fgESiSCSC7XaruBWdJ3S4DAYDMW663S6ePn0q1wEFCDZs0bni9/tRLpdRKBTkvmCFPV0nTqdTLhByh7bbrWJtXHDZNsbXzZan5XKJYrEIi8XyoJmL/5ug6E8++QTJZBIul0vXI/8vRVEu8qVSCXa7HdFoFI1GA5vNRpyYQ3cLxRBGkCaTCdbrNQBIEJhOpxgMBnJv0G3Cz3uj0VCcqtvtipXCZiYCyM/Pz1EsFvHkyRO1WE0mEznbBoMBttstWq2WRA02zFWrVR0371l0CJKDRDcOm7JKpRKy2aza6/b7PbxerwRDOhhHo5GELzY6Hbp/arWaWv0CgQCi0ajupXSZ1Ot1OcDa7bZA0IwUshVtMBjoc8vY23a7RbVaRSgUQiAQUIMYzzMdf7FYDJvNRtfUycmJ7nm85ur1us5tMBhEt9tFMplUHToZYRTkGFnrdDoSXaLRqJyK8/kcAPTZp/uREbNAIKD7psfjQbvdxvHxMWazmZrOyELj55/vQbPZVBTUGGOMMcYYY4z57M3uu69gH/3w9qTYN3vYbzYw/eKXUPqXwij9i379avzlU5h9vnf6PpZoBON/5Zf/8Nd/5Zdh/t5D3j8t0/jvfAN3/+sQ/r3//m/9wF+X/5tjdP/NX3mnr7V78QbZf9QDdge/uQdSv7uA6f/33Z/J8RvzcH6s1qr1ei0OwnK5xPn5+QPgLxurCLadTCZaEMjpYINVJBIBAFnt6RRhXTiFGdrcO50OEomEoJX9fl9PU+n6ub29xZe//GWEw2F885vfVGUwl4FAICCOCfkJfPIcDAYVvdhsNjg9PUW9Xperh9ETxpT6/T4GgwFcLhcKhQLu7u4E5qR7hyLOarVCKpUSV4iiAcUNh8OBbrer5XQ6nSKTyWAymYiL8zu/8zuIRqOIRqMAoKWXy+pisUA6ndYTdz7R7nQ6YkKQm0EhbDabqSkmHA4jFouh0+koDsJ2o/1+j3q9riU8kUhgPB5jMBgo8sL4CyvS6VBYrVZyTRwCk0OhkFhB/X4fmUxGYhgFGopfZFww7kIHUjQaleMnGAyKIxIMBhEIBNBqtVAqldQsxErtyWQCl8slETIej8sJwqpj8lhMJhO22y2SySScTieur68BQPBhiit8L51OJ6xWKzqdDiKRiKqg6ZQ4fI/JhGL0ihGd6XSK2WyGWCyGTz75BEdHR7qu6BhjoxQFIovFomuDQguvnc1mI+HTZrPh6uoK0WgUlUpFLUp+vx/RaBTD4RCtVgv5fF5uneFwiPl8jvPzc4RCIWw2G1xcXCAajQpevFgsYLfb5ZgzmUwCc5+fn4vdxKgj7yEUoshq6vV6coT0ej2Ew2Hc3NyIiVUulxGJRCRkECbN64qxLq/Xi+12i8vLS9jtdiSTSXzzm99EMpkU96VUKsHtduves91ucXd3pxY8AAITOxwOlMtlCU2MYFIgW6/XcLlcaLVaMJlMek08D+Qd0emUy+UEvw4EAtjv94riUYAlj2a1Wgno3Wq1cHZ2JqfIZDKRKEqXEe+nrI632+1yls3ncxSLRaTTabnCAOiz3u129Vqz2ayOyel0wuPx4OLiQuKSw+FAMplEpVKB1WrFkydPUCqV5FzLZrOoVqsS6OmII7S+Vqshk8nIcVOr1ZDNZlGr1RAOh+WSK5VKCAQCSKVSqFarak3j/SUcDqPf7+uzzX9TKGKyTY3X8fHxsSKW0WgUNptN9xE6hiiUsrnPGGOMMcYYY4z57E7yt97CNnr4e6YtkPz9FXYX19j/yoco/5d82Fsf/plFzITmv/bHNzBZYjFU/9pjDM7Nf/jrkRmd3/jKT/FV/ORjstlR/h98A//Wf+Pv4z949o9+6J/7H371H+C/+pv/D7T+3W8Aph/MFTqc3SdvUPgb30XiW2sE3+5Q+Bsfw/Kff+eneOTG/Kh5ZyHnzZs3ODo6AgAtOXRcENLJJ6Pj8VhLDn+QZtxkv9/D5/NhPp9jOp3C7/crBsXFzG634/T0FJ1OR3Z5Pjn1+/2KY4TDYQQCAXQ6HfR6PTkmms0mMpkMUqmUbPqhUAi1Wk38E7PZrHYV4N7ZEwgEcHV1pcpePiWnKMKFLxQK6RwsFgu8evUKJycnsFgsYlzQScJFiAIJcC8scdm32+2KWJFDQ5cJq3D5PVlXzeWMDopAICDg8WazUayJzguKA16vF+VyWU/R2exFRkS32xWrhy08FFCOjo4EF2XUjG1QjMj5fD4tklySer2eXBM2mw2DwQC73Q69Xg/r9RpPnjxBpVLBZrPB8fGxKuAZPWFlNOHCZ2dnqk2mS2a326Hb7UqwqVarcmDk83lsNhtst1s5VOgAItx1vV4rPsGn9rPZDH6/XyKb2WyG1+sVaPrw9QUCAfFCFouFmtD6/b4atw4jWXQfjEYjiVLBYBDFYlH/nXwSMl8YP2E7Fp05jAVxyd/tdopxud1u+Hw+3N7eYjwe4/HjxyiXy8hkMnIM0bHS6XQwHA5xcnKizxRjVlzK1+u13EJsRzqsdQeAaDSK6+trcbRWq5XcLnR2MZrD64IumsFgINGXbCyybihW+Xw+9Pt9udLIoAqHw/D5fHA6nXC5XBIpj4+PdT+iG8dsNmM2myGTyYi1FYvFEAqFMJvNcHNzg0QiIcdRs9kUY4n3PLfbjZubGwyHQ71XFAsjkQiKxaKE6N1uJ7caHUYWiwW9Xg9HR0dy/y2XSzVoHQq8kUhEUTUAcr8xNlUsFnUN0O1HNxqdf6xGHw6HOD09ldOGoF+eRzY8jcfjB84WnnsynAi7Xq/XEr8JLaaTj61XBMQvl0t4PB6JW4eAb4fDAYfDoagbY7SLxQKZTEaiFYV1uuToqiIziAI57yPj8RhOp1PCfTKZRKfTUdyK/0YxjupyuXQ98/cInzfGGGM+32OJxbD6F34R2z/31fd9KMYYY8xPebadLrL/6R2Sv7vUr/Q/ncP2//w2AKD65zzY/xBMztpvguXZox/59Yd/7gRr7/f//jJsguX85E96+H/iqfzm1/Ef/LW/A595/sf+2bBlgn/73/ttdP/rv/xOX3s3m8H+D78F39/+vfs41f6Hu5+M+enOj+XI2e/3ihrxyTSdB/w9tuzY7Xa1F5lMJv3wzEXZ5/NJnEilUvj4448lPJCjEgwG5UohR8ZkMqHX68Fut0tIIJiWcQ+ycyh0uFwuVCoVxONxzGYzOVSi0ah+YKdDgQwVukXI3GFFMp9Yc9kmcPT6+lrL2mg0wsnJiRZwRiKur6+1/FJQ4vIynU4FNh4MBnA4HDg+PpajKRwOo9VqCW58e3sLm80Gn8+Hs7MzxdP45J/LGCNF5N5QROv3+xLQcrkc3r59K5cMHQ7ki5AVwZYsxrMo3lDYKBQKmM/nYt3QhQNA8ONwOAyn06kFixXPdMtwiaPAQqfGxcWFltTDlhmr1apjZjvao0ePFJEwm81i2JDnYzabUalUVNtdq9XQ7XZxenoq5wcjG4zBlMtlCXEETnc6HcFZJ5MJMpmMYl48llAoBKfTiWq1+qDFig6Yfr8vJw05SvV6XRE5Olh4rihssjUpGAzi9vYWmUwG6XRaf4ZCH1ueXC4XSqWSIjEU58iz4etmDJL8FVZ7EwTM1itWfAMQMJwxw0O3CB0lAFQVT3cEI24EmRO2bTKZEA6HBTUnIJiV7DyG5XKp+CKbxRaLhQQgk8kk2DOjMmzV4ufA4XAoGsc6eKvVimq1KiGNgON2u41IJKKWq2QyiXa7LXfQV77yFVSrVYGwGZXkOdntdnrdnU5HYsh2uxXkmayY8XisKCgFNIpJbrcbV1dXEpAikYiuL4Kb6cw7OjpCs9lUxDWTycBisaDVakko5nXN95vv9Ww2g9VqRS6XQ7VahcfjQTqdFpCeAij5XavVSrDvcDisuGwwGMR2u0Wv18N+v5fTKBQKyZ1Jhk6v1xOrieILHZkUlieTiVhJtVoNhUJBYh+F01arhcFgoMgfHyCYzWZdlxR+2u02zs7O0O/3sd/vJWzRPcmYoDHGGPP5HJPNjtlf+TOo/qvnaP6iDbX/ghOtf/cbsDx//L4PzRhjjPkpzqZShe0f/4F+mf/puzlHtg5gehL8of/d8vQcw5MfrAJt3MCiEPpJDvenNha/H0f/4i3MePcHU27zEn/p3/8nGP5rf7yYY7JaYfH7/ySHaMxPOO8s5BwdHaFYLAqGyyfp5CGMRiO1ETWbTcTjcex2O9zc3MDtdiObzeoJrM/nw2azQb1eF6MiFAohGAwKjEzeDp+qbrdbsWSCwSBisZigrHTOOJ1OzOdzRYssFgtOT09RLBaRzWbFlKEbiE9gAYifQugllw8+taVrxev1qjmF9bSFQkFPiP1+v14PW2QYFQOguEoul5MDyWazqb2lWCwikUjAbDbLRePz+VAul7HdbmG32yWGLBYL3NzcoF6vI5lMIp/PYzabqVq73W6rOWo6nWK5XMrRsFwucXx8jHq9juFwiPPzcwwGA3GB6LTgkjydTvHlL39Zjh86MGw2m57i393daVli/CWbzeLu7g5WqxUvXrzQ8fh8PglTwL276xAcTaeQyWTSYg8A1WoVqVQK8/lc9eR8cs/mqJubG7hcLjSbTbl66Nii4+v4+BitVksuMS6eBE4/fvxY0TGHw4FYLKZK8GAwiLu7O+Tzebx+/Rqnp6eKYJBDwnjhzc0NbDab3A0nJycYDodqd9put6jVaphOp0ilUgCgZZ5OB7J26BawWq3i+dDN5XK55Dahi4CxE1a208Vmt9sxHo9Rq9UQjUYFPwag7xWPx8VnslgsePPmjTgiJpNJDBUA+iyxeQi4F6WOj48lrvr9fsVsGAlkk9AhGHm1WqnpyOVyYbvd4uTkRBFAAAKtJ5NJQct5XfEa4nGxUY3iIM8569jpnmMEi9BlisidTgfhcFiviyJnrVZTOx6vP0aMWq0WfvVXfxUABBSeTqdYrVY4Pj5W9Gg6nYrHxGrwbrcrgZouRTJw6E6KxWK6DukM5OfbbDZjNBohHo8jHA4LCv/mzRt89atfxW63wyeffCLoMkWVQCAgJg7v8YzzUWwZDAbw+XzimvF+Tgg5o6WMxVHw5X2STYcUvOfzOYLBoAS1fr+vpkGLxaL752AwQC6XU608hWibzSbxeblc6gEC31+fz6d/J8rl8gM2m9lsRjgc1vd78+YNstksbDabxEGynChEGmOMMZ+/MdnsGP/lr6L73II19xAzsIwAd78eMcQcY4wx5o8d02zxA6G/f1rm9jc/wF9NfuvH/nt5eweL8I+WCizPH2P6L30Nxf/2B9j92a/Amsv+pIdpzE8w7yzkcNlyOBwA7hdqWvttNhtSqZSAl36/X09Q0+m0RBXGG3a7nSz6BLOSq5JOpzEej3F7e4v9fg+n04lAICCuBIUEOjIoeAQCATlwCJQ95NTw73Ax9ng8chuwicdsNiOVSqkpaLvdwul0qhWIteibzUZPxtmGAkCAZUZp8vk8arWa2p6sVqsiIGazGbVaDS6XCx6PB81mUxwSs9mMu7s7OVBarZacM4ypmM1mxWWazab4K9vtFv1+/wFMebFYyHFRKpUQDAbh9/slUux2uwdxpWg0qsrjcDiMUqmkJ9Nc9ihM8b1nJbfdbsd6vUan01FTz/HxseIJdMewJr3VaiGZTCouxifiXq9XLJvD9iTCrynUsKrd6XSiXq/D5/OJocGF0uFwKM5GfozL5ZJbiWBaNj0x9pLP53F7eyu3EMHFZOIwclSr1XB3dyewK9kjBGQfLo31el2OpfF4rOuQrhC6JujQIlSbjrhGoyF2EGuoHQ4HXrx4IQYUr/d4PK5r5/LyEoVCQZExRhQnk4kiVozBLBYLxe4oZPD9JXh5Pp8jk8kIUD2fz5FKpXB+fi4XG98rOqsYg6PTi58biq8Oh0ONUby+GIeiS4URH4qc5M5Eo1EJnYdsJfJYBoOBgLcUiCgwkXPT6/Uk0IzHY+TzefF8WN9NmPV6vUar1RJUmNB1j8cDl8uFjz76SA6ccDiMdDoNAOj1egLwEgC/Xq/lRksmk2i1WgIDszo+FArp9Xa7XbF7GAOt1WpqnnM4HHIKEupNYarVaiGTycitxaY3XvuHUUS2C/IzeShO87qyWq1ot9sYj8fw+XyIRqOo1+tiifl8PrkD+f0oCvFcr9drzOdzOf/Y2LXb7XSNHMLw+XnifcZmsyGfz8Nut6PZbErQstlsKJfL8Pl8OD8/F9+N8a7BYCA3FVvvCGOmSMi4qjHGGPP5HLPXg/7jH/yj8N4CdL4e/jkfkTHGGPM+xvQj0kC2CeD9TuWH/vfNXRm+6vYH/jf7EHC9+OF/92c9lueP8fifv/6J/37k1yuwppI/8L+ZP3yK8l+MoPuBBTAB1V91ofqX8rB87yG9MT/7eWchhwsqBRRa/Sl8LBYLgWhpcydXYzgcqrmI7TX+71mwWCHtcDgEmg2Hw8jlcpjP59jtdqhWq4qhcMhbmM/ngvxySaeYwogHXQpcDghepWjkcDhwe3uryAyXjc1mI9YJ4zBsR7Lb7ajVampqYaQpEAhoWSXDg9Z+ChpcmAkqbrVaWrC55Pf7fcUVJpMJnj9/jsFgIEGICyKXPi7xjBbd3d0JmstzTAYORQwKTzw/w+FQC3U8HsfNzY3At/F4HM1mUzXyz549Q6VS0RLGhiMydsgFYR30drvF+fm5FlGLxfIA9MvYVqFQwNu3bx80c8XjcYzHY5RKJSQSCUXr2JbEaIrNZoPNZpNjaL1eS/zy+/1ixRBmarVa1VxFZgsjJWazWa6aSCSCdrsNAAgGgyiXy3j27JniLLzeBoMBnj17hslkIheU2+1Gv9/H7e2tnAis645Go4omvnr1Cm63G/l8/sH1YbVaUavV1JZF1xWZO6lUSl+DogwdUTc3N1q6Dx1XjHDx2uMy/ujRI4lJJpNJTpF2u42joyOJDXSVtFothMPhB/Dbcrksbs9yudR1yut6NpvpGFh7TjHObDbj9vYWz549g8PhwJs3bxCNRiX4UmALBAKw2+0CqFOIoKOOrhk2uhGavNlsJCwtFgsJXoxMUVgj94nRJIqQ8XgcL1++BHDvtHn+/LnEE/5dl8sl6DvbnyhYsr6cIPFms6m4I5vi2u02UqmUHIHT6VRNehS8CU+m+2u5XCISieD29lb3Vn4OeR/bbrcSyOjCcrvdYgDRtcRzOB6P4XK5xKwhvBmAWgXL5bKg3AQus8a8Xq/LObZcLiXSbLf3P+jw67Etz+v1PuDo0JnEaCMdd3RIko+Vz+fRbDb12iiiMzpFEYmxMbrYGB8kv83v9+vzw9dEyDKFXmOMMeaLN1sHfqw63s/KWGIxzH/9l77v158Glocxxvy8Z7/ZIP936rD+gLZs2wjI/K1LbKq1H/k1vL/9HXiq368GeatbbBrNn9ah/tizDrvxVxJ/8BP//X89+zvYe93f9/smmx2TUz+2joe/vwoCJo8RSf95zTsLOavVCrVaTfBer9erxQyA+AJsz+GyzGWdMRcKDs1mE/v9Hq1WS09Y7XY7gPsmq8FggE6no0YdQon5Qz7bc9g+xPgHY078wZ/OgG63KzcM24Fub28BAJlMRq4AVn1TbFgsFlpqHA4H0uk0Li4u9LQ5l8uhVCrBZrMJ3JnP5yVYnJ2dqT2GQF0yJ8jCcbvdSKfTmM1mcrEcHR3h9vYWVqsVsVgML1++1FNnq9WKUCiEYrEopgVr1zOZDMrl8oOWrnq9rqfUp6enWui73a6WIsJtc7mcoLyslqajYrFYIJm8V2UvLy8RDAYBQE4et9stJ8hwONTfSyQSYueYTCbYbDZBsHe7na6j5XIJk8mERCKBcrmMdDqN9Xot9gldDaxPfvr0KYrFoiC3jF2Uy2XF7wjyXa/XOD8/V/SKoh8AcVXIr1mv1xiNRoqoMXYH3AuIjLRQCCAolxwonhM6LtjYc3h+lsslms2mmEMOhwMul0utPIw3Ma5G5w6brvgZIseEMZDDiAoFTi7YPM+DwUBONoqM+Xwew+FQLVL8vNKFQVcMW8/oqqMgtVqtxLV6/PgxOp2OHF10tzHm1Gw2EY1G5fRgixqdT2/fvpXDZzAYKI5GyDbFAoKT6QbhOXe5XGi325hOpxIQl8ul+FMUHu12u8C4zWYTsVhMn6der6f7C88RI4SJRAL7/V415ow5MZZHgLvf75d7hkIzmVE2m03Cjd/vVwSRnzeXyyVI+6effqpzzOgSP0fk5xAKTIcdhUcKF/F4HPv9Hul0WnDxw0YnNvK9ffsWdrtdLXvdbleRRDqi6Iqk44stcZeXlwiFQopb8RwfRpN8Ph9KpRLMZjOOj48VZ6Rg7vf7MR6PdV+gIMzWLordFIn6/b5ceNVqVS1Y4/EYJpNJUVVW2lPoXC6XSCaTartbLpcS21KpFHa7HWazGfb7vaJ8xhhjzBdvtg4TTO9YPfxZmea//w00/o9h/H//t/+H7/sV/j/3cPs/f7fqYWOM+TzN9uoW2X/QRPwP1vo9+wDI/mclbL/3M9WPmv16hehvvUD6ny0e/PL+9k8uovxpHks8iu7zH0KHNubnNj9WtIqV4azZJZCUP+CzeYTLJFkSrJvm01EKAF6vF8FgEK1WS0wMq9WKbrerZqn9fi+GAl0OZrMZhUJB0OI3b95gu93Kzh8IBOSGWC6XcqUMBgM0m00EAgHVzz5//lxNPoQxs3GGzTxcxDudDrbbLVKpFBqNhp748un34RJJKPRwOEQoFJJAQzeCx+NBJpMRVJaCGJcfCi02mw3AfaU0oa9kwQDQssUWJi46LpcL5XIZoVAILpdLyxLjJLvdTg6H/ffo4ul0Gv1+Hx999BEymQw++OAD9Pt9OQMO+UWETBNqy4jKcrnEfD4Xr4TLntVq1RN9PvlfLpdyBeVyOS3QrNgGIE6HxWJRYxQXOQpDXEj5fsRiMfT7fbTbbcRiMbltJpOJ4iO3t7cCwVLsstls2G63D2DGjHLQwRCLxQRftVgscLlcaubie0fRhk4Lk8kkPst4PNb1wTgfOTdmsxnJZBKj0UhCHQWk6XSqaFKz2RR/iNcz+R+xWEwCDcHC5A85HA7F0mw2G0qlkuDC6/UadrtdkUfG2obDIbLZrM5Bp9PRa2dL3GazQSaTgclkQq1W0yLf6/UwHo8RDofh8XjkTuPnhM4VCh8UehiH4nXJ2B/5OcA9J4fRTjomGJ3qdrvodDrIZDL4hV/4BcF5+Xnu9/tyo7CtK51OSwDY7XYSRarVKkqlEvb7PSqVCvL5PDweD+LxOK6urhAK3QPsRqMRnE6nXlMgEEAwGNR9i64vuoF4XY1GI8UZg8Eg+v2+eFuMMcXjcTllKKo0m03k83n0ej0kEgk5AjudDjwejz4LBPtWq1XE43EJMHTT8DOdTCYFeCagmjE2CkWMmDGOaDKZkM1mdQ8Zj8dqc9vv94roUQQ+dKp5vV45t1ibzrihyWQSAJ33Cq/Xq+uaMGM+MNhut6hWqzg9PUW/39f9HLjnKBH8zEY9AHLfUaTheaaDbTgcitnGmJ8xxhjzOZz9DuYfgcGyj/bYlN9fLOKnOmYLav+9b+Dv/vX/BT76+t/+gX/kbx395/jor/0N/NrLPvr/xq+8U/2wMcZ8Xmb79hqOf/gRjv/mDY7/5g3Sf+v1j/X5343HsPyTjx782v8E8WyTwwFL6P0Ckv+42TZbiH3XiJ6/73lnIcfpdGIymaBeryORSCCRSODly5ew2+2IRCISMux2OyqVCjweD3a7HRKJhECUrBDmMtNqtWAymXB8fIzBYACTySRnAatml8ullh6yKHa7Ha6vrxGJRNDpdHB6egoAgqVykSkWixJryLXgghMMBjEcDgXGTaVSgvzSdcGYTjKZFCcGgFqtTk5OBPUkO4gL1WAwkACyXq/lpqGYQXgrnQhc5lqtlpg9mUxGTggCQFnb3Ww2EYlE1MDDuBjFBAopFotFTVz7/V7uG74OgnitVqsAovF4HIvFAldXV3C5XIhEIhKO6Cyi24YwU5vNpohcPB5XxXQsFsPFxYXqnvmEnfGH5XIp3hLB1nTe8PphY47b7cZwOJQzwmq1SnTh12Xzl9frVTsWn+h3Oh2JAwRYE27LqBzjc5FIRJwcNu7QEcPGqWKxKLGEyyeFP/I4KJ6xEpuRDjpBgsGgqpt5rLxW6YhiRLBUKsHn86kWnQKZzWZDOBwWJ6pWq2nppihGcTUWi4kxQzEsHA5LpKQjIh6Pq3mM7+PLly/FsWKTG2Hjo9FIURy2AW23W13DjG5RhNput9jv9/B4PALUjsdjfT55P9lsNhgMBnLGABAXhuf7ED5Op8rjx4/1fXmeKVjRVceoTiaTwWg0QqPRkLsJuI9PAffxT4fDgUwmg+vra50rh8OBxWIht4zT6UQ0GkW73RbPiQ5CHjOFxtVqhcFggLOzM3g8Hnz88cfIZDLwfe/JLwVTCiKMNH7yySc4PT2Vs6ZararpLxQKCf7L+229XseTJ0/U3sV7Ep11u91OHKBAICChNPn/Z+/Pg61P7LQ+7Dn7vu/73d+t9arVWkYSGI+TCTA4OEwI9sQVDEWqgo1ZDBT8QVE2cWISm3LFgRATY5IY4jAUZFjtVAjIHjIajVrqUW/vdtdzz77v+5o/bj+P7h21NG+PWt3q1vlW3dLy3uV3zvmd373f5/c8nyceh9lsxtXVlZhitVoN8XhcTjoKN3RCUWAmDy0ej+P6+lrXn/l8jlqtJrEOuGGtud1uuN1utZnxeZ1Op2o+BCDuD2O7PFaKocCNQHN0dIRqtSrhx+fzodvtStC5LdY6nU4xkeg2oshEsZmtcrvZzW4+fbPu9ZH8/71PngIAtoDv8seYYPoBp/Knfgrv/Mn/M/YtPzgu6jba8ccC1/iH/+FfgtH5vZGK3ezmUz2bNVbVGlbVGta3kB4f5RizKYx+2zFg/OEdL5b6AP+34m/5TX/9//HsfwAMRt/z/29XK7jOuzD9ujZze3OL7fB7P383P5p5aSGHDSDT6VT1tlyEGH0iUDgYDIp9cbv5Bri5k87KZY/Ho2YWi8WiZWyxWKg6mMspGSitVkvLInkKi8UC0+lUyzJbbJLJJFwuF4rFIlarFYbDoRgV/X5f8YLtdis+AuGyvDPf6/UUD6AAxDvVrBXm92b0hO4LPha6F25X/rIliXd8WUNMh4TRaEStVkM0GlXF8Xq9hsFgUNvPK6+8ogrgbreL5XKJ+Xwunkg6fUMO5/PHFpnRaKTGHJvNhna7jVgshnq9rspj1h97PB4ttPP5HJlMRtEsCh4EsjLCBkBRIIKBb8OCKVyxnSkYDOLp06cSNAAollKtVrG/vw+v14terydQKhtv+v2+4nKlUklV23QS8TVmhA24YXywdp5iEZfC8XgMj8eD7XYrVxTrvvl6vvXWW/B6vQgGg/D7/Wi1WphMJhIK6MAhLHhvbw/tdhvb7RZGo1ExF75e4/EYq9VKgFUAd+CwRqMRPp8P8XhcUZ1mswmfzyd+zXQ6RalUQq/XQzablSuGr9twOEQoFMJ0OkWlUoHZbMbe3h4Gg4GYMLlcDt1uV+4XPl42mRHwTF4Wo1ibzUZNYoyjDIdDBAIBXFxcqO2N34uvXzgcFpyYNeAUhCg0MXoG3PCJ6AybzWbodrtYrVZqGaLrD4BccoSAM1pIMDbFR163KDYDN0BuCgh0A1E4YqyIQsVisUC/38f9+/dRKBTkAuP7icypYDAo0YnRQADiJgWDQcXyKErSObdardDtduF2u5HNZlGr1fQ8sZWMrWEEqVPwjsfjOD09xcHBwZ3KckLTeV3j+93n88Hn86HRaMBkMqmtjWIYf9ZtoXg0GkkkYhzxdoMYXVN0JJExxs+h6DQajVSPTkfhbXHJ5XIpmkfXW7fbhcFgwPHxMRaLBcbjMc7Pz+Hz+STSbrdbXX8Iu2ZjG5sR+b7lOU3H1u2Gvt3sZjefzjG/KCL3j7owLgHD+ubDe7XB3i+2YPzlNz/uw/tQxmA243/xB/6/H+hrwiYHXvzFz/yIjmg3u9mNye9D7/d/Bb3f/xXMf/aLMLz3d+j67BKOf/g6sHl/gPIHmfXpBer/IoXNy6/8d7/+n4axrjfe/9+enSH7ixXs/WJbH/G/f4F1u/PDHPJuPsC89KtKl4zL5RLI0u12i7fBP/RHoxHW67WcAtPpFG63G81mU3+I0wHg9/sF6AwGgxiNRlitVjg+Pr7j3kkmk8jn87DZbIofUNS4HUup1+uIx+Oq+ObCZrfbcXl5iYcPH2qBazab8Hg8SKVSij0BUPsRF1bCdb1eLxKJBIbDoaDLt6NS5DC8/fbbag4i34cOhdVqpeeFCxTbp1hZzaWOfA5WrjP2wZYru92O09NTLaytVgtf/OIXtQBTzGHDULPZlMD2hS98QXyJX9+8RQgwY0Tk+mw2G4lEJycnEgR4F5vCWjabVe35YDDAcDjE8fGxjv+2E2E4HGpxSqfTqujudDpqbfL5fHC5XDg9PcXh4aFAveFwGBcXF5jNZhIJ6GYgLDoSiUhgoZOGIhoXZFbRT6dTHB4eSqDjXX9WE0ciEbkj6AYIBAKoVCrw+XyK7Ph8PgFcR6OR4kyM+hHCWi6XFRViZJGAZo/HIzgwXTMEjPP1KhaLglDn83ktxGwqYxTI4/EgnU4jHo+j3+/j4uJCLWwEKLOyvl6vS8zy+/3o9Xp48OABnE4nLi4u4PF4tLDzWJ1Op967AMSVogBbr98A3igS0blksVgwm82Qz+cRiUQkLhLwTB4O3Xs8BoqsgUBATiHGa/heonONEF26T66urtBoNBCNRpFOpxVDYkyU5ywAuddyuRzMZrMcHow9MtbEn31+fo5YLKZry2q1EuSZQGE2RDF2enx8jG63i1KppAYocn8oOmy3W8RiMYkr5DJRCKT4UqvVFEEiDJuxOpPJhNPTU3zuc59Dv9/HaDRCJBKBy+VCv9+HyWTC4eEhqtUqAMiZxBir0+lEq9XC5z73ORQKBXS7XVitVgQCAUSjUfHRotEoarUaPB4P+v2+rpvkGd2GVJNtBkBiP2vFR6MRRqMRHjx4cMcpZrPZdOxsgItGo2KN8XcSGVsUmm7Xot92i9GBCUDRQQp6brdb4vq9e7v64d3s5tM863YHm7eeIf2ffhuZv3Tz4ft/fgvrp6fA9gdU2XyC5vw//gL+VPD5B/oai8GE3/svfROmk8Mf0VHtZjc/uWOKRFD5tx5huGfAcM+AxuctKP6ZL8H46sMP/Wdl/5M38AuVL37gr3syTcNd/cFi0uoyj/WTF9/9+D6iz25+NPPSQg4t/PF4XIwQgow7nY6WUqfTKbYD65ebzaZqdSlghMNhuQsePXqE4XAo0Cxw88c9eTpc9geDgX4G635ZN84liGBPRmkIUuaCDty4iywWCwKBgO7e0pHDO/cul0vRgV6vh0wmg81mA4fDAa/Xi2azqXauYDCIy8tLmM1mHB8fY7lcotvt6m4uozuZTEZOh1AoBJ/Ph3fffRfL5VI14nQfTCYT/TzGtm4v+YyezOdzADcA5adPnwpCDUCugslkglgsphYjRi9uu1+4IBNcTHGNLUZ83ieTCabTKXw+HyKRiCJu5Ait12v0ej3VI3c6HbhcLsXAjEbjHacNGTJ0XtjtdgCQ64rclO12i0qlIngyH/vBwQEqlYpcAM1mE+v1WvBZin9s5VksFnjy5AkODw8V5Wm1WvB4PFiv16rdZgMYGUdkRHFJ5tJH/ozD4ZD44Xa7JXaSvUQXDkXI5XIJn8+nc5tMoHa7jXQ6rTgR66TJKmETUyaTEZCbYlQsFpMDhGwWOo/a7TZsNptqrAn3JsybbotMJoNGo4Fer6dY23q9hsVi0VJO3gjdJMPhUJXuAPT/TSYTRKNRWK1WLfVsdSuVSlgsFkgkEmpFYmsaq+4JSKbwy0avWCwmhk+pVEIwGJTARSAw44+8LjF6dJvDRGEuGo3ecRiZTCYMBgNdExgTpbvMZDKh3W7rfKlUKkilUhLu6Pjie4qtWaPRSOBqCppnZ2dyMxFiTYGYzie2tNHRtt1uFeei+MS4UL/fv8ND4vufjjJyu8g0YyX95eWl3Hp2u13V6eR7MQLYaDTklmN0cDAYIB6PqwLdaDQKGsx6+m63K0Ht+PgYT58+VbxwtVohn88jk8mI8cXrNUH0o9EI/X5f5yqvR9PpVA4pcr+WyyUePXqk5kAAeu4p5iyXS2y3Wwn4Ho8H+Xwefr8fqVRKbLdQKIRK5Qc3VexmN7v5dMx2udDHh3En/MdlTA9P8HP/yjdhMXzwmMZfin8H+d8X+xEc1W5285M7pnAItd97hMXtQjwDsDUDlZ/2f+g/bzufo/7/yn0gV85w48C/+EtfhuvvffNDP57dfHjz0q8oW0QI9IxGo3jzzTfvOFIIxSQHYjKZoNlsIplMasF0OBy4vr7WcsyFnMBTAmLJ9aAbIRQKwWAwoFqt3mnhIYOCn0seCmvIGQcLhUJot9viihDAyigIAb0ElPKuv8ViQSQSwfPnz1W/3Gq1kMvlVG0MAK+88gr6/b7iOOPxGAcHB4p9cfGLxWJqyVkul9jf30etVpOLgY4Gsj4Y29putxI0ePd6OBwiFospYsAWGx4/n1M6fRhXs9vtcLvdWK1WOpZut6uIDit/z87OMJvN8Nt+228ThJRuAcaE6IYiiJVRNH5vp9OpGAMjGHt7eyiXy3eaZLj805nh8XiQy+UAQHBgm82mO+pkwOTzeezt7SmCQWAv429svGJcKJlMqs2ILJByuYxIJAK3243r62st4vfu3cNqtUK5XMZqtUK/3/+eVi8Ck41Go4Sr1WqlxizG/RhhWiwWsFqtuH//viCxfO1nsxmsVivy+byWXrfbraWaImk6nVYjD4U3so3IJkqn0+LlkGPF14RAYoPBIM4T44U2m00iIxuZ6NJ68eIF0uk0hsMhstmsxBo6h+h+oHjpdrtVOd1utyWULhYLZDIZAYDJtolEIggGgyiXyxgMBhKBGPuhqAfcwGn5uvH4bDabYNTlchk+n0/PLyvq+TrSSUP2znK5FC+IAhLdYGwSIw+m1+thtVohmUzKIdLv98WZ4nWOxzocDtHr9SReE3rd7XZxdHQEq9WKcDiMYrEIAIp/PX/+HH6/H36/H/V6XU4ltvH1ej0MBgO0223E43H97EgkoufH4XBI7KBLcW9vT+/Ffr+Pg4MDOVMozCyXS732jH11u10kk0n4/X45Mxm/oihKATsUCokjRCceRSdyuwgtZ4SO76uDgwOMx2N90N1EXhSvi3xNeZ2hi2i1WqFUKqk9r9PpSCSqVCpqZuP3ZQSSTDYAagtsNBpyDu1mN7vZzSdxphkv/lL8Ox/3YexmN7vh+L2YRT5akHjs//I6/rO/+j9DdfkbQ5QLizD+1l/8V+H92zsR58d9XlrIIWeAMFo2khwfH2M4HMrKHgqFMJvN9Ic5/+hn8wvvOjN+YrFYtIhyieLyRncJ4x63QcKs/PV4POh0OoqdkCdC+zzvro/HY1Xikg/T6XTuxFoI8nW5XLoTzdgVHQt0UhBASwGJcSZGxninlwu02+1W3XokEoHRaMT5+Tm22y0ePHig1iQ2TZEZMhqN4Ha75SBgMxC5O/V6HQ6HA+12Gw6HA9FoVMtNIpFQ3KdUKiEUCkm4mc/ncLvdWC6XiqkxvsM4xWazwauvvopf+ZVfEceCwFoAiEQisNlsOD09xXK5VENVOBxWvbXL5ZLgxXrzfr8Pq9WqSA2XUa/XK64FwdJ8HunUsNvtipkwflMqleBwOATxtdvtiEajEs+2261qu5vNJo6OjnQ8pVIJx8fHuttP7s1sNsOTJ09gNptxcnKC4XAIi8WCUqkktwQbcTweDzabjWJWi8VCVfA8z81ms87/er0uFxIhs+QObbdbCUAUzKbTKVwuFxwOB4bDodwjHo9HQiqZHs1m885ryZ8DQK91NptV29Tl5SVyuZzO4U6no/Y4AOIfzedzJJNJCSyj0QjX19dIpVKKNI3HY7XGLRYLGI1Gvb/5XmA0iPEjg8Eg1wPFoHg8DpPJhFKppLgYmSl0olGY2263cLvdqNfr8Pv9aDabEiFYG06nCgXY4XAIl8ul45xOp3LNXF9fY7VaIZFIwOl04smTJ9hutzo3yeqioGOxWO44+SgMDAYD9Ho9VVy3Wi3k83nFJr1er5xCrVZL5zmB2OTbTKdTPHv2DCcnJzpP2GrF56bZbIo5RBcfRQ6v1yuIPK/B7XYb4XBYkT8KZJvNBrVaDYlEQg4puoI2m42gxSaTCZeXl3ovTyYTXddCoZDeq6vVCtVqFcFgEF6vV66o8/NzQa4p8AM3bCI6BxOJBFwuF8bjsThqdrtd/CSXy4XVaqXH2+v10G63EQwG5bqhE5RD6Div24xfEpLPOODV1RVmsxkSiYRat3azm93sZje72c1uPqmzXa0Q+yu/gr/7l38Gf+H1f+37ft5/8Es/h//mf/fT8P0/fvVTEy39NM8Hgh0vl0ucnZ0BgGDCZFLQhk6nyGg0QiwWw2q1wsXFBfb29uD3+1UZzkU9GAwCuFkYuQxxKd1ut1rQNpsNZrMZMpmMnAOMeLBSORAIoNVqqcqcsFtyD2j1B26EqV6vB7vdDo/Hg3A4jMlkglarpRgEo0KNRkNxiFwuh6urK8V3bsdiAKhel04EwqDX6zW63a6iGbVaDalUCmazGc1mUwLY7ZgWIzvZbFZRHcJI6XpotVro9Xq4d++eqnc3mw3S6TRevHihaupwOIxAICB3AiMYw+EQjx49wmKxkLBFZwgdBmRkEAwbCAQklC0WC1Vmt1otvPbaa+J8NJtNbLdbpFIplMtlTKdTbDYbbLdbJBIJVKtViVZc0PmfBD23Wi1sNhvFXyhOLJdLMZsYz2GjF4+fPCS3261qbt7Vvy020v3FZbpYLEoYIvuG5xFfP7JIAEj04L+zDpsRkWKxiGg0qigWhRTClnne9Ho9MYnC4bDeW71eTwJivV7HYDDAwcGBmEIUQiuVCjwejx5fIpGQg8lqtSqG1Wq19F5gO1uhUFAkJhqNolAoyFH27rvvwuVyqXmJ5zdFCABa4Plc8LX1er1ySjUaN7lZvq/I5fF6vWq3YwNZv99HOByWyEI3CVuWJpMJHj9+DLvdrhja9fW13BZOp1OxtOFwCKfTqZYtQq+XyyWq1Sqy2ayidORC0TlE8YFRovl8LrEAgJgwrFRn7NHj8ej6wvN0vV6r8YnRK77+l5eXcpE5HA65jShOkynDx0Jhg4Iewdj8GSaTCYvFAldXVzg+PpZAOR6PFY9kBI9Q+s1mI6cKgcv1eh3D4fBOsyBFcwpKDocDr776Kmq1GprNJtLptLhV/Hl8PVhNDkBOOYKMCdFnK91qtVJsk2JOPp+XMJ3JZHQNOj4+xmq1Qq1WQzKZ1PuJAG4yg+jAori4Xq/1+tBBxeeD0Vj+jtrNbnbzkzGmUBDdP/gVdP/gDYD0k17BbVhvMdn8gI713exmNx/pGDZbYPN9/u1HnOoM/fVv4P6fusbf+DP/0/f9uP+nnsL7t3/1R3sQu/nQ5qWFHN45jcVi4kjwLqjH41FEplqtIpfLqfmHQsB8PtedTYPBIJt9JBLBYDCAwWCAwWC4U43NGvBmsynGCRegUCiEWq2mP8zfeOMNJJNJ+Hw++P1+sRbOz8+RTqdhNBolyPCPfMbFWq2WWBknJydyxRAM6vF44Ha7tYizbnqz2Sga0u1277hJ4vH4ndjJ2dkZHj16BIvFgnw+f4fLQVgpIbqpVErOAi7Ztxdnsj622y0CgQAcDgcuLi70tbFYDFdXV4pZ8fnudrvI5XJqZNpsNnC5XDg7O0O/30cikZBAxPgCG8YIci0UCnLtsLnH5XIpakTYMlt+uPizoYoMoMFgIAGC7JRms4larYaHDx/qeUqn0xLDCGQeDAZyWjBiZDQaEQgEkE6n4ff78fWvfx3hcBh2ux2DwQB+v18CTaPRwMHBgaIVRqMR19fXiqvEYjHFbVKplKJcdC7w84bDIYAbIWexWCAajSo2MpvN5ATL5XJqLDs8PMRoNMJkMhHYulqtSkQ6PDyEz+dDPp+/IwbY7XYUi0U5KRjDITvGaDRivV7reSd7iO4wvtYej0fxF7p2+DpRrKHjq1KpwO/3I51OK6pE8ZRuEApcrVYLqVRKYiyZPoVCAblcTvE/NqTReXFwcIBGoyEAbq1WkyOObCe68Bh1Wq1WEtgoWEwmE71HeF2he4ag6e12qygknXKMcrKZiuDxTCajtje3243nz59jNpupuYqRUDrnyOcxGAyYzWYSOxi/I6eG9dtkgs3nc4GHGcejcEMYdCgUwvX1tTg3m80G2WxW7hE6E0ejkf69Xq8jEonIXbZcLsUXSiQSKJVKGAwGik/xvbRarfQ+47Xr9s8ly4q/AyqVihyawI3An8/nxSxLpVJ6P5BzRSF4MpnofZ1IJCSOMfbI15LcK6PRiHv37ul7dbtdHB4eotvt4sWLF4hEIqqSH4/HqhhnxKrZbEpU5+PhdTAWi+l7xONxCWL9fl+iz252s5tP/5jjMZR//32MMgaMMjcA0u1XHn/ch/VDjeWf/xoe/eM/+nEfxm52s5v3ZnWZR/prYxh+neHX2gOSf/vFj/znr1tt2P/x6+/7sXnvhvJuPhnz0kIOGSsUTiaTieIUjBz0+30Eg0Ekk0n0+300Gg1FJyaTiaqR1+u1GC7tdltsA4fDoTvQq9UKfr9fUQcuWvwDezwey9VgtVrVQkMQKTkvsVgMrVZLTTxWq1URHUZf+HjY3uRwONBqteB0OuHz+WA0GlGtVrUkezwenJ+fC8hLFwuZCoxaMAZjNpvx4MEDvPPOO+IC0RUwnU61PK1WK7z66qu4vLwUJ4YOBcaBZrOZ4gb87wCQSCRgt9vRbrcF7YxEIrqrTUGjVCqhXq/j4OBA7hU6qWq1Gg4PDxWh4HJKrhBjDIFAQM1IjCuQnTKdTtFoNCTuFQoFOBwOwa5ZW8/XnLExs9msxY4VxeTrWK1WQbDtdrvYGwTREkxLlsfZ2RlOTk7klEokEhICWq2WWDs8NgoqJpNJDTvkEb399ttIpVKCGTcaDZTLZZycnKh2nC1erFeu1Wp4/PgxJpMJOp0OptMpUqkUVquVWt7oOpjNZkgmk3IYketC5wXbfsh1MRqNOr+2260caozY8bzgIs2YD8VLvq/4fiZMl7GV7XYLh8MhtggXXXKNBoMBksmkqrXpHKJLBIAcVGy1MhqNuLy8RDAYlHOHIguB0hRgHQ4HqtUqer2eYNvxeFwtZicnJ4JHD4dDXF9fw+v1yg1ltVphs9n0HqeYSjEnEokI+j0YDAQ+7/f7sNlsePz4MTqdDsrlstq1KPh6PB61KFEMtVgs4jANBgNd/8iiqtVqAKBGP4oaBIfT1UWha7FY6Fys1+s4OTnBfD6HyWSSW41NbGxzYnyS3BmXyyUhnPG8fD4vVhlFaX4/niMUkVidHgwG5T5qNBqKmlarVWQyGcXs+Hjy+TxisZh+H/A853WKAh/PS7raZrMZWq0WptOpzhuyzwDIrbXZbBR/pOi3Wq0QCATg9/sxHA5Rr9clKm23W6TTaVQqFWQyGTWKEb4+Ho9xenoqp+LJyYl+T/HmAx1Su9nNbn4yZvTFHJaeW/+HAeg8cML43t9sn8jZbmFY/uZcRX+lm0P6v598yAe0m93sxvCNt5D5p334zzbABkj9iylSf/8a61b74z603XyC5qWFnOl0qupv8iSSyaQWEjo5NpsNTk9PMZ/PEYvFxKjhEvX06VP4fD61pbAGPBwO36kUj8ViWrS5ePDnOJ1OhEIhbDYbFItFmEwmRUXm8znS6bScOHQ6sJLaYDBIMKAgMhqNsL+/j8lkgkqloiprikuMbs3nc1QqFWy3Wzk56H4hl4GchvF4rJader2uO/e808x2GpPJpBaVxWKBFy9e4OTkRKwbxkluA525RPLxsKWLEQg+PoI9l8slhsOhHCrhcBidTkfxJrYhxeNxVKtV9Pt91Go1BAIBOSDIhRmNRnKdBAIB3aEnV4WiASMPXMQbjYZEN7ov2ODDSIXRaITb7VaDDiMVVqtVERLGsyhyMJYWDodRq9UkclCMYwU0wbusPJ9MJmK+kKeyXC7VmMR2MZ5XbE5itTodFrPZDFdXV4jFYjAYDIoTnZ+fw2w2IxAIIBQKoVAoiAFDXhFFl16vJ0fCu+++i1gshuVyKQYRxSc+v3a7XTXvvV4P6XQa6/VaohfjOhRzKGQEg0E1TJEbY7fb4ff7sVwu5bBzOBywWCzIZrPo9Xr6d7rNyAvyeDx6DigKknXEliOypyhgEKbdbDbx4MEDZLNZnJ2dwe/342tf+xqSySQePnyoSIzP59O/k7V1cnIiIC/FKD7vFBrIhSF8ezAYYH9/H/1+X0wel8ulZjubzSaweDKZxHa71fNOJ5PBYIDZbFbMijFFVs2zHY3g6Farhf39fazXa7hcLnFq6I6yWCxyrFE0YLU8xYrbEHaHw4HT01OJOQRc0ylHgZXvnVAohHK5LPcMn/tKpYK9vT2EQiFcXV3B4XCgUqkgl8sprsm4I0HVwWAQbrdbIhpr68mxqVarqlBnnfp2u4XL5UIymZQARucam99Go5Gu/bPZDPV6HXt7e+L4rFYriV0AdC00m804OjpCv99Hr9dDoVBANptFLBbTc0buUiKRUHU8HXmMxpITNhwOJfIwwuZwOO5EwXazm918+sc8Xn9P5GEeMMBgtXw8B/Qhzf3/6Ap/tZf5QF+z3m7wV975l2H4+ps/moPazW84Jq8X+NJnMP09X8L093wJs9/9JZgCARgs1o/70HbzIcz2jSfw/v3vYO+vPYfxl76DVan8cR/Sbj5h89JCDl0pBNmy6YZ3Xl0ulxwQ5Fnc5tMQpHp4eKjYEKNWZrNZTAzydigYkHFCuK/JZFLdOQDdfeZSZbfb0Wg0EI/H0W638ezZM7z22msCpHIJLRaLsFgsqmjmont4eAgAqjbvdruK7ZTLZRgMBrjdbt3Jns/nWowvLi4QDAYRCAS0iLIi9zbAmYLRZrPREsOYB0WQzWajmnIu2aFQCEajUcsx276ePn2KZDKJSCSCcrksEYrfn0yTyWSiqJvZbMbp6SkODw8VQSCYdLvdqqEMgGrKZ7OZ7nATxjsej9Hr9e7AZ28Db+PxOEqlEgBokUun0wiFQvB4PDg7O4PVaoXVar3DU2FT12azUSsWobPBYFCcFLqEbsdejo6O9Byx0tpms6HdbsPn8wkKS5bPZrNBs9mE0+lEOBxWxIPiGt0bTqdTFfJcbnkuAVCjGwDE43G0Wi014tBRRmgsn7Nfz0+KRCLigHQ6HTU2UdAKBoPodDoSB+h4sFqtODg4QLFYlLhwOz5CtwgdPYRod7tdHQtfe7KSyuWyoLB0PNy/f1/MITJJWCkeCASQy+VwcXGh+AsX43g8jtVqdQfg3Ol00G63BUw/ODhQbI+OLT7/4XBYogtbpeLxOAwGA9rttlqtHA4HEomEmuMYd7PZbHj+/Dms1ps/fni8dPs5HA40m005cFqtltxOjB49ffoUjx8/hsFg0OtM5wbFxna7jfl8jnA4LBC1yXRT+dput5FMJiVasJmObU1Go1HcMTYt3WZsXV1dqcmN0Ty6IPf29vDixQsEAgFUq1WkUqk74GS6iwgj5/uD7i82Yfn9fnQ6HQQCAUWQWG9Px+B8Psf19TVyuRzq9brip6lUCoVCQcIJo2mz2UxiYiKRENzZYrEIvuz3++XG4fnB+Kbf79d1NpvNynFTKpXgcrng9XoVnyK82mq14otf/CLeeOMNQanZmMXfYWS5Ea7Nhj+j0SgRkwyz3exmNz8ZY/7aG4h+Z6n/bRkC2V+sYN3rf4xH9cPPut7A/+G//Vc/0Nd86dd+Hge///mP6Ih28xuO0YTezz7E4MiFpdOIpdOIhceI7s/eg3H/g4lyu/nxne18jnW783Efxm4+ofPSQg4jNQDEmmGTjN1uv7MIM7JAXkooFBIwlsszI0YUbrj4+nw+RQVuV27TMs/a5mazib29PbkEyKfhzySD5Xf+zt+Jd955BwaDQa1PtxdcOkHooiGgl8s/Y0eM34xGI6zXa3g8HolYbBMib4KxKgomdHDw8xqNhtq7uJB98YtfxNXVlVxH2+0WsVjszuJFLo/dblckixwMRprYiFOv1xGNRuUcGY1GKBaLqq6eTCaCyW63W+zv76PdbiueFo1GYTAYcHl5qYWMkSgufXxcBNkajUacnJzA4/Hg2bNnWqh5PhBcajabUa1WJXQQ7MyKYbI++Nz2+31F8vr9vu6u09VENwNwI+wtl0vd2efS3Ov1JHD0+/07fKHlcolAIIBnz57prv3t9jFCWQeDgcSxZDKJfD4vZwUjHp1ORxXm+/v7qFar+hyfzwcAEhlPT09hMBh0LjHC4ff7YTabxUNiLTrFPrKZ6FYgXJdAZDJ8ACCdTsv1dXV1peeL0TVCeMmH6ff7iEajckwx+kTmFV8bwrzJYeLzRm4V4zAU71ghf9sJxBYtRtkIZaZIHAgE9HPr9bquPxTGACjaQ3Dz+fm5vm84HJZrh+eLx+NRmx0jhLVaDeVyGXt7e+h2u3eq1AGIacNIGd1HFATJUqJbiU45r9erdrRut4uDgwO9dhQpe70e9vf3YTKZUCgUYLfbMZ/PJRzzGkWx+7YLjoB5ikAej0euSTq3fD6fmEZ8vi4uLmA0GiWMrtdrxONx1Ov1O7B3o9EoJ9BoNEK/35dIS+aW0WgUbPw2xLjRaOjazYgWIct8PIzDbbdbRd8I9Kbg7Xa7dX5QjCU7h9FEgpyvr6/lZFssFigWi3jw4IEijayQ53P56NEjvV/dbrdisyaTSTcZKOjtZje7+ckZ+z/9DvZ+oYa9X6gh/XfzWF3mP+5D+lDm+M+/iYNf/MMorEY/8PPW2w0+/8a/jvj/aoDtcgdJ/rjG8NoDbL/Plja+F4Lhvb+DdrOb3fzkzksLOaPRSG4LciVarRYODg70xzQbhvx+P6rVKjweD4bDIV68eIHPfvazMBgMuLi4QCAQgNVqFSeGDp9ut4vlconPfOYzatFh9Ckej9+JmbBieTgcqhadiz3FADoReNzb7RZ+vx/r9Rq5XO5OhIEtVWTocKEEIJYE27NYW0vAscViwXQ6FQODS0ckEkGhUIDP55NDhzEdOmTIiyA7hQuy2+1GqVQSDDqdTiOfz2v5YiMT3RSMeW23W6zXa/FQCLcNBAJyPLjdbpydnWl5prhFaCnFjVqthlwuJ+4I+UMUoKrVKmKxmICiwHdbfBKJBCqVCgwGA2KxmBwT3W5XDgfyWDabDaxWK87OzhAMBjGdTuF0OlVTTrcPxScu1Z1ORw07FKsAqDmn3W6raSscDt9h7fB5ZlOZ1WqVW4zxPDpeisWiHFys3q5UKohEIgLwkoezWq3wmc98BpVKRSDbzWYjBxUXzHQ6jf39fQmJFKn43JKtRJGIUTLCtRlppFhHwYlxLjq7xuMx6vW6mpW8Xi8Gg8EdIYJsp3w+rxYxm82GUqmEWCyGzWajune+JuFwGAD0HpnNZri+vsbx8bGamih+8nHzcfI9zSgdK65dLhcuLi4UgxsMBlgsFlrAKQZQTCV7J5fLSVAJh8Ow2Wx6zzP6SM4NHRx8fSgWM55GCLHb7Ua/30c6nVZ8h68HY5T8z1qthvl8jqOjIxiNRtjtdhQKBTXTkRPESNDe3p6ENoPBgFKphIcPHypWmEwmBWcfDofweDyoVqu69tCFRvHv4OBAwHS73S5WFF1TjBvxvZdMJhEOh8XSCgaDahRj69l4PMZ6vZaTka6marWq62M4HFZN+Xg8VpOUyWSSQDifz+USOzo6Qrfb1fX7NkA8Ho9jsVjg/PwcyWQS7XYbBoNBbi+egwRH87wYj8eK4/K1X6/X4gddXFwglUrpfCAjiNBum80Gv98P4KZ1jufEbDaTQ7Lf/2Tfid/NbnbzwWa7WmF9don12SVW5crHfTgf2mxmMxz/8W/jDz/6WXzuWz+P//sgeuffX58v8dPv/h7c/6U/hMjPXWJVrX1MR7obAFj6bMD3QRst3EYY3rvZtJvd7OYnd15ayGF8yGg0wmq14sWLF3diQ4ziMDpBECp5N6VSCdvtFtlsVq4VDkGYuVxOTgj+MX672rnZbMLv96Ner6u1iYsGa5bJn6CLhVEks9ms5ZHCDVk1FJLIYuAf8/fv38d4PMZyuRQsmYtVPp+HxWLRIjAYDNDpdNR4YjQa0ev1cHBwgPl8jufPn+vuNUGekUgEJpMJ19fX4mDwLjXrm2/XAIfDYVgsFjx9+hSRSERRGC4bbrdbDIn79+9jvV6jXC7rtbi9BMfjcUGeAchBwEgTXU634zHkcIxGIySTSSSTSbx48UKi3Gq1UjU4xRouagSVsu2IghD5QfF4HMBNpI3iELkljE1x+VutVjg7O0MqlUIymYTdbsfbb7+NyWSCdDqtxbVarQq4S1fQYrFAIBDQ8zydTpHL5dBut/Vc08XDtjS/3y9Bik1ihG0z1jedTuU+6Xa7uH//vuJDjMolk8mbN53RKDGF4ojD4RDLhKIVgcgulwvz+VysH7pqotEorq6uEI/HsdlsxFyiYwG4iS1SWOHX8nkcDAaCRTMmRoGWcGifz6dmJwDw+XwYj8cSzQ4PD9VE5na7cX5+rtjacDjEZrMRG4rHyepxAAItn52dwefzIRQKqQ2MESq+T+mkikaj2Gw26HQ64hzxcTJ2w5p4uskYMWJDGB8H423z+Vzg6VKpBIfDIVGXbhoKaeRzke1EFyIB6YxsHR0dod1uo1Qqwel06jVutVo4Pj5W1G4+n+PNN99EOByGz+cTcDcWiwkiT8GXIt/+/r44Qb1eDx6PB5FIBKVSSdcfgoWdTieazSay2SwWi4Ug3XQ/soIegPhV5AFRKKcIGo/HxZJihbnVatV1iCBouty63S6sVisikQhqtZoicc1mE0ajEUajEel0Gk+fPtX7xOFwSKB0uVwAoChtPp+/U9NOsZUMND6fPEYACAaDWK1WEsjH4zGSySRqtZoA+nQ8LhYLRCIR3SSIRqNwu90v+2tyN7vZzW5+vGezxmY4RPR/8hx/84//a/jMf/ZH9PHH//wfg+2353Hwb76J7Wr1G3+v3exmN7vZzcc6Ly3k7O3t4a233lIkw2KxYDgc4jOf+QxarZaqpLkwut1uASjJauBiTlYD/2i/zUBg8xJrwi8uLhSbstlsuL6+xt7eHlwulxwxrP+ez+eIx+MSDwi2nM1mWsQ6nQ78fr8Wqul0KugooxtcCAaDgSz38/kctVoNR0dHWm4YabJYLALddjodFItF3Lt3D1dXV1qk9vb2FL/q9/uKWwBQ8w3vhFutViSTSfEpGAdgNI3NP1xuufRRdCJQlzXpjGhxadtut3IIHBwcqLGHyz9FMafTqZgO28cIYAaAi4sLJJNJhEIhPHv2TNEofh+2djHWNp/P8ejRI7z11ltiHg2HQ4l7JpNJrUUEOy8WC+zv7+Pp06d6HXw+H7xeL3q9np4fr9crFxDFIAojy+VSizxrq4Gb2EQul8Pp6SlSqZTcFvP5XI+XjBq6ViqVioQnnhMul0tiD88tigEU0BwOh/hF2+1WbJZisYhUKqWmn+fPn8s5Q1cDzyG2B7HangBuQnb5ue+88w4SicQdSLTP50OpVFL9PN0HjDzOZjPs7+9rKWZ0hwICobztdluuHQCoVqswmUwSGQ4ODlSPzkjSbDbD8fGxYLZ0bVFAoLuDzxkff6/Xw3g8VmMXYcBcsPn6k7XCFrTtdgu32y0GzyuvvIJGo6HHxRia0+mU8+62kEtgNIW5TCYDq9V6J/LXbDYRiURgNpsljBEQTNdLr9dDvV6HyWQSR4csGH6fwWAAv9+PeDyOcrmMYrGo15kuN7b9Wa1W7O/vw2g04q233tK1iW4VulroEORjabVaYvHwukjRArgR0yKRiGJnrJq/7SDk9zUajQLfU+Cez+e6XrfbbdjtdkWiTCaTmFur1UqAabobjUajYnPr9RqvvfYaisWiopAU7MjG4s/2eDy4vr5Gu92Wy47tVRQoGaWrVCrw+XwSzrbbrW5EAJCDyOVyIZVKodlsyuE2mUwkVO5mN7vZze0xx2Mwp5L6+KQ1W1n+6beR/E9+RR++//pXP+5D2s1ufiLGFAh8z8dudvObmZcWcmazmf54n81mSCQSgp2SsVIsFmGz2bTwOJ1ObLdbxTAAqImFbAvW7AKQ7Z5OCbPZrDgFa6p595QLGaGuFB7oXhmPx7Db7XA6nYjH46hUKqqtXq/XGI1G+PKXvyxuCFkfz58/x1e/+lX4fD6cnp6qSYssCgJKCXcejUZot9uqtyUEt9Pp6I4xnUG5XE7133QvkRViMpkwHA4FdGbLFO/G83sw3tHpdATBdbvdGI1GiqDRWUFmBRdc1iFTePJ6vbi8vLyz/NCZ0e/3JQww/kJXUywWw9nZGWw2G/b391Gv1xVTikajqNfrmM/ncLvdyGQycljQrcPI3Xq9RiqVwosXL1AsFvW5hPKSs0QXDZ1YrCfnEk13xWazUQvOs2fP8FM/9VMCVAcCAUFq6RhhZTyZIIznGQwG8UhY48xj4CLb7XYFTnW73ahWq/B6vTAajSgWi1itVmrSisVicLlc6Pf7cLlciEQiWC6X6Pf7WrgpHKRSKdTrdTnH6Fyg64eAYro7COT2eDyCQjN2xvYpwmpTqZQqxQmV5dfQ/UVHC5viWMNNRwab2GKxmGKOy+VS8bZarSa2TDKZhNlsVqyQ4sWrr76Ky8tLtRtNJhMBZukIo5OPrBou0xTjGNEib4kAcooDbBKbTqcC5rLaezAYIJFI6Lwl4JjtW8lkEh6PB/V6HS6XC71eD/P5HCcnJyiXy5jNZnoMdrtd72cyh3h+AEAul8NgMFDL1GQyQa1WUyyKgG8KY3Rhvfrqq3Jl0VlEfgtdVXzvuVwuNBoNNBoN1ZKHQiG5Y2q1GrxeL8xms6J+Z2dniEajsNlsAgGTj8O2L0ZAKZSMx2Ndl27HkADoGCwWi657jUZDrsHpdCqXD7lFvB6TvUVhlPwtgrbJLKKY3W639brxd4rf74fP50O73RaEnY4jRuBcLpdYU/v7+3C73ZhOp5hOpxK5+Huk3++LN3XbPbqb3ezmJ2PMuQxM4dD3/XfTwxNc/8FDXP2hPX30fs/jD7VNaP3Tr2H90699aN9vN5+ssZX6MGze/9+cjSW2y51r6pM45v0cer/9Hro/e+vjd96D6fjg4z603XwC56WFHNbxXlxc4Pj4WG4aLhjX19fw+/1qnup0OpjNZshkMloS2GrFiAeXV0JE7Xa7QL10G9D5wzvidDwwvjUcDhEIBCT+kD0zGAwEj2U0xev1ChY7Ho/RaDTg9XrFw2BMgEt2KpUSVJb8m9VqhXg8jl6vp7vhZDZQRGm1WmoGcjgc4sAwykEmjsfjQavVwt7enmrCyfeYTqfiRbDJiE05bNEiCJjH4HQ60Wq14HK5dLc9Ho9jNBrBarVitVphb29Pi1m5XEYgEEA8Hsf5+Tl6vR6Ojo7w7Nkz8VPIDiLYeLVaKRrC+m+Hw4FHjx5hNpvJVRGPx2G1WlGv19XyRJFvb28Po9EIi8VCr/fBwYEgtUajUc8XYx10ZbTbbdWW83PK5bLiLlzY/X6/olF0e7B6OpVKYblcwmg04vnz56rKBm6iQ+VyGbFYDNVqVS4OxtoCgQAGg4GiX4vFAqPRSDXMy+US6XRa9coGg0EOL7fbjWazKbGJkSa26nD5jsVi6Pf7qNfrEk8PDg5gMBjuREt4bjJSxUgLgdcULAmAZuSEwgzjWD6fT3EbOrtu85/YIkY33mAwQD6fRy6XEzj4NjNmMpkgkUigVCrpWMbjMcLhMAKBAL75zW/qfccIE3kwfF3ZcsX3DMVBg8Gg9xndFfz+bGAi8DyTySCfz0sU8/v9qlMno4YiCYVlfs9SqSS3H0XA09NTfPGLX8RoNFK9ucVikSC93W4xGAwkIjMy6nQ6VVdO3gsFHDai0amYTCYlNNL9FgwG0W63AUCCSCgUwmw2k2AEQIBqft5iscBqtcKjR48kMA4GA72uhAqTVcMIGgWfy8tLcWj4PBMkz+ss2UXj8Vi153x/GAwG2Gw2uN1uCb2MODGKS6GN8Gm+H4xGo1xvFNn4OtJRxHOPP9NqteLq6kqcMgprfJ4LhQL29/cVAWM9OR2GJpNJ8S/ydq6vr+XU3M1udvOTMaZYFKWfy6D68/cw+x9/6Xv//Wgfxd8VxubXNZL3D4ww2H94AK05k0bhL3wVF/+GGRf/hhmFv/BVmDPpH/r77uaTNesX5/C/3YFxub3z4awvYf36kx2I+hM45kwa/c/Hsf31eCMD0H81AvN+7mM5rt18cuelhZxisQi/349EIoFarSaux+2IDyM5FCPsdrsqYvnH93K5xHK5FGuCdvz9/X1st1tFJBwOhxgZvEPMdpTlcikHRCAQwHg81h3bi4sLHB0dwev1ikPDxYhOi+l0ikgkgn6/j8PDQ9RqNWQyGRQKBUSjUdXhMrbCSEg0GlW8hC0/Xq9XMSlGuihS7e/vKyKwXq8lphB+ulwudeeaCwmdIXQREF5M/g0bY+x2u5ppGMfiktTpdASfBoD9/X1cXl7i6OgIxWJRrBWfzwebzYZvfvObuH//vqq0v/CFL8j94na7FQ/hAsnYHJc+tvAEAgG0Wi2YzWb4fD61N7VaLVX+krdD9wprpyuVisC5ZBxxiabIx1gFH6PX65UzhdGfwWAg3gnFBQJ92e7DWNNms1Fcw2q1wuVyoVAoqBKekFO2fBHYTBFiPp/LuUAIcjwex3A4VNtTPB7HYDAQyNput6Pb7eocD4VCeg0Z5xiPx+IvOZ1O+P1+XF5eyn3E2ntGR0ajkWrA+ZjdbrdA17edE263WxXcPOcYZfT7/apov91ENx6P9V574403kEgk4PP5MBwOEYvFUKlU4HQ60e12dS2w2+06V+bzOVarFfL5vES+VCqlFqRer4doNAq73Y6rqyuYzWY5I4xGI7rdLmKxGILBINbrNZ4+fYrJZIKHDx/KvUP4MyNDDx48QL1ex/3799HtdrFer1EoFNButxEKhZBIJODxePCtb31LgOJgMAi73Y7Ly0u9NhRLAoGAXFUWiwXValWvw6uvvqoKeooUFKsJ6KbDLRqNCpDMx01BjCwvCkkABIh2uVxynrHdq1Kp4NGjR4o/Uiyh6AzgTh08RV2KvpFIBJVKBUajEalUSu1OFMhPTk7E3Dk4OEC73dbX8vvTFXl5eYlwOCweE88vtm5REGJUy2q13hEgGcdjFPC2E4YcKb7PKbLdZuDwOSP7i7+ngJuKeEZqHQ6HhDM6ooxGIwqFgpyMjABTpOL32c1udvOTMZV//QhLDzAPANPQ9wJlt04b1o4f3c8//WMZzPfmMDjWMDjWmO/N8fxPp3d37H8CZ/30FO6/9/qdD/M/fwOb99zTu/lkzdZhw9r6/gTrjcWArdP+ER/Rbj7p89JCjtlsRiqVQq/Xk7uADgO6cwKBALbbLa6urpDL5STG1Ot1GAwGGAw3Jy/hyISmUvQhH+e2U4ULORtYKMg8fPgQ7XZbUF1WTsfjcVSrVRgMBkSjUTVhsUmLlcRsnqJzhosj4yuNRkPCEe/MUzAiFHU0GqHVasHj8WiRZXX0ZrNBs9lUdMHn82nRImg3EonIkcKqYbpRACimweWPFb+j0Qi9Xk+LCJ/Tq6srOTEoulAoYr0w3VKMWlUqFWSzWbFk6Awg34TNVDabTfEdwnDJwrDb7XdiZzxGOodYu8274Kwhp2hAbgbZM/1+H+fn5wCAcDiMZrOpmnKKcAAUieBxUVjjwk84Ls8hCk18HtmeZDabUSqVBEWl82o2m8HpdCIUCokXwqr0WCwmt0q5XFadMR1JdOSQpcL6bZvNJugxz+XhcKiYDiuY2cA0m83k+BiPx4jFYnIjUBhhJI5iU7/fFztkb28PNpsNgUAAvV4PrVYL4/EYiURCAiHFxdsOGgpWrLynE4OuOjJxTCYTIpGIuCoU24bDoRrBfD4fotEoPB4PTk9PJXiSUcI4VKvV0nNDaDndaay7ns/nODw8xHw+x4sXL9SMZTAYxGCxWq3I5/OKo/F7ejweCZx0x/B6QIcS40C3G6oY32KD3GazUTyNIhVdiYRD81pI9g7ryW+D0/1+P4rFIiqViurbDQYD7t27p5puXjN5nlD8LhQKcvrxdR8Oh4hEImKVkcEUDoeRTqcluNHpQyGYriLGKPn/kR3FaB7FvkKhoKan27FMtrZR4LLZbPrabrcr2LLb7Uar1VJ8itFGxgDZZsbmLIq1RqMR5XJZ8bJYLAan04nz83MxcObzuRhhwI3Iw7ga3VdOp1OuT5/Ppxgw44h0I1EoymazH+y36m52s5tP9tzaswzbj/7Hb99nz9v6l2j+1thHfzC7+fhnu737sZvd7GY3781LCzkE2rLGeTAYSHwIh8OKLw2HQwE5nz9/rtrjQCAgECb/4GbD02AwULRpsVhgPB6r9pcLei6XUzW22WxW3azJZMK3v/1t8TSm06ngpKVSSfEpLnmEJROmWiqVEI1GsV6vdaeZLUkUhAglpZOFC5jRaEQoFLrjniAfhK1CzWYT5XIZtVoN2WwWgUAAkUgE6/UaxWIR4/FYghJjYxSOyJap1+taLhnN4p3s0WiEfr+PTqeDBw8eyPVCUCidRE6nE4VCAYvFAq+++ioqlYq4Jly2l8ulmD+8S57JZOByufD2228rNmMymRCLxeQyYlTLbDYrvjQajRTNYsU7hbDr62u99nx9fD4fTCYTzs/P4fF4kEqlVNXt8Xjg9XqRTCYxnU5RLBYlejEm0W63sbe3pwpmAqApQFBsJOMJuKkpp8BB0e42r8Rut6v6mW1GhEeTJwJAUZlf+qVfUuQtnU6jVqvpc/r9vhghZNpQMFwsFkin0xgOh5hOp0in05jP52i1Wqpv5utgNpsxmUzQbrd1PtVqNVSrVYRCIZjNZozHYzUn8bmx2+2KplD0Y8VyNptFpVIRcLbX60noarfbil8BEA/IYDDcicHwa91utyq86UDxeDzo9XoYjUbiDnU6HcGw+X3IgGGMstPpiJPFSnC3242rqyu9TygcrNdrdLtdBINBLJdLAb4TiQTK5bIa7jKZjBrYLBaLrhsE7lKcm81mArhTlGFzntfrRSQSUXMTHUt0oITDYRQKBYxGI/h8PjmUbruoisWiXGDD4VAxPAK7k8kkFouFHI0mk0nXQzoLs9msRFAAcoPRiTUajfScUuRm5JKRQTqH6Njhz2SjHqN5LpcL5XJZ7Wts7XvnnXdwdHSk84WsIoo5jPMxFre/v48nT57o/GTLFCO2jBvSiUhB0OVyiTfl8/nucM08Hg+urq4AQOcEo5btdluQaafTiXw+r5sSq9UKl5eXmE6nyGazig9T/GPk9e233/4hfsXuZje7+aRN4m+8hfQ/G8NR3yL499/9nn/fPj2H//R74SWWEYAdHH03u9nNbnbzEc1LCzmEv/LOLQA1oCwWCzQaDd01pXOGrUm8s0xmAiGjy+USoVAI4XAYvV5PCy6XMQJzTSYTWq2WXABmsxnr9VrtRvv7+4IxM7ZCN4HH48H5+blaZbxer5q1Op0OGo0Gjo6OVBfOCM9kMtHdZovFgkgkItYL747bbDY1LTGq02g0kEwm1YTl9XrFXSiXywAgCC7dKF6vV8/r7QWFteHkxhwdHaFarWop5s8kDJdVv4vFArlcTnEOigcnJyew2WwC6FLkYf261WpFLBbTa9tsNsX9YDyONeeBQEBVxoQtk2vRarW01AI3zUaMoJCdxNYlh8MhuC/r2TudjiJLdCI5HA7k83lEo1E5NfhahMNhtFotVKtV/NRP/RTeeustfOlLX0Kj0ZAzhhyjhw8fitsTjUaxWCwkcFxcXIiXxGaccrmMcDgsBpTVahWfBoD4TbVaTcshK41Xq5WcWKlUCpVKBb1eT0suxTiec+R5VCoVRKNRmM1mOBwOBINBNBoNbLdbxX7C4bDcRIyY1Wo1xcjocLjdwMSfSwcEF3jWyTP6SMEUuGFj0SVFF1U4HIbf70etVtN5x8gW3TN0Z9G9d3FxoQYvirPxeFzOGDY70S3icDgUM9vf30e3270D7r3tJCInirXk2+0W9XpdziJCcelU4flMJgyhv81mE7FYDKvVSk6cbrer54OQaT7XFotF0bztdot79+7h7OwM1WpVMTeynTqdDg4ODjCZTFAqlfD48WOJsLlcTs5Bsl7ICLt3757EPsYDC4UCHjx4gH6/r/OO7BzgpuWKLi2ygNLptNq5zGazrrOlUkktanycwWDwTiywUqlgu93i4OAAnU5H7xmfz4fDw0N87WtfQyaTQSKRQLfbRa1WUyuVw+GAyWQSH4fMHMZXt9utRCG3242DgwNcXFzIcej3+wVbz2az+j1DgYei1u1YIUVLvoYUchuNhqJgq9VK7WJsKDQYDBKiQqEQ3nnnHQwGA6RSqR/29+xudrObT9BsxmMYvvEWwv/FN7B574ba7dmuVvA96SH05Luija0LpP/eNTbvOfk+7NlOTAi9O/qRfO/d7GY3u9nNJ3NeWsjh3XW6LLi4bbdb1dauViuBLh0Oh+7ep1IpXF1dibPCJbVSqQC4iWlwIaIbptls6k40XRKLxUI8FTae8E73bDbDw4cPsVqt0Gg0xHAhGJYWerPZjEKhgPV6jUgkcge8vFqt5EThkrDdbhXrIUSXAguXUj5uu92uuM7t+BN5Ly6XSyLXfD7H9fW1Wr3Y+GO1WiWIcAGfTCaYzWbo9Xq4d++e3AetVgvxeFyV1uQLBQIBCQ2LxUINX4T/clnm46bLymQyoVgsaolmNGIymSgOxTvdZ2dnckU0m01Eo1EBqBltYrsOcCN4DAYDZDIZJJNJOSKy2SxqtZp+JqNJrFsuFouIRCIC/4ZCIfR6PSyXSzlpms0mHj58CL/fj1/6pV/S88B6ab6eXOTdbrdasQiLdbvdACBh8vLyEiaTSUBej8ej1qlKpYJYLIarqyu43W4t0uTJUBSLRCISeqxWq85h8pf4erB5jaIHK8H53qBAwiXUZrPdaWoiR4gLOF0x2+0WvV4PDx8+RK1WQ7PZRDgcFlSY9dBOpxOj0UhOOAqvZLZQPOVSPhgMsF6vBTLP5XJoNptiNd3+Gd1uF81mE5lMRo+33+8jFouJd8Ql2mg0IhgMol6vSyyz2WwSvxhfM5vNOD09FYSWwtlkMsFwOJS7zev1ip8DQGKUz+dTOxnFI7JbSqWS3FabzUaNY8PhEI1GAycnJ3p96b6jcPXkyRO1Pj179kwsILqZGo0GLi8v9b4JhW4aURjtYosYXVDBYFDXNjqCttutrltsr2s0Gjg8PLwDrL4tmPO6y9gm4dAEDJP3s9lsFHvj9dDlcgnwTbC5wWBAOp3GYrEQ/L7X66FSqUh8yefzODw8VFRqvV7fAUB7vV4UCgW1lRG2nM/nkclkJFRSiGLE0O/3y9V5m3VGpyYFYUYzGZlllNfn86FUKumx833PmwNutxuVSkU/Kx6P7xg5u9nNbr5nNu8+h+sffwf7f6uE/b9VQuLvnGFVKn8o3/vo7wyBsRnb9c3fT9u5EbaWCdtvvfOhfP/d7GY3H89srktwVRfAr0/IbQFHY4ntxfXLfzODAUaXC6Pf91No/eGvoPWHv4Lhz38ZRpcLMLw/h2c3n755aSGHSy9BmwRGkkdwdHQk9ksoFFJNLBkUPp8PwWAQ1WoVs9kMbrcbLpcL5+fn4qPw66bTKU5OTlCr1dDtdsXbmc1muLi4QDQaxf3791Gr1XR31ufz4Y033tCCuFqtEIlEYDQaJZqQ7/Ho0SM0Go077BkyeMLhsJw+wM2SSPs/FzcyV2w2m6pyuaSwnpeNQHTXbDYbWCwWNJtNfU40GsVgMMDTp0/FfXnw4AGq1arg0svlUmDXUqmEs7MzGAwGPH78GL1eD6vVSjDYSCSiiAo5KAAUUfrc5z6HXq+nmBeBtQSoOp1OuW7I1gmFQqp1r9VqiunwLjWhoe12W5XcjIywcYjuHd7JH4/HqoG+vLyEx+NBLBbTc+T1etFqtdDpdPDw4UOJUTx2Vpw/evRIEZ7lcolWqwW/34+DgwO5jigosSWKbUzBYBDdbldiG+NldJSkUik0m005uXq9nqJzTqdTsG3G5xgLYeUzX5N+v6/2IAByCPDnkt0RiUTuMJbG47GqkVerFdLptNwB5Nfk83n0ej2Uy2UcHR3J0WYymSSikKdCZxfZI1yI2S7ECnE6MW6/Vrc5Oel0Ws6Y4XCIdDqNZ8+eKeJCZtD9+/clcDIGNplMFL+hONnv9+Vyut38dXp6ilgspmjO3t4eJpMJms0mptMpDg4OFMecTCbYbrdqvCIvhq6u+XwuR+FoNEI0GkWv10Ov10MmkxELha64XC4Hq9UKp9OJJ0+eyLF1//59NJtN9Pt9CWYA1OLk9Xqx2WzkNiLDi8KVzWZDNBpFPB7HxcWFGvcogqfTaYlWFK/J8Nrb29M1bTgcotvtolQqwW634969exIfksmknH/b7VawdsYwY7HYndgjcFMTz0gpnZBkLtntdokcwI2rablcotFoyC3Jcw2AhG0KcgAUp9tutygWi3C5XIqs3W6ne/LkieJMPHcp6B0cHAiyT0iyw+FAp9NBJBJRpXm9Xhd0mTFIstxsNhv6/b7O4UqlIsAxRavbjWEUTMlY2s1udrOb27NdLrDKF7DKF7BuNj+87/vtd3H8R19H6v9tgvfXbLj3X86Q+w9+9UP7/rvZzW4+ntnO5zD999+B53oKd2EG82wLd2EGT2EGy9fe/EAQa3Myges/+Vl075swjRowjRrQOzbi+k9+Fuuf/tyP7kHs5sdqXlrImc/ncDgcEh9sNpuEEnIiLBaL7t4vl8s7TgPWPZvNZjQaDTWIUOQhZJbQTPIz7HY7Wq0WvF6vapMXiwXK5bKgrxQ/otEohsMhRqORRA22ank8HkFY2WZF98rFxQWcTqc4PnQ+nJ+fi9XDu8CsPqf7gBwKVh+zDcpkMqHX6yneRUfQdruVO4TxDsajGB+KRCJq4WELFO92j0YjiQyM3JCxs1gs8OjRIzVzmc1mADcMF5vNhqurK2SzWbUwMQ7FJYjiDaMlR0dHqj1utVoSenj3nP+d4FdGZGazmcCrFJr4et8Gs9Lp5HA4BLX1eDzodDqqO14sFuj3+4hGo3KE5fN5wZvpJCgUCvjc5z6nqA3FAjJB+LMGg4G4MsvlUkwNAoopcPC/X19fCzI7n89hNBoFxGWUj4wTwmc9Hg8WiwUGg4HEv1gspqga3WqMvS0WCy2ujBgxlmc2m1XzzkakxWIhngvZQLPZTBBnCjZcTKfTKbrdLuLxuBZwuuYodtFhRgcWF1y65TabjWDat6vGCftlBIouO4JjLRYLDg4O0O12Fa9kHIYuNgCqnmeki1Df9XqNcrms9+R8PlfFdr1eV7yRghiFz8lkIvZVLBaTYDsej/V6J5NJvHjxAna7XQ1ebIdj9OzevXs4Pz+HzWaTwEaBidc1Co3AdwHW4/EYmUxGAgbb+gKBgADIx8fHqNVqEjK63a4cfWy6owuuWq3C5XLJKcJ/s1qtaowCoMcwn88RDofVJFYsFsWeIZCaQsl8PlckNRwOSzSZTqe6ntJBRKdfr9dDp9NBNptFr9dDKBTCer2W64WOrFqtJm7QfD7XdZACLB2Ok8kEh4eHACAu1mQyufMcA1DUii1e7XZbYGq6EvP5PAAoGkZhfjweI5vNyhVJRxmdoMvlUrDj+XyO6XQKk8mk13Y3u9nNbj6y2W7h+nvfROwv/8qNE2cHud3Nbj4ds93C8CtvwfjLb8L7jWsYf/lNGL7+JrD5YH9rdH9r9g6YXWMA6l+0/2hcOUYTDJ9/hKu/+BV9FP6Dr8Lw3r65m49+XlrI4Z3mZrOJo6MjGAwGuWsAqO0kFArhxYsXAqRGo1HVxRKKGwwG0Ww2Ua1W5eRhZTY5BcDNHXKKJRQKCKg1Go3IZDJYLBZ34JlscaLLgM4ZcmboMmALlc1mQyqVkqOES10oFMLe3h6+853voN1uI5fLaRHkcku+Rz6fV000W3GcTifi8TiePXuGSCQCu90uWLLRaITFYkG5XEY0GhXjhW07dMcwAmU2m9XKk81m1R5Ed8hgMIDH44HD4cDp6SlcLhcMBoNcPWwJAyCHSr/fx8HBgfgRXLq8Xq/YJXRikAfE15C8klAopFYcslLorPF6vaqWZ2sXhYJer4d4PC4GCRdbLoqxWEwLI2NhXFy5/DOGQz6K3+/Hs2fPFFviop3JZNDpdBRTCwQCii4xcsM79W63G7PZTMDkUCgklxEXRwCqfuZjIp+D4sX5+TkODg7UAMbnm+cBF3/+p8vl0vuCrUaM2tABwcYdwqPr9TrS6bR4S3xvjcdjFItFiaCdTgc2m03g506no1gV3VcUNyiUkSlE0Ya10wAkXLLhjAs+m8YePnwogZZRRooUFMsMBgOcTiecTqeiM8BNCxmb5QjsJnT37OxMIGmHw4Fmswm/34/T01M51whATyQScLvdePHiBUKhEAqFAo6Pj9FsNuHxeHRO2u122Gw2iVlsJOP1YrPZSICaTqdotVqIRqPw+/1IJBJoNBqo1Wp3vjefo729PTSbTYkso9FITCRykyqVChKJhBrieG7E43G157F5i7FFi8WCTCaDZ8+ewWAw4OTkBJPJRMwexlGBG/H1C1/4gtrIKKQaDAY99mq1Co/Hg81mg0QigclkIuHq/v37Yjfl83kMh0OJ4mzTKxaLYutQeLxdU350dITxeIxqtaradbfbrUp2CvjdbhfRaFSA7kajoVaqTqejayevAbzmWSwWDAYDcbey2Syy2SzG4zFKpZIaC3n9Y9Mdbz4sFgskk0ndoKCjiAI1AD2fu9nNbnbzvmM0wfTw5LsfsejHfUS72c1uPgGzqv7mo9uj9Euv8D/0GO12VP/0V9H9xwcY/UdTnHw1r4/9n86j8w/30fy3vwLTo3sf2THt5mY+ECPHaDTC6XQKxgpAToROpwO73a6mmF6vB7PZjOFwiGg0qogRIbVmsxl+v1/uHHJfaPe32WxqOkomk+j3+wKTVqtV2O12bN+7Q8G7plxWzGaz2nTIJbl9x/52zGWz2ehYI5EIrq+v1eREwYgNV4lEAqPRSK0/Ho9Hwg9wE7FotVpYLpdwuVwYDAZ49dVXtWQzKkM2A+8Ic9nm8nibURKNRuX04DEPBgNEIhG1/9yu8GYdMR0wXJQpfhBMS7cIYw6NRkPPAR0mz549U6zNYrHoeeLz2rxlJWa0q1KpyGXDumXWhtP1QEcGG4sikQiKxaLEjXq9joODA8V0GNljlCYQCMjxZLFY5OKIRqNyarESmhFAuqQASNiiUFYoFNQuxfgdBafDw0PFj5xOp8SBx48f45/9s3+mpig6qxh1IUR2MpnA7XajVqshk8kIrLrdblV3TkGk2WzK5UBxgR90FI1GI/FCwuEw2u22vgedMxQmTSYTUqkUOp2OatdDoRCWy6WAxxQu6Oyy2Wxy91BctdvtWK/XCIVCqkKPRqOYz+dot9twuVxqcWo2m3puGVfbbDaIx+N6b/OcZWPZ3t6empfS6bSag7bbrYRKvo8pPu3v72M8HqsBymw2S+Rk1Coej+PNN99EOp0Wq4WPhQB1n8+HXq+na5Tf75dwwKgnOUYUFZ89e4bBYAAAAvweHBzonKSrcLlcik/k8/kwm810XaG7ixEzcnvYxEbXHN2N4XBYkbZms4nPf/7z8Hg8ePfdd+HxeO7wy8gCogBD6C/dOh6PB6VSSRFXXj95DvNcY9Ma28Jms5la0A4ODlCv1/H48WOJaIywmc1m7O3t4fz8XC4+4EaYT6VSKBQK2Gw2CAaDqhJn+12n08He3p6cYbw+GQwGxc6sVivG4zEAqPXP5XJJlGy1Wjg6OhI/jecd31Ns/qOAVSgUdM3m7yyPxyOuEG8s7GY3u9nN+830d38ez/6MB8//7M3H1b99BNN7cfnd7GY3u/kkz/K3fwHXf/o1ZH/3FaKuEfz26Z1/Nxq2iLpGSP58HtW/aMDmX9rFuj7KeWkh5/adz3g8fkd8sFqtAG5YLIwmhMNhrFYrATdZB+5yuZBKpVR1TYFhtVoJoEyXSzAY1JLJZcJisYitwT/wU6mU4lfdbldQT7bAEHDa6/XET2BbD90J5P6EQiGBWxmLooBzcXGBk5MTcTy4WGw2G7EaGBUAbvg6p6en8Pl8qidm9KZcLiv2AXz3TjGXODIqut2u+EFkiFitVjFWjEYjTk5OcHl5qepvVhQT3NztdgWVJf/C5XJhuVyi3W4jk8kAuFmwLRaLhJxMJqO2JC5SBoMBgUAA4XAY5+fnEpo6nY4iGCaTCc1mU84cNhhNp1M9X3RcdDqdO4JDLpdDvV4XC8nj8cg5wAWLYpLf7xdDhtwOCgysQGY8iI4YsmcYl6Djg8034/EYLpfrTjU4HRT5fF4L3ng8Ri6XU9MUn2sCfTudjtqRgBuh6/nz54oj8jnp9XriMVFg4eNbLBYYjUZIp9P6vrddNQRoWywWdLtdOJ1OxRNLpZIawoLBIGKxmFw1ZPzwmMkBIruI7ig+fsKE6QqiY4Lxq06nI+7IZrNRO12n0xHQlw6mzWajKB5b4Wq1msS55XKpJi86JMhAIYib8bvZbIZQKCSHCCN89XodLpdLkR+KmwTeAjfcFooVdF4wVpjJZMT4SiaTuk6w5trv98v5QvYMOVoU1Rgb8vv9d6KOdrsdlUoF6XQaV1dXqpWnK+f6+lpNfuRKHR0dodfr6f1LYZStZRSZeF3ke4iuG4qd7XZbIGc2RvE17Pf7uoYybpXP58WaoZjr9XoRi8VwenqKw8NDdDod8WXW67UEKrvdDo/HgydPnoipNR6PxSii26larSIYDKqmnWBlui1vN/TRRUUYdqVSEVus3W6rJp6vC5lRwI0AzkY6utzo0iGInOI0rwP8/dL8ENkXu9nNbj5ds/idX0Txd23vpBjm+3Nc/IkjmN5ziO/mNzcmrxetP/wVNP7IV9H4I1/F8mc+Dxh3Dsnd7OajGoPNhspvseLgf3T1Up+f8vXR/jMTbL/y2R/xke2G89JCDtud6FKhRZ93R3mn+bY4QGGCy14ul0O1WgVws7SmUikEAgGUSiW1hfDrxuMxxuOxXBv5fF7LH//YByCL/f7+PobDoRZ6Ln+MdrBphs0wFJv6/b4iV6w7J/SWYk+5XEYwGITD4VCU6ODgAPP5XJXldEuQOdPpdATVLZfLaDabyOVyAmvSzfPixQt4PB4JHqzDrVQqcDqd4pnwzn0ymYTVasVsNlPU6cmTJ6oH52LOtqjr62tFIAj+ZAzKZrOpYYYLM2HAjDA5nU415zDSQ/GEbBZChrno8/mlI2Bvb0/V4QQCz+dz8Wi4pB4cHODq6gp7e3sIBAIoFototVqql6dwRQ4KY0YU3SKRiHgYXKAZuxkMBmg0GshkMhIWybuJxWIYj8fi8gAQqJUi0Ha7xd7enkC0BE3TncS4TjAY1LlOGDddIADEUeJzcHJyImcRXSeE2NI9MBwO79SBExJLgS4SiSiuyIpnsnZ4rvX7ffR6PcRiMXFBCJxlrIrV55FIBO12G/v7++j3+yiXy/B6vXLAsZWMzJpUKoVnz54pfnbbjXJ1dYXNZoNsNnuH/UL2FR8jwb6M4fH8K5VKanByuVy6ftD1RY4OXTN0txDUzfPl+voaR0dHilDV63U53tigRGGCDjByUuj2oNOIwg8jV4FAAP1+H91uF0dHR3A4HAJ8P3v2TBEgHjNjfGSK2Ww2Oab29vZwenoqIYwi2nK5RDqdFr+Jzqhmsyk3y2AwQDgc1vXKZDJJ7KHjZn9/X7wlPi5GQY+Pj/X+dDqddyraWW+ez+fh9XrVHkhwOtvYXC4XRqMRut0uJpOJfieUSiVEIhHs7e1hNBrh6uoKVqtV5//+/j7a7bau+QRyM4bJlq3RaKQIJhvbrq+vEQ6HxV+LRCKKshEeTo4Tzw266IxGI8LhMHq9nrhNVqsVh4eHOD091bVsN7vZzW5+/Rg9HrQfWWCwfe81YpleoPIHX/kYjurTM62fe4Rp1IB5CJiHgNpXbJj/jtd2jTy72Q0A7/X3Z+p4CpsfnqtlNKH8xz6Po3/55UQcTtI7QPvPTXfv049oXlrIYTMOF8ZkMolWq4XRaITtdotcLod+vy+gZj6fF5jYbDZrOSOIM5FIoFqtio3gdDrlVOGdVC5mbrdbdbOMZfEuvNvtRqfTETCTMMxmsykRgossG1L8fr84EbVaDR6PRwstYa3JZFJQ5EAggGq1KrGCzSkejwehUAinp6fIZrPo9/uCtjISQieI1WrVnXoAapK5XaVLJwZjZ263W8KJ1WpVrMzr9WI4HKLT6cDj8QgqbLPZ9Hq0223MZjOBWePxuJqcQqEQttutYkiEDbPxiLDj+XwudwbdIRRf+FoajUZUq1Usl0skk0k5ScjzCAaDimQxEsTFdzqdShSg8ERhje4S3llnXfB8PgcAtZhx8R+NRqqPD4fDqFQqquV2Op2KEHU6HcRiMfGFWNEdCAR0DPP5HAaDQVGi+XyuRZiuqEAgIKguz1G6SRjdC4VCCAaDuLi4gNVqhdfrVTyMj4dRPEYMGR0jiygQCKDRaMgt4Ha7dR5Q0HO5XALqLhYLiRYUMelAMBgMclv5/X41ebEyfTqdSoBKpVJq7GH0bjAY4P79+ygUCnKOLRaLOwBiikN0cLHhjfXxFG2GwyE2m80dFwWjdDyH+PqSO2Wz2eQQIU+mUCjIRcLY43w+RyaTkUA5m82Qy+VQLpflHjEYDHr8FK8ajQb29vbk5rv9nJnNZhSLRXz5y1/W46cYSMce441s+GN8iKIdhXDCtn0+n2DZyWRSjii6owhW588ZjUawWCwYjUa4f/8+ut2uQMUUM4Ab0Z3XmkKhAJ/Ph3g8DqfTKVZRv99HrVbDK6+8ooYmXqPq9ToKhQKy2ayuw4zG+v1+Qa8nkwk8Ho94RYxxEjpMIZpxM77uBF4z8kXGj8PhgM1m03ucvCzCkSke2e12OBwOtdDlcjksl0vMZjM5myji8zpBB91wOAQA+Hw+sa6Gw6Fq3Qm0L5fLiMfjiMViElF3s5vd7Ob2GCMhDB8tPu7D+PTO++yBzVctgOGjY4Ps5gOMwXDjmNot8B/JeP7JWwi82AC3deQN4DvfwPeP3v6hv7/R5UT4d5R/U18bdE7R/QNf/qGPYTe/8bz01ZBMDd6dZxyKAFM2KvX7fblNeHd8vV7j8vISR0dHMJlM6Ha7aulxuVyKZrHWN5lMiu/CSuR79+4pgtJut1GtVpHJZOR6cLvdyOVyGI/HmM1migSFQiGMx2Ok02ktjnQnlMtlcXLY7JRIJARWJtuGj4mLHe/kzmYzdDoduRluwzH5/Rj1oOBxWxzg4uZwOOB0OjEej+H1euUK8Pv9cnTcXlIBqDHJ6XRiNBppmWbsiouTxWKByWRCvV6H1+uF0+lEo9FQa5TRaNRSw4Yg3qEeDAaIRqM4Pz/H48ePsV6v1QRF5g3ZHxQwCCoulUqIRqNwuVyKkTFC1Ww2kclkxAJifIkunk6noxYdNvtwIY5Go3IvbLdbsXasViu63S4ODw9xcXGBdDqNUqkk1lI2m1XchLE48nAMBgNyuRzOz88VsyO8m9X2hNEyCmIymXB4eIhSqSQhitXjdrsd7XYbsVgMrVYLkUhEx8z3DgBcXl7CarUiEAgoblgsFhXVe/jwIUajEY6OjnB6eqo4EN1dtVoNZrNZIFmj0Yijo6M7Qhp5KBQcPB4PLi8vAdyIs6lUStBmp9OJi4sL1W+TH0KnxXq9FniczqKDgwOcnZ2pmYhxLJ7LfI9STLNYLKp1Z3yIANvb8Ri/338ngscIGgUDOor29vbkeGPMyGQyqQKcbUmlUgmpVEruHYPBICGTohSdFzzX2u027Ha7nG/BYBC1Wg2xWEztYKPRCLVaTeft4eEh1uu1qsgbjYaYQtvtVvFURqGSyaTA5tlsFtVqFRcXF4KFO51OnJ+fKwZFCC8h87lcTo1aFosF9XpdTLBqtYrPf/7zuL6+hs1mE8ydLX2EStO1VavVJLQRyL5arXB1dYVWq4XPfOYzWC6Xuh7xPI7FYhJYCIrfbDaKs/K9Ruemy+VCMpmEx+NBoVBQ9IvuRUbmyCeq1+uIx+MShAl+ptvOaDSiUqkgl8vpukjBtF6vIxAIIBKJ4OLiAgDEZCKTiGKPw+GQC4zXDYqyu9nNbnbz62d1dY34P3v/tpbt2gBnY+fm+7DH3t4C293z+mM1BgNMR/vo/5s/hfKf/SlMfu5LMO/nPu6j+tTPZjaD5xe/jb2/+gSu8hbOyhZ7f+05fH/329i8h1L4Yebs338FLstvTqi2mVboH//Qh7Cbl5iXFnIajQba7TYMBoMqum02m/7z6uoKyWRSfBj+oc+4iN/v111X8mh8Pp9aZG7DXQFgMplgb29PcGLeeQZu7qYGg0Gcnp7i+PhYd58J8GSTjsFgwHw+V6SKxx6NRnWc5GcEg0EEg0GcnZ3JGcPh3X/yP0ajkdwodNLcZgc5HA6s12s1o1C8Wa1WaiqiS2ez2QiKy/YqxtjYsrTZbFRBHQwGZf+nA8XlcsHv96NQKOhON6t0WQlOAWSz2aiphXf82+02hsMhVqsVHj16hKdPn+Lg4ADBYFDw4kKhIEeE0+nUz2B8aTqdKiJCca5cLiMWi8HlcsHlcuHs7AzT6RTJZFJRDYfDAZ/Ph8VigcvLS6RSKcxmMzidTgyHQ7l2GIm7uLhANpvFcDhUyxPdLRQwIpGIeEyMtBQKBTm7eOe+Xq8rfjEYDHQXny1qgUDgTkyJbp/ZbCZxhNEMxtlisZjEzkqlAr/fj8lkosYx/kyCZwlSdjgcagcjvLparcLpdKJYLOLVV19VVIeOFbo76PaIx+O4vLyE1+tVu9RgMIDT6YTJZFJ9Nl0zdFkBN8wYOjkqlQoMBgMSiYSW4Uwmoygh45XT6RTX19c4OTkRn4VMpnQ6rfgbhdrb1w4Kw2TLOJ1OMUnIraHgQaGCLWcErhNMHA6HJUBQVOH5yOtHNpsV1Ho0GqmByWw2IxgMolAoYG9vD5PJBG+//Tam0ymOj491TeJrxP/NFqtsNovlcqnjb7VaEtfoMkyn0ygUChJz2VTlcDhQqVTEHyNfh7FN4KbO/HZrEpuh6Myj6DWZTMSgoaONEGGfz4disYhoNIrRaIRkMinB7J133pETbzKZCAC9t7cnqHw0GsVsNsPZ2RncbjcCgQCGwyESiQQMBgOePHmCZDKpuBfZWP1+X9c61t0HAgH0ej0sFgs5yMhjWq/X8Pl8cDgcKBQKyOVy6PV6agB855139O8AJA7xHCNIngDk1Woldx7dmRTKAejaSfG1Vqspxsf3Bp14u9nNbnbzPbPdIvBmB4au5Xv+KfIvLPD8wq9+DAf16ZngO4M7bgNndYvY333+E1/Fbt7PofsHvgLjewzGj3s2v+1V5H8+gcGBERsL0H5kQv7nUzB8/tHHfWif+tmuVlj3+gj+X7+B0N/4BtbtDrYf0g2ozftERj/IrPenMD6+/6Ecy26+/7y0kJNOpwWVDYfDYhyMx2MBfClUOJ1O3ZGmmEJXgtPplIOnXq8LNLlcLuHxeOD3+yUGNJtNVSdfXFzAbrcDuImsVCoVpFIpFItFRQ5CoRBMJpMWHEa0COQkr4ecBcaGuAywcWWxWODw8FDiERtg/H6/7tIzjkCOBsUiOnw8Ho8qsovFopbz25BnNhgxQkCI7G34Jqty6TJIJpMwmUyoVqsCfbLdhUIQXSoOhwMmk0nRgmg0KjHN4XCoyYvQ3dvV3lxe2PY0m82QyWTULtTv9yWaEeLKOl86jfhcU1QgSJfxHsbtKHQQvGq1WsXGodh1dnYm4Y/LndvtlthjNBrlkGEsxeVyodFooNVqicdUqVTQbDbx8OFDxXkYb/L5fGK+UFgki4Y17Py+rFYng2M8HsPhcAiIzSaqer0On88Hm82GXq8nZ4/L5dKy3e/3MZ/PJRxxAeZzRbbQZDLB8fExEokEWq2WnF0mkwmxWEyiltPplKBQLt/YItm45Pf79ZzlcjkBdCnKRSIRxYgompBj5ff71ShEzghjixRrptMpDg4OcHFxIRE3FAqh1+uJq8NoHs95ALqOkFN1cXEhB8hkMtH5f3JyIvGT0T+yuEajkUDcy+US5XIZg8EAZrMZTqdTry+FC4LQycQhU4fnLZd5xp74/qc4ejv6yYidxWKB0+lENBrFt771LTmKKMyyqY4QbMYn6TSi6ElRkqBfxrA8Ho9Ay3zchUJBETbGBfk8vPvuuzAajdjb2xMUmSwkq9Uqkfn09BS5XE6uJboIKfRFIhEBifv9vsDo/B7NZhMnJydYr9cYDoeoVquYTCZqAwMglyMhzIxrUTxZr9eCkTN+RncPXXwUuxhB5fWX5xWh8wSwOxwOxW4Z86QYSQcYfy8Mh0NkMhm57gDovb+b3exmN+8366enuP9XajB0LDD0bj4iX7Mi+Au/9nEf2idqjC4XTIEADO857wFg+8YT7P+NC4TfWWHvF2qI/J13sX7vhu5P4hidTtT+va/i+Z9IoPWvLHD2Fx5j8D//Mgzm93eFfVTTPbF/TwxuawZ69z0wWKwfz0Ht5mOf+8k6Roe+j/swPvXz0kJOr9dDJBIReNPn8ymiEgwGVZfLxazdbsPhcGA6nSIej6PdbiMYDKJUKsHn82G73eLk5ARnZ2f6A59/MDN2w8WE8QRyXObzuUQAp9MJm82GUqmk2mkuMhSDYrEYVquVAJhcLGKxmOq45/M5LBYLUqkU5vM5Li8vVUFMkDGBndFoVMBn1gXzbi55OowssBaarVF0iTCaxcfMunEu94lEAs1mU8sqWSUXFxcwm83I5XKYzWaKxBBCStGJjh0Cb+nSuC2yUShgPbfFYkGlUsG9e/e0rLHtJx6Py2EzmUwQj8f19RRDbjN+CKSmCLFcLtFsNuFyuSQ6UOQbDAYIBoPweDxypfA5J1/ktddek0jFO//D4RDr9VrOo8FgIGi03W7XY6JAMJ1O4fP54PP5cHFxoTgN65Z5d77dbgMALi4u4PF4kM1mdUx0RoXDYQyHQ8Xa2HAG3IgSdP/QhUJxwe12o1QqybXARrLnz59LHOC54vP5YLfbddyTyQQOhwOnp6e4d++e3A+r1Qq1Wk3tUwQ/M9K03W5xfHyMy8tLtNttMY96vZ7gxKygZuQnnU7LaUSw+WAwwGaz0flHsYPQX8bryO0hD+l2/JKunfl8js1mg9lshsFgID6K2WxWTfvXv/51ZLNZtUe53W48e/ZMzXh0NjHGQ4fTu+++q6gOm8p4zvT7fS3sXOyXyyUikQjy+bwExUwmI54LOV+Mi9EBEgwGFeOMRCKIx+Oo1+v45je/CZ/Ph8985jM6L2+f03we2BzFawjjh81mE9vtVu+dRCKhGvvxeCyHCdlFdKncZuMwlurz+QQpZjzL7XbD5XIJeE6I8vn5+c0vhfeuTWxGI+jcaDRiOBwiEAiIW2Q2mzEYDORGopORvx8IZ2ZLF+NX4/FYTXFmsxntdhs2mw3xeFyiZ7fbRSqV0vntdDp1LnJMJhM8Hg98Pp/4ZgSdO51OQefpEqtWq3jw4IFcle12W5yik5MTRQbJ0olGo7oe7GY3u9nN+83qMo+jP/WrOPr3bj78f/Mb2L7nqtzNDx6DzYbtVz+L2h/8LPJ/9AH6v/dzd5wcq1odjn/wOtZnl9i8d13+SRyjx4Pyv/Mqhq/OsfW/d3MkskD9dyzR+/kvfMxH9/4zODACj3f5mt3s5kc5Ly3kMMLBmEa/34fD4UAkEsFwONQCwuUzGAwKRtzr9STssKKcrA+Hw6G7wmya4R3+23fefT6fwLWMe2SzWWw2G9RqNdjtdv1cskbYSNNqte5U0dpsNmw2GzFNbDabRBFWFns8Hi1G19fX8Hq9uvPOhZ7LOhd7ig6sOY7H4wAg9gRbbxizMhqNipaNx2PEYjEcHByg1Wohn88r1nP//n185zvfQTgc1vO5Xq/R7XYRiUQQi8XEBBqNRshms1qEbDab2obYwELODcUhxlMYPSgWi2rCyeVyMBgMKBQKAphS+HA6nbpDH4lEdHedzq3VaoXLy0vs7+8jm81qaeXdfMZPKCbwvCiXy3L/UNgyGo133D+VSkWvG9k59XpdIG3e0Z9MJkgkEhKZyPPZbDZq9GG9OQHLdFkQ2nvb1XG77Yc8Do/HA4/Hg4uLCzld+v2++BrJZFJxHLJMKGT4/X40Gg0JmRR/KIiRK7LdbtXsE4/H1ajGKnOv1ysRc7lcqqaasFou/3xPMRZmsVgkeLIWfLlcqnHt6upKDhWKqeQ7Ef5NdwXFXDouKKCVSiUJWTabTbGn4XCIBw8eqFGIcb1qtYpcLgePxyOxMJFIIJ/Py1VDrorBYND5TfcOzyOKqozg8JgYvQqHw6jX69hsNndeE5fLpegYRRS2OfF9S8GEAgfr3skBIyyXkO1EIiE3Fhkso9FITjn+22q1QiKRQLfbRaFQwMHBgRwlXq8X0+kUAORCdDgcer0oao1GI7GryO2imEruDkXI2+1uPPdXq5XOBavVKpGPzLPVaqXzezKZqEKdohVdVfF4XPDys7MzXW8ovlLAIXOL12O/36/4FJ8XNvsB0HnAaymr0dmESOiy1+tFo9HQ+2mxWCAWi+Hdd98V94guzul0qmsXI3Rk+GQymR/ql+xudrOb3ezm/Wf0u19F6X/owvzGBIn+oRHF3+7D9re8+nLfwGC4caR8ymvJjUE/Rp95f3Gw/ls2P56Pf4u7IN7dfKLGsP7hoNVPnmXgfb34IR3Nbr7fvLSQ4/P5YDQadTeVrpHhcIjJZCIWg9FoxPX19Z1qaNYbE6rZbrfFrGHEIZlMwmaz4cmTJ7DZbHC73boDTQcLlzGyH1qtFg4ODlTjS8cLI1MOhwONRgP9fh+JREKAUMYrZrOZFhk6Ner1ulpjWAudTCZRrVZhsVjEWDCbzQLhsnIXgJaD8XiMq6sr2Gw2uQq4cDOiAUAtUTxWumNYpzwYDNSQxRgJv44LZb1eRzgchsVikSDDuBfvqheLRWSz2Ts1vARPh8NhtTeR0UPGzng8BnCzFDEaRvYJxQ4ycuh2sNlscLlc+pput4tnz57h8PAQVqtVsQu/34/VagW/349gMKgWnkwmg+fPn2O73cLhcCgiRRGHVeC1Wk0iSaPRQDwex2KxkKg1Ho+RSqW0TDIGBEBRuUqlgs1mg1gspgppOiHo7nE4HBLbKO6wTWe5XKJer2t55Odms1mBcdvttkRNLpC3BRfWTl9cXCCRSKBSqcDtdgsgfu/ePeTzeTF8uIT7fD6JAIwQEvYbjUbltKHAB9xERTKZjMDZdFWQO0TQeKlUwosXLxAKhWCz2RCJRGCxWNButwUwr9VqipTlcjk1S223W0ynU7WhATfxP1Z5k0myXC4VhaFTiQ1U1WpVgPHPfvazeP311xGJRDCdTuWaoJhis9lQLBYVw2QsplqtquWLIhqr4i8uLtDr9ZBOp+H3++/ELhkRpeuEtd4Ejw8GA10TCfJNp9MYDAbo9Xo4Pj7W+4nXwHw+rxYxNs0RhF0sFgVg9vl8aLfbcLlcyGQymEwmyOfzcgyFQiFYLBZEIhE5niqVimKRdEVRdKVATaj1cDhEPp8XPL5er6PT6Yj1ZLVaJexUKhVst1udk3w/LBYLNWTxWsb3oNvtxv7+Pt5++21VmFPQvN0ERscRXWFms1kxt9vuzFKpBKfTKXdNKBSC3++XiBmJRBR/NZlMuLy8lLDLc5VQdYvFojgsAfmMO6ZSKUXtGFVlUxx/b+xmN7vZzW4+vDFFIhhmvleA2JqB0k87Ybp39AO/3mCzYfJzX0Lxz3wJ3X/rSzef/+MoaHzKx9l8/xps38UG2zeffsRHs5sPa+79hy8wXv7mo3HWjgmrcuVDPKLdvN+8tJDD5cDhcKDb7SIUCqHT6WC73cLr9WIwGGA0GiEcDqsOmksenQDkvRCSORwOBREme4TVyJ1OR8uxz+dTg9E3vvENZLNZVem2Wi2k02ktaIxJlMtlWK1WpNNpGI1G9Pt9sV4oINGtEwqFUK/XAUBVwvP5HOFwWIKIx+NBq9VCIpHAdrvVH/i8g8yWoNlsJrYDW4MouDCaQrdLr9cTeLRarYqDksvlMJ1OxU0ZDAZyJXApYrVxsViUM4L/xnYsvlbJZFLCAavF2ZhEbg1bhCqVippgyBXi899qteD1epFIJMSOCQQCyOfzcLvdatQpFoswmUziCaXTaXi9XiwWC7Tbbd3hZjSHbhWPx4NisQiv14v9/X0ANyweOj34uBmvYHTKZDKJm2O32xUtIbSUoh/dD3R00IFEMCubibjYUmgxGo2Ix+Ni8TCqYzabEY/HxZIhwLjdbmtJ7/V6iMViGI/HuL6+xmq1wtHRzR8ny+VSzxFrjguFAh4/fqwmplAoJFGDiy9dGHxeXC6X3it0wo3HYyQSCdRqNQmhdI9Mp9M7TXN0M/D8YcyHLT6sRmckiGIGF3W2WwUCAcTjcQkdbARiCxPB0rfh5W63G4PBQG4pvpd8Ph+m06mg2Iy+0S1is9lweXkpeDqvHWzNCgaDMJvN6PV6MBgMan8ivHdvb0/vyV6vJ/GITVpsR9put8hmszCbzYoGMdZFsDL5UOSEUaiYzWa6vpnNZkXafD6fwOOr1QqhUAiDwQDb7Rb1eh1utxt+vx/z+VyuMpPJhHQ6re9XqVRweHgohg8ANW0xxrXZbORsYeyL17V6vX6nJp0Qbwpvt1lCfA9RwKejiTB71n3TrUc2F0UhCusAFAWrVqtot9s4Pj7GaDRCIpHAeDyWuLdcLpFMJvVc2+12neMEtlNYs9lsEpfpWiwUCrh37x4qlcqdiC7FREbvms2mRLzVaoVutwuXy6XX6PDwUOLvbnazm93s5sObyZf2sHR/n380At3Xwj9QmJn87GfRfmTC1gyMMgbkf18URpfzR3Owu/m+4/wnv4bA87tijmEFBN7q/MSDqT/Js33PAf6bmcHcjvTXdkURH8W8tJDD2uRqtYpoNKr2JgoT/X4foVBICwoZNnQh3L5DTZ4GXR9sRiK7hWwF83sAL4/HI9u71WrF5eUlDg4OFPFyu29+E/D7kYPw7NkzOBwOLS4EALdaLS2MFBIYu6BgFY/H9d8bjQYajQY+97nPoVwui09BZ0C1WpU45PF4EIvF0Ov15HjJ5/NIJBKIx+NoNBqYzWbweDzIZDLI5/Nqj6KgYzAYBD3t9Xrw+/2KCXCh7fV6chpxAeWCzvgaFyxWAt9mSHBRpthQq9WQTqcRiUQEYgVu7ojTsRSJRLBYLNBqtfTcAJBzgRG7hw8fotfriW3BJcvpdEqgoQuIQGvWbrNWm9XmjUZDESQu/IysUbDgufP06VM9BrIvWGNN98JsNoPVakWlUsHJyQnMZrP4RowJ+v1+7O/v46233tIiymO/uLgQ/4OvP91Bt2u/6dxhNIjV75PJBI1GQwIfHV5sIuP7g+4Bh8OBFy9eyFlEkTQej+u8Ia/pdmSMLViMfdHhwGNgdJHOhFar9T0Q4NlspugKnXCMF/F5pDDIZjq61cxms9qvHj9+jM1mg2g0ijfeeEPHSH4N42Rs3SIgmOfWu+++i1wuJ6cPQdN0kJD7QhHEYDDg7OxMsUCj0aiYIZd18raCwSA2mw3y+TxyuZzEvtsRwF6vB6vVimg0ikqlImeazWZToxUjT2TVMOpJYYKCKCNqs9kMsVhM7w9WYJPhtV6vFa9LJpOYz+eqWKdYQiAxPxi1pGMlFouJw/XWW2/JEceGK5PJpPa609NTOaAYcXO73ao0t70HoKQASuH9+vpa7klGAdkGRXg8eUl0+k0mEzWfUcDje5XQaoqwrKs3GAw6B91ut54fRsFms5nEIIp/ZPvweWa0NhaLodVqSUQlT4pi4GKxQKFQkAhGh9dudrOb3ezmo5tJzAiD8fvHO3qHvw7yawA6v+eVH/FRfTyzLleR+G/M2C7vrm3bmQnHf2sObN7fFfNRzHa1gucfvYm9/+JcH7m//gLrp6cf2zHt5oefzXyO9V+NoTtzfOCvrbT8MH9tB3z/KOalhZxCoQCfzycBhJBc/vGfzWZxdXWFQCAgS7/H48Hl5aWcKNlsFhaLBd1uF5PJBEdHR+j1erh37x6ePHkihwr5O1w4GTVxuVxa2t955x0Eg0EtCWx+6na7WlYIEfV4PIjH43jrrbew3W4lvjAGYbVaxYbh3elyuYz1eg2v1wur1YpHjx7hxYsX6PV6sNvtGA6HuH//Pubzue7KNxoNNVqt12s4nU5cX1/j4cOHiteQB9TtdlWX63Q6kc1m1dbF2t9yuYxcLqeFgi1fXNjpjOGSPp/P0W631Sy0WCzklqIrgMsUXUpszIlGo3IVNJtNDAYDeDweOVxarZaqm+12u5bJwWCAQCAgvgbdMFwkWTXORbtYLOrOfiwWQzgcRq1WQyAQwHq9htVqhcFgQKfTweHhIUwmE87OzgRpppPpNgOFd9UZ+QmHw3eajgaDAY6OjuR6SqVSqh+n86DT6ahJajQaodfr4cGDB1pcl8slnj17hsePH+Pi4kLnSbPZlOgUjUZ1/IwdeTwesT9isZjawCgoLpdLRKNRdLtd7O3tyTkEQFGTr371qzg/P9f5st1u9R4k6JlNRnTBbbdbnSsXFxcIhUIYDod3hApGdYxGIxKJBBwOB54/f67Ke7oqKDzM53OEQiHVeHNJp3BEFwqBuBTRms0m+v0+2u02Hj58qIiR1+tFoVAQo4YODAotZKCcnJyg0+no5zDaQ6dVq9WC1WpVi1ur1VKDEUUsthrx9SCEmq/jcDhEq9XC3t6enGKMgfH9SsErnU4LxEvOy3K5RDweR61WE9uJzwldXt1uV7XbdIOQTRMOh+FyuVAulzEajSSmsgmPzhsKJ+VyGclkEo1GA3t7e3C5XDg6OsJoNJJTKZ/PIxaL6Vyg6P348WM0Gg0UCgWYzWaBjcfjsRg6FKEnk4kis6w557UfwB2Y/Waz0XuSQj4dSGxIazQaioDy94PVakUgEFDTGJ1gFBQDgQAajQYCgYBa8Twej24A+Hw+eL1exXgp7NG9R3GKPChCrT0ej4Q+crIODw9hMBgklFPc2c1udrOb3Xy447gewvgDbtqn/j/ND1yl/H0dPp/w2a5WcP/dbyLzTwwI/rJVH8d/cw7Dr7z1cR8etvM51vXGdz9au5KAT/xst3D8g9dh/j+F0fsAYs6TyxTu/bnWzo31Ec0HilbdBnK2222YTCYxIOggaTabiMfjWK/XuoPMlqvhcCixg24bgmsDgYAiIFw+6MDgH9qXl5fIZDJwOp0wGo2qzGbbD9kKXGw9Hg/Ozs60GLMSmy1S5Ejk83nUajVVUjOOkkwmUSqVAECWe7JsCFpljTIXvul0imQyqf+eSCTw7Nkz+P1+RSjcbrcWpmazKYcLlz+r1YrZbAa73X6nsp2RBgJsy+UyttstIpGIHEds6GLjFNlAdrsd19fXirawbebg4ADb7Ra1Wk0/g3G51WqFRqOBWCwGl8slMYELs9/vR7PZlLDy+c9/HvV6XXE1Mibi8bg4LalUSgJLr9eTOEMIqsl0Y6N1uVx48803EYlEcO/ePXS7XdTrdQQCAYxGI8FRg8GgIn8UEFn5bTab9TpTeGIl+HA4lEgD3Nyln06n+hl8vQnDnk6n2NvbU0SvUCjAarXC7XbD4/HA6XSiUCjotaJzgNXrABTxYQyR7x3WgtOpxtgW4dhsA2JL0mKxwGAwQCgUEmuk0+lIJKBIQf7LaDQS54MOlkqlgr29PQwGA9RqNRiNRgwGAy2ys9lMDju+910uF4bDoZqwyIPi46LIMBgM5PQi64nCWSKR0PuUfBwAMJvN6HQ6qNVqElbZKsb6brrFWq0WqtWq3mdutxv9fl/Q5aOjIzSbTcV42GZHpwuPl86hXq8Ht9sNn8+HFy9eKMJlt9sl4vFcSSQScs0w5uT1euWcI7B6NBophka3CuNHy+USs9lMr1U4HBbwmi1NbGczm82IRCISYmw2G66vr5FMJtUESNGNwlUymVTtPN2MvN5S+Eomk/B6vYrtEV49HA7vMGQcDgeur68Rj8cFnaewNJlMdO1hfJbOLgpu3W5XohVr0BnBZGSJQhu5T6w9J0icr7HdbsdkMoHNZkOtVlOMdzAYyF0Tj8dxdXUFr9eL+XyOYDCI4XAoEbrdbiORSKiN63YbHyOnFHzYtsZr0m52s5vd7ObDm827z2EdvP+y573aYFv4wXwNw/p7/3f8lz7dAoLjH76O0H/5DX38OIg4u/l0j/2fvA7jXwtjuf7BfwvN12Y8L8fx8H/bwup6Bzn+qOalhRy6PqrVKjKZDKLRKMrlsuquT09P1WY0Ho+RTqcVQSGXg9Z//n/tdltLjsViAXBTW07GRjKZRK/Xg81mQ6PRwKNHjzAcDtHpdMSqsdvteOedd+DxeJBIJOTq4WLLNqler4dEIoHBYKClLhaL4fT0FJlMBqlUCufn52qnYnSMyzHbauh8sNlsWhbK5TI6nQ729vYQCAS0DAQCATVPEb5LMYwLPe/O864xIyoUpsj0YXyAd8W5aDAaxP9usVgUOdrb20M+n8dkMsFsNsPnPvc5sYDYvgRA7UWEmxJQHI1G1dDFGunBYIBisajKeafTiVKpJJZLOBxWhfBkMkE0GkWtVkOz2UQul0On01HkxGq1CnDL84s8jGaziWg0CgDiIbFhi89tq9UStJnNPdfX13o8VqsVi8UCTqdTTTX8fKPRiHq9jmAwKNGg2Wxif38fjUYDABSz4utgtVrR6XQQi8WQSqXQ6XQExr4Nl16v13KEBQIB9Ho9TCYTcVh8Pt+dBrBGo4HRaITtdotQKITVaoVsNot+v6/zhrG/VCqlZdloNKJQKGA0Guk8I/uJUHHGS+r1Ok5OTjCZTFAulxGJRBStYmSIYo7NZoPf70er1QKAO0JZr9fDfD5HNBrVa8D3/G2mVK1WQyqVQqVSUXwlGAzi8vISy+US6XRawq/BYMD5+TlSqZTcJnwfEp5N0bRareLg4AC5XA5XV1eIxWJiKw0GAxgMBnS7XSQSCS3v8/lcrCw6ysiW4uvs9XpVSz6dTjGZTBQbDQQC6Pf7aDabSCQSWCwWCAQCKJVKEltsNhuePXuGe/fuSXyy2+1ydwWDQTidTjmmKIguFgs0m005Vyj6MTIUCARQr9d1rWy1WshkMneqwA0GA8bjseJVoVBIjX2VSkWsLp6T/F6MjdlsNgSDQb32VqtV70GK9NvtVkIrGUTNZlPvWfJqeB0nQ8nhcIgZxlheMpnE22+/jfV6LTcfRTYK9RTBZ7MZ+v2+YMqRSEQuoXQ6jevra7m7eKNgf38f5XJZDiOKQbwh4HK5xNHhMdTrdTgcDnHIttstDg8PUSqVdCNgN7vZzW528+FO/G8/hbuwhekWU95Z2cJ7OcXmN7j2pv72OdyFrT5y/6C1i/PsZjc/gnH8g9cx/ctJPH0rh6f55J1/e3KewtO3ctj8xSiO/tBTrC7zH89B/oTOSws5BGHSgj8cDvH48WOcn58rJjOdTgU1pauFf9gPh0Nks1nFBtj2AkCNS/z/uICv12stHOPxGO12Wy4Qgjzn8zlMJpP+u9/vl8Xe6XTKqcLq2mAwqOpqNlwVCgV4PB4BObkQcuEki8blcqFSqciuf7vqNxaLAYD4J2zX4UJHCDO5JrcZJWyLCgQCWvzZkMSICBeS4XAIq9WqWM14PEYoFJKAsFqt5AoghJbLCR0fjK15PB7k83kYDAYEg0G43W50u121uIzHY3i9XnFd3G43UqkUFouFHDwGgwGZTEbLq8FgkCtju90KXkwBbLPZ6DkCoLYetlM9f/5cMSkKeuSl8NwinJjChdPplKvktoA0mUzQ7XbhcDjg8/kAfBewbLfb1dDEx+L3+1EqlQRQZWsZIzkUl2azmeIbBHZTGCyXy3LpMDpDCK/D4UAkEtHiS4ZMJpOBwWBAqVSSgED2D98PmUwG7XZbzg/gJu4YjUb1+Pj6XV9fYzqdIpVKSeykGGY2m9WUxbau2WyGcDiM6+trxenIPwoEAmKOlEolGI1GPW+DwUCxJjr12H7ECAxFtcvLS0HOzWYzTk9P4Xa7kclkdCz1eh0PHz7EZDLB+fm5xCyCrPnad7tdOT0Yz6IYynpssoO63a6EQta4s52LVeYEr5vNZj3/TqcTPp8PnU5HIinFVHJqCPzleRkMBnFxcaH3DV1jXq9XdePAjTDGxqtAIKDrFp8Dt9uNTqej6y4FhtPTUwl5fO/QLcIYHgXG+XwuphDPNzrt6EKjG4xCFeOgfK4o8MxmM3Q6Hb128/kc6/Ua6XQaAOR84XlLVhcdPOQy8edTqGNjF6N2q9UKlUoF4XBY3CGbzSYHJ11HbKNjYyLFa8YqySIyGo26jrtcLjUHUrCmUGw0GlWZTvA6277IYtvNbnazm918+LPu9RH4r76BzH/bRuq/myD1300Q+a+/81JOk3W9gcB/9Q197ESc3ezmRzeOf/g6jv/Er+LB/7qF5t/Y08fDP1/E8Z/4VZj/+RvYvvf35W4+unlpIYdxAYfDgfV6DY/Ho6reSCSC0WikqEK73YbFYhEzwe12624/RRLCk7vdLvr9vu58MxbFpWs+n4trwjvsbAmiOyUej2M4HOquucvlwmc+8xlxfci3GI/HmM/nWK1WWnrj8Thms5nu3pbLZUWwvF6vHnO73dbXkX1B9898Plf7D+MO5KfQ+cDoDRdRRjsoRnCR63a7el65aLBynJ/DKmAAEkfI37BarXeiRQQsezweVRQzZkFhhw1V/X4fq9UKe3t7EqkASPCiYEZ3Cp8zslnW67WcUnT+UAhhJTEXW7bQhMNh8YO22y0ePnyo9i6fz6c6cbfbLWcTP4xGI0KhkM49VtoToO12u1VdzFYzioOz2UzRPy7WFLDMZrMcSayfpuPLZDKhVCqpaWc6nep5Xa/Xch6Mx2MEg0G02221O5VKJQQCATVwhcNhrFYrXF9fw+VyyeXBn8Poyng81uPiwkuwdqfT0c/g8bMiejAYYDqdStygk2S5XEqwKZVKd+C1BFCPRiNks1lMp1Pcv38f19fXmM/nODg4kMONzheyVwggLpVKAiEfHBwINFutVjEcDhXn4/PG9xXZSA6HA6lUCo1GQyIExVRyhdjKxOga4cJkDDGGyM8jMJxxnfV6rXry0WikKGCn0xGs/erqCvfv3wdwIwCymY0QX8ZBS6USwuGwzpNgMCgH2Xq9louM102Cg9lwl8lk1FDF6x9FD4vFgkajoevBfD5XdTy5WtVqFfF4XGDlw8NDucxu86cqlQo8Ho+im4vFAiaTCev1Wq5DOvUYGQMgUZRxOEZD6c6ioEw3FkVTOgR5PWOUlFBzOit57Q4EAkgmk8jn82Ke0SXE1zUej6uV0Gazwel0Kj5pNBoVHctms+J2MVpGMZ2tVzxfVqvVHaDxcDhELBZDv9/HYDAQo2w3u9nNbnbzo5n1kxcw/vKbMP7ym9jMZr/xF+xmN7v5WGZ1dQ3/3/qGPla1+sd9SD/R89JCTjAYxMnJCfL5/J2a2mw2KzhpNBoV+yQSiSiisFgsYDDckOcJpXS73RIHAoEAyuUyjEYjUqkULi4uYLfbxcfhnefxeIzJZCIuyosXL+Dz+ZBIJFSFTj7D66+/ruUmnU5r+c/n83jttddQr9fFqzk5OUGxWFTtMh0xbEphnfB6vcZmsxHrZjKZSLzyeDzo9Xp45ZVXcH5+rqV+sVgoznG7xWWxWCiKxChVs9lUhMVoNGK9XmM+n6v+eTKZKF5D8YPuANaR367CZgsOgc2MivD/J/djOBwKZkrOzHK51KJO985gMNDddzJSGo2GnptEIoFSqYRKpYJYLAabzYZsNotKpaJoFRuAut2uHFqsEp/NZigUCkgmkwKtRqNROVB4HABQLpfFN0kmk7qDzyWPziGyM8jjIU8kEolgu92i2+0CgIRHOiAMBoMcCovFAtPpFJVKBbPZDK+88ooiQBThWMNMN4XP59O5b7FYBGVlOw8jIxR+TCYTms0mUqmU3BTRaBSXl5fiAtGBEA6HJXDYbDYJEHa7Hefn57h37x48Ho94VNfX14hEIqqnZiNbrVaDz+eT6yYej6NarSoixtrwQqGAg4MDhEIh5PN5ibo+n0/nE6HXV1dXePjwIcrlshxffr//Di+IYhXFUEZwCBG/Df09PDzU63N2doZoNCqHh81mUxzS6XQKtM7WL7oHV6uV3DTkH7Hhbj6fI5PJoNfrSSAl/4YRnVKphAcPHqi9yWKxyNVGVhGdYYzD3RYyKSTSjUinDFvC6BDk88RzkNBxuh3dbreEHApSPJcoVPj9frz99tsSUCmqMJbEmFe9XpezqtVqyVFI/hmv6wSwMx5Lh0s4HIbNZkO5XFaMkfE+coDm87k4Q9vtVrGq5XKpOBldM/1+H+FwGMvlEq1WC6lUSjcC2HQVj8exWCwwn88xn88VbeQ1iTcHGCUlF6vf7yt6SWdcIBCQm5HivtVqFXvJaDQqRsbfXbvZzW4+vTP/2S+i/b/8yvd8zH/XFz/uQ9vNbnazm93s5n3npYWc09NTTKdTPHjwQI4Or9eLFy9eiJXCO+RsZuLiXalUEIlE9DmhUEhsFADo9XoC+NLSzypZWv7prjCZTKo2jsVimEwmEmECgQAikYhYFazvHQ6Hsv0/ePAA7777LlKplBaxyWSCTCajJZvOGi5ghAVzQSB4lS4c4CamwKYVCgCj0QiNRkMtSYzbUADy+/2CQrNSu9PpKCbBCmBGs/hzzGYznE4nwuGwYhQ2m013pK1Wq6p8GRVoNptqc2Jzl8ViQSwWQ71ev1P7zrgXI3Ber1cilslkQr1eV9sR78LT4WQwGBQJG4/HOD8/13HmcjnFeujQ4HEwhsaFP5VKwWKxSDCaz+e6u89llEsxBRXGsQirbTQa+OxnP6toCJ0GnU5HTT10cxBOTLgwBTw2dBE0u16vBV8djUY658ljonODfBzGw+iaAiBRj4IFW6QikYjcR2xNy2QyqnL2eDyKkHFBJmyWrrNsNovr62uJMRSa6D6iGyGbzWIymcjxRiYLnW4U8tiqNZ1OxTEZj8da2ofDIXK5nOJmgUBA8T+DwSA313Q6hcPhkHhIRwfFV7a4UXjk8kwm0e2WJDox6MjiMeXzeTlZfD6feES8BtDJE4vF4HQ6dd0gsyudTt9xY5ydncHv98tZQueJzWaTKMoGudv17xQKAEgMIFx7uVzqPcNrKHlct+vkWTvONiXGxejUSiQSKJfLEkZXqxVisZiibKwr39/f13lHMHWz2RTfiCIGmwIpUNEt6Ha7Fcekg4fnCs8/RhJXq5ViTQRhs1nK6XSiUqnIIUawM6OxfK+wVY3g8clkgmQyKU7UbDbTuUuQN5sUCcp2Op2Yz+f6fEbbCM9frVaKSfK6QNYVHXB0Au2EnN3s5tM95lwGp//5lzD+d/uI//7893xM/t0eTv/zL8F8sAfDe27K3exmN7vZzW5+HOalhRyyPshKYBSA0QlWW9PFQnFmNBopQkIxolKpIJvN4uLiAoFAQH/I071C943NZhOctlqtineRSCSQSCRweXl5h3XC6JbP50OhULiz6HLBvg26JDhzOp0iGo2qonuxWCAej+Pi4kILVblc1lLHCI/dblf9MgA1aDEuYrFYEI/H0ev10Gw2tSQmk0k1EfX7fbE1eKebMZ7r62ukUikAUGU3Ixxc7OnSYW17KBSSO4MLoNlsRjgc1vJJMeXq6gpWqxV7e3t67abTqcDQjJKwRpiih8FgwGQyQa1Ww8nJCUwmkyJljHhNJhM4nU4xNsxmM66vrxGNRhWZoCBHhwhZIcCNA6xUKqHZbIpnZLPZcHZ2hkwmoxp71ghzeWXM6rZAM51O8dprr6FcLosP4vF4tMCu12uBcBn34Lkxn8+1eK/Xa4l0tVpNQh/PXS674/EY2+0W/X4fe3t7iu4BkGPI7/dLSCIHpt/v4+TkBLVaTU4LxoSWyyWWyyX29vbQ6XTkOmm324hEIop5cVHmsg1AzjYKGzabTbHDUCgkxwPFsEqlgtFohEePHmG9Xut8Y7MP3W18v4xGI0Xr2PYVDodhNBoxHo/lAPN4PIo89ft9JJNJ1Ot19Pt9vf8IAM9mswI90+G3Xq+RSqXQ7XZ1XIwNlctlcYII4Lbb7QgGg7i+vkaz2cTx8bFanOgEA27EltVqhXq9LsZMMpmEy+VSuxGdKQRL83pC0Ha/30ckEoHX68XFxQX29vbkWozH42i1WnA4HOh0OnKNUCBdrVaC9TLW53a7JZiu12sBsgm9jsViqFQqAsIzpkXW13K5lFOL7W4UKAnxpojC2vfxeKxIYLPZ1Ht3Npuh3W4jFAqphYucG4rTdOwxAkiRl2IiAIlDFDmfPHmCXC4HALi4uJCQy+sxIcSDwUAg+dVqhW9/+9uqlY/H42g2mxIA1+s1rFarYrutVksOQvJ66AK63dRFnhUh7JFIBJeXl4JA72Y3u/n0jenBMa7+Ux8ePSgi5h6+7+dEXSM8elDE6q+vcPG/eQ2mcOgjPsrd7GY3u9nNbt5/XlrI4fJiMpnQ7Xb1B73T6UQkEkG1WhWLgHfN+QczGRxc1KLRKFwuF3w+n0Qg3v3kYsj4ltFoVJMTIwWNRgNWq1U2fdYDk29B1g4btYxGI5xOJwBoAbdYLLDZbLrbXCqVxBJhLIwQTd5Nn06nuqvd7XZ1jHa7XbXnjBnwuAaDgZwl4XAYVqsVlcpNpSLFBDY2MapSr9cFIq3VarL6c6EwGAy4d+8enj17hlgshmKxqCYZxpQYl6DIFI/HcXl5KceKxWJRyxedUHTghEIhTKdTvd7kdpjNZjlZyLDZbre4vLxEOBxWqxZfM7oT6M7i3XFyhhg9ovBBYWSz2eDy8lKiBeG7lUoFv/W3/lYUCgXMZjOk02lFKLhMs1qeLqXZbIbhcChxo91uw+FwyG1AN4PJZMJgMEA6nUatVsN2uxXgltEoxtMYPaO7rFarIRaLiRNCQDFbmRjHodDC156vDTlJmUwGxWIRbrcb4XAYxWJRLUN0o7ndbtVt3xYs2EDWbre1xIdCIQlerJNnxKRSqcDhcEiQIE+EwgIfH90RXI5vV5AzOlOtVuXQSafTikXR+XVbEByPx4rdtdttbDYb8XUIG4/FYhIETSYT+v2+nBl0Wsznc7Wrmc1m5HI5DIdD8VgI6e52u0ilUrBarXJz0fnCqBEdMOQk0bXF9wEB6hRuqtUqJpMJ9vf3xW+hwMnnBPguRJdsJJ4HdrtdLhm+X25fl5xOpyrCKRCRk3N0dIROp4NGoyH3k8PhgNfrlVDSbrfVgMb3HZ083W5Xghyvt+Ti8Fyny/C26MQIEl97uov4HgYgODVfE5PJhEajoYY9noMWiwWLxQJer1fXLgpyfD4qlYoeOwW16+trCfkUkAwGg+J5jF0uFgtdx+m0YTsX29ysViusVivy+Ty8Xi+y2SzOz8+Rz+fl5vR6vZjNZhJ/d7Ob3Xx6xnR8gBd/zo2D8MvVVVtMa9z7Uh5nf/YEMO4A6LvZzW52s5uPf15ayNlut4oS8Y4rmQ0AdLeZzpFEIqEICdt9uESShTGbzfDqq6/KCcFZr9diPjCKRPcChRGLxYLr62tks1ktF7T1VyoVHB4ewu/3o1KpqPWI34NuBYvFAoPBoEVtMpkIvOlyuRCPx3VXmQ02XIoYpaII43Q6YTAYxIwBvuuiIfuCTJZgMChuC4G+XCbJV+l2u3eW3EwmoxgSW33YWESw8mQyUcyNEQuKbhR2KG7w9VosFqjX68hms3rdLi8v4fF45BKwWq2q9uaSfHsRPz4+VsSLd80p4hDCm0gkYDQasVwu8eTJE0WUEokEnE6n2DIUdYLBoGqhq9WqXDdkZaxWK+Tzeezt7aHZbMJisSASiegxsUWKNcjn5+cIBoOKU1BkDAQCmM1mqNfr8Hg8qiRvt9uw2WyIxWISMWazmRw0bIqic2E4HOrc5ZJIAe/6+hoAkEqldJ602229FxhLYnsSWURf/vKX1f613W5hsVhQrVYBfLdFzu/3y1GyWq0k4NhsNpRKJS3JFAGDwSCKxSICgQBcLpciWmwdczgcggOPx2MBh8PhsARIAILkVioVvPrqq3J5sUabyzcdby6XC6lUCk+fPhV7iQ1YFLv4HJVKJQmxjOFUq1WBcfk4m82mxDxG+XjtGAwGEvUozHg8Hv08Co2pVAq9Xk/PKWvdR6MRvF6von18vJlMRtGkJ0+eYLVaIZFIIBwOiy80m80UTWRrHQVsAIpwlkolTKdTvPLKK6pJp4uK/KlUKiX3VrVaRafTwYMHDyQazudzxGIxxdRmsxmcTic6nY5Emmg0inq9jr29PbU71Wo1HB4eyvVFp1EsFpNAEgwGBTmmw47uRNZ2W61W1c/z/V+v18Uxun//Psxms6JXFJ/IHSN8nefdaDTS9Rq4AQ/z5gEbvoDvsoeGwyEePnyIZrOJXq+n+N9ms9E5RQGHEcvbHKVcLgeDwYB8Po9UKqVI5mw2w8HBgVrvdrOb3Xx6xryfw/X/3oEHmdoH/trjL16j8O//1I/gqHazm93sZje7+WDz0kKO1+tVHIh8BzpsGJsgiNhgMGAwGGCz2aihihZ9RqXYUlIsFsUh4V3+QCAAo9GopY3fbzAYIJlM6uvpnLHZbGJs8M40HUSME/EuPNunWGFNTg0bgOhmSaVSKBQKdxwRBoMBoVAIzWZTfB/+O9tbyA/y+XwCjfb7fQyHQ0UZGEkhYJiRiMlkokUmHo9ryTaZTHjjjTcU+8hms7DZbKjVbv4I6Xa7SCaTWK/Xei7G4zFKpRJ+5md+Rk1YBJK2Wi0xa7jMfOtb35LDhlwP3rF3u93o9XqIRqP6esZ8yFehmEGXExk/ZJacnp5qUez1emqK4ZI8mUyQSqUkvBB2zGX/6upK0ZVWq6XPJ1QXgNw2dM6QfwFADqxGo4F4PI75fK4672AwCJ/PJ/GCMa7ZbIZyuSxgMe/0U/yhgEiGETk6VqtVoh9dLPzeFO94DlutVsXW6D6iEHN5eama83a7LfA0m3QoGNHdAdxtGFutVphMJphMJtjb20M8HpfzjHDlyWQCh8MhgHexWEQoFEI2m0WhUJATrt/vA4CAwQAUj+t0OhKGCInmtWK5XCIQCKBSqcBoNCKRSCi6w+sCz1su4nwv+P1+RbWCwaBEJbKYKLT2+329l+leSSaTGA6HuiawVaxarcLpdCIWi2E4HEo4NJlMgt1ms1mJCqwqp+uvUCjg8PBQ7wGbzXbHrTWfz9Hr9bDZbCTysdYagMDWVqsV9+7dw2QywZMnT9RIxUY8Vn+fn59jMBjAarXiwYMHaDQaihJFo1G43W5Uq1UJTeTxJBIJxQ4pxgyHQ4RCIcUtGStyOp2qZW+1WooVsrmKte4ErIdCIXi9XrzzzjvYbrdy3EwmEyyXS+RyObHMarUaDAaDrsPD4VBCN6vQZ7OZfmav10Mmk8FkMlHMi/FLj8eD+XyOfr8Pg8EAi8VyR9Cy2WxIJpNYrVYwGo0Su4GbGxGLxQLb7VbNV+v1GtfX17pu2Gw2sdOGwyEqlQpyuZzO/d3sZjefjjn9d5LYD3V+4098nzEatlieTGB8fP9DPqrd7GY3u9nNbj7YvLSQ0+/38aUvfQmlUklAXgovdJTwD3OHwyFxgpW2dGecnp5iNBohEAggFArpbu9qtcJwOPwexwwjXBRlqtWqmkycTifK5TLi8bhqsB0OB9xuN66uruDxeBQR4TIRCoXQarXEkplMJooW0UXEhS4UCskFwzrh0WiEaDSKWq2GUCiEcrmMSCQCh8OBer2uVp3RaCQ+A5fTxWKB2WymGJnH48Fbb70Ft9t9p/6W8Qy73Y5arSYnE5dAikBc2m02G4rForgRDofjzvLFu+MUmcjHKJfLikQlk0ktUhQjCO29uLiAz+cTS2Y6narNiQs8RReKWrFYTLEpRs7cbjcuLi5wdHQkngwhrjw+ih8EmPLuOcWD8/Nz2O12CUJsGSPI2ev1IhwOY7vdKrbHO/fr9Rr7+/uqIacrIBwOo16vYzqdIhgMSoQxm8347Gc/i/V6rWXObDaj1WqpKWm5XGI8HguqS8cY42hsZ7q8vMR4PFZcxmw2qybcbDYLeH10dIR33nkH0+kUnU4HqVRK0bBeryeWEeM32WwWAMSuWq1WcDgc6Ha7sFqtyGazisRwSfd6vXjrrbfgcDgQDAYVpXK73QIe05lEhhAAuZ7a7bZ4T1yoycOhmOVyuXQNcLvd8Hq9ahDr9/t63wcCAYlYjNfl83mEw2EANw4digz5fF7XDT5G8poIO6dTjks8OUl7e3vi8Ww2G9Xec8hz2m63OD8/x9HRkZw68/lckU+6fNgqBgDFYlFtSOFwGLFYDPP5XO4tg8EAo9GoVjLGnS4vLxXvY7Me3YC3I4Kj0QjlclnsGLvdrvd0r9eDw+GQa4c/dzQa4ezsDB6PB5FIBEajEQ6HQ01e9XodRuPN5X+9XgtUvVqtMJ1OJSYZDAaYzeY7zi26Ymw2m1yJfr9fccxut4v5fI5kMoloNIpf+7VfQzQaFTh4s9no+LvdLtLpNCaTibhfvLbzfclrFFuzeEx0RtF902631eR2W2ClM67f72OxWChCSoEok8lguVxKGLdarToO1rLvZje7+XTM8mc+j8Cj1g/1Pe4n6+g98n84B7Sb3exmN7vZzW9yXlrISaVSeP311xGJRCQ6sCEpFouhWq2q3rnf76v+mdWyuVwOtVoNgUBAsSna4OmAYPyDiwIhslwqZ7MZAKhamHb/fr8vQYCNS/v7+ygWi1iv1zg4OMBgMECv15MQA0Bug0KhoDvVk8kEdrtdINlutytnDWMfAHDv3j28++67sFqtCAaDijJVKhWBNLmAkMHi9/vFYGk2m+h0Orh37x4SiQS+/vWvIxQKIRQKqVZ7uVzqcd52dZDPw4YhtmiR9+F2uyWgcFm12WwCQtvtdthsNni9/3/2/jRWtwRf74Oe9c7zPI97PEOdGrqr25d727J1bfiQSAZhiGRlIAkS+WAUEEhBQsgCB8nEKApggiyRRAgkf4kFtuMoIYaAka90h759u6q6Tp1hz+88z/PMh13P03tz++JTt4dqV62/VOquOntY+33XWmf/n/U8vyeguNx0OtXTdj695xN5Lvj1eh2z2Qy5XA4+nw/j8ViuBj5RNwxDoFsu66zMpjAzHo/R6/WQSCTEn2FNcC6XQ6VSEYuH7w3dUGz4oTg4mUxwcnKCXq+nSvuHLjE6k46Pj/Uzkd9UKBRQqVSwWCwE3uVx08lze3sr4Y0taYz7UPwgWHW73eq1zeVyuLm5weeff45CoYAnT55gNpvJ5UURiDwcwohZy054MgUkClo8Ljp7KDrlcjmxpWw2m4DPt7e3eu19Ph86nQ4qlQo++OADOSMYHwqFQgiHw2g2m3LX8Tymm4MsF772/HhefwSB7/d7cWPIilmtVhK8KpWKWqTokGKDXCaTgc1mE2vHYrHg6dOnagdjpJMOlJOTE7GLrq+vkUwmcXZ2JtGmWq3qXkFXB8UE8oU6nY64SLvdTsBjCoh02SyXS7lLCG+niwaAmCpPnz6V26hWq0lo5hDUS17Oj370I7Xb9ft92O12MZ/i8TgAiPvS7XZ1Pfp8PrF8KErTPZZMJsWVcTgcaDabcvKEw2F89tlnKBQKOpfokCIEmPEzxgq32y2i0ajiY3TObTabRzXydFM1Gg30ej2cn59jPB4jFArhcDiI0wX8FAS92WzUVDidTuF2uyUqUqQcDAaIRqNIJpNy4jEuFQgEJH6SUcRrhs1exWIRs9kMb9++RT6f18cw5khRbTab6X7Nn8scc8z5ZswyakfCO/26D8Mcc8wxx5xvyFjDYRjffQHjuy9gS6d+pd/7nYUcu92u2uXBYIBUKoXRaKTlB4BgtlwCuYSyFjsYDGqp9Pv9cLvdKJVK+nNWcNOtQWGHzIPVaoWPPvoIb9++1S/7dK8wPvVQ1KFFn18nFouJ10MGCh0sXD4JuCW/xW63o1aricGRz+clXlDwAe55OJvNBpFIRL/88yl4MBhErVYDALlzbDabGl3oEGI97sPWFUJBM5kMPvnkE1WTn52dqVGJQhMjIgS0UpTifyfYk8fACBSFFsZiDMOQK4mVwuFwGB6PB7PZDA6HAz/+8Y+Ry+V0vGSCEHQM3AtyrPTmE306kpLJJF6/fi1+kMPhQLVaxW63Ez+FggS5Q4T2sr2Hn8efh8tmv99/FLXJ5/O4urpSfXgwGES73Ua/38ezZ88wnU7FVKF7h7ENLqbBYFDnAuuoGdfjec9jqVQqmEwmKBQKEvfYSEao7mKxeAS6brVaYg8RYJvP59FutyWK0JFAMaTRaGCz2SCfz+t8ZMRmMBjA6XSq9ns2mymyxUWbzhW6q9brtSq1rVar/pe17nwd3G43Op2OWFdOp1NV0AQQp1IpiT382ShItFottV/RpRaPx+WCWCwWsNlsiEajct20Wi34fL7HN68vBaJ2uy3Rjs4bnpd0DFHk8vl8CIfDaDQa8Hq9aLfbEpbT6bRYSYxIMUrJ858RMEZBnU4n3r59i2g0inA4jLu7O52L5A6xTWs0GmGxWOjaI1eGUOmHsUvGsubzOQDo86fTqYTz4+NjAEC1WhWUOpfLYbFYoNvtKgLY7/cRj8dht9ux3W4xGAwURySUuNvt6rrN5XIol8sS8lhZT9C1YRiCjLO9j+6jzWaDTCaDRqOhWC2jc06nExcXF3IsMhrY6/XUzEZhj3GxeDyu2NRms5HIbxgGfD4fQqGQ4M7xeFznE+vgV6uVHDmMtDH+u9/vsVgs9Dm89/N9oIhIYcgcc8wxh9P8iztYvxT6zTHHHHPM+XaOYXeg85efofyXgij/pSDmH+V/pd//nYWcH//4x/j+978vB0elUkE+nxc4k64NLmVsHXnx4oXamzwej2qcu90uxuMxCoWCBI9Xr14hEolgv9/LEn92dobtdotutwu73Y5mswmLxfKIj1MqlcR/YbMMgZVsgvJ6veh2u1qE6HoBoGgXowSM5vBpNCunKa7Y7Xa02228//77qFQqACDmBONEu90OgUBALBC2WnE5oKuJTT5cMBlFo7vI6/VKmPjud7+L4XAIwzBQqVQQj8fRbDYF+WUEgItpNBqVEMDGLDpiCDHma2S32zGZTBR3IUek0WhguVxivV4/AvnSnWKz2SQ0kMtCYY5MmkAggPF4jGQyKV4K41HT6RStVgvlchlPnjxBt9vFbDZDIBBAIpEQt4fni8vlQi6Xw3A4VNTIYrGgVCqJY5TL5QTlplukWCyi1+vJIUZXDquo/X6/INNcZHl+MXJTrVYVFaLQ2O/3JTasVitks1kJDs1mE0dHR4oDUmDh+8sWI3Jn1us12u22+B3dbldtVYzNDIdDCYoPxZZSqYTD4aDGp5OTE6xWKyQSCb12rFY+OjpCuVyWMMOYZKfTwWQyUbSNLpbxeIxgMCinAllSjLjQ9ZBMJhEIBCRqbjYbuFwuZLNZrFYrdDodVcyzOWw+n6PT6SAajSIWi2E8HmO32z3i0zBOGAqFxGSiY4mNWnSbESRcLpdxdnYm9w5ZMHR1UNxglCgcDovD5HK59HOwNc7v96NUKsFutyMUCinaVqvV5G7iz2u329HpdBSF5PX5sAkvEonIVRSNRh+10q3XazVOWa1WRYDi8bgigHQTud1uOQ2j0SharRbW6zWePn2KbrcreDPded1uF7FYTM66RqOhmvbVaqUYG++b5IdRZKbjhS4ysqHIC4rFYqhWq/jOd74jflEsFkO73cZoNBK0vdPpyJUTi8UUESPrjMyv6+trAPdwbbaaUXSkGBqLxR61+1GYorOLTW68f7A+nQD6aDSKeDwuDhuZWJPJBNlsVs4rc8wxxxzOi/MqDI8JQjfHHHPM+SaMLZeFLZ+D8aU55V3HsNswyxj69/Z37bDlsr/ow/sT552FHLpHHkaVarWanljySWkoFFKkgDwGPmklK4WwTQCKjxiGgUQioXjAcrnUU1m73Y5EIoFAIIB2u61IFCM6FI4oMnAhpFul3+9jPB4r8sJaY4/Ho8rsbrcr5wKfpO/3e7lryB8hi+Wjjz7Cp59+ilQqpdgFAFWtk2tD/g5jUYxlEKbLJ8nAfXxjPB4rQgRAcSRCZQFIkKnVahJr6OZgrbDdbsfFxQW++93vwul0olwuI5PJSGDh4lksFjEcDhVfm06nisTM53M16dBxQIeF0+nEdrtFKBRSHTwZJfv9HoPBAMViUU/kQ6EQ7u7uMJlM8Pz5c9zd3ekJOXDvAmBkhEyO8Xishfi9995Du92Gz+dDq9XS8kkhgAwOsje45PO8ZNU33VB0pBCoygWS4tPZ2ZkWUQpwFJdev36NWCym15/wXjYgEfS83W7lZJjP56jX6zg5OVFMhLHA1WqFQqGgSB4ACQCTyUQtXF6vV3ypXq+HXC4n8S2TyagRbTQaod/vw+l0ilnF+OJkMoHP55MDzufzSewiwJutXZvNRo4WinY8r51Op2JddCORJUWRg667ZrOJdDottw3ryOPxOEajkQQC8oJYFU/WC6NybEtKpVJiNvG6PhwOKBQKWtjT6TSazSZarRbi8ThyuZzg0jc3N8hmszpvDMPAZrNRPLLb7WKxWODp06cYDoeoVCrYbrdIJpO6TsiKYmvacrlEtVpVix7ZUeTGuFwuQdx5XtBlSHAzr7f9fo/D4QCHw4H5fC4hmT/jbrfDYrHAZDJBp9NBMplU89lkMkEkElFzFKNC5BZ5PB7xx8gdq1aryOVysFqtcthst1tUKhUJeqyoZ0NVr9dDKBQSD4hgZcacPv/8c4mQFKYoHLPNizXlfr8fhmFIsKtWq2oyy+fziuxNp1PBjRmP5dckNJzizMnJCT7//HMAP43iknW03W4V46S4OBqNFMklM4qV7A9btMwxxxxzAOD1J0Xsx5Ov+zDMMcccc8z5Ocew2XD33yni9l8vYPVf/c5X+tzDeo3gzV7/7u4ccJj+6uK7X6l+fDQaodVqYblcCroJQKwbn88n4CsX0XK5LCHCMAw5R9xut35BJtOFH8OFOhAIoFqt4sWLF+JccHGaTCY4OztDo9GAxWKRY4BRIVZtr1YrRKNRzGYzcW+22y3G4zHi8TisVqueBjMSttvt5AhizGm9XqPX66HZbKrhiDEI4N7q7/V6FYVirIGQzocV2nz6Pp1O8eLFC7Fb2NDEKvTRaIRgMAi/36+vwcVpNpupHYbCAaMmdP0cHx9jOByiVCrJJUJ3CiG2XGocDofEkfF4jFevXsHn8+HJkydwOp349NNPAdyzgeg+IXeH7hRGklhjTvfUbrfDfD7H8+fPYRgGbm9v5Uiy2Wyo1+uw2+2qFff5fHj16pWAwhSB+LMzHsZYXSwWkyDAp/WxWEzNQVwcWUccCAQwGAzQbrcF36Wgx+jfer1GsVgEAP25w+HA9fU1IpGI3suHDBvGaejMWC6X4uCwcY3RMi6sdHBQmKRTiBEYxhF5vvA18Xg8ePv2rSJFjFjReUJWlM/nk2BIwefly5d4/vw5RqMRbm5u1PTDn4MxLP7/8Xisaublcql66mg0KicWANzc3MBqtapdiw4Yni82m02vZT6fx9u3b/Hxxx8rSnVxcSEmjMvlQqlUEq+IAiejYXToWa1WHB8fo1arSRRhzIzuuna7reuOjj8C2wlDpwOObVtsvaLLr1wuy11EwWW5XGI0GoltRVdhIBDQdUGBi+yqTz/9VA5BRuSq1eoj3hJwLzwahoFkMgngvpmu0+koUsiIITlXFMN4jvOex/OUNfaMvtJdRyGOMT232y1XCwHWjBLO53PFwhg/AqB7JmNKFKsp6PL+xRgqYfU8JxnVslgsiEajehDAxi02DNLVyepxRjoJdLZarbi6utL5xM/nsQWDQZ2XsVhM/CheQ8ViUdBtisKNRkPRVXPMMcccTuKHwH5iCjnmmGOOOd+k6T93vPPHGnYHDKcT4X90geDVHo4hkPi/vsJu+KtrO31nISeRSCAWiyEQCMjhwmV+uVzi448/xs3NDZrNJgKBgHgrdL7Y7XYJDgD0RJ1P9a1WK6xWqxpHHjaw3N7eKq7g9/vFtmk0Gvjwww+1JPKJ8na7Fa+DbSR0JAyHQ6zXaxwfH8sNkUgktHRQSKJrgzGY7XarRXyxWOD6+hpHR0dYLBbY7/fYbrfwer1q9mk2m2rtYb01l3EA4jvU63W5WigMcNngE/xms4nRaIRkMol+v6+4BEWocDiMfr//iNND4cLhcCjaNJvNkEgkUK/XUSgUsN1u0Ww2tWiylYrw4kgkgtvbW1gsFsRiMWy3W71+iUQCjUZDkZrhcIhQKCQXBh0CjUYDPp8PyWRSEN3D4YBIJAKXy6VWLS7XjDHl83lFobbbLVarlerOKRzy8xmn4xLMSu5Wq6WIBuHCZIawbvhwOAjCShfNbrfTk38ukuSeeL1e7Pd73N7eIpVKIRaLqZGr2+1itVqJAULBhpwZsktKpRL8fj9yuZyuoeFwKAGAbT681sgBoauB50owGJSY6Ha7MZ1O0W63FcnbbrdyWPh8PlSrVXFk6BrjOT2ZTCReRqNRVKtVxQHZ0kWBA4BcPITtsuqdSzCrnoPBoFhOXq9Xx9VsNvH06VOUy2Vd/wRm393d4XA44MmTJ3K7UMCKx+OKjpVKJUSjUdzd3SkSmE6ntfj3ej25bh46gOjkeugc3Gw2mM/nODk5QTgcxg9/+EO1OSWTSSQSCRwOB7RaLTWThUIhBINBNJtNgd1fvHiBXq+Hly9fysF1e3srp1goFEI+n8fFxQWSyaQinRSRQqGQnG4UKuLxOKrVKmw2m/gunU5HYg9dgoSHbzYbXTu8XtkwR+cY33823VEAmkwmclg2Gg2cnJwAgAQR1pIzXkhnHmN6fC/JR3ry5Ana7bYYS3RPEq7M6BMr2vl68DxidM9ms8ltw/Oh0+mIf8Nr3jAMiVGGYWAymaBYLGLy5cIVDod1XQMQU4xuukqlglgsJoGPgqg55pjzzRhfZYG7fuSf/oHmmGOOOeZ8K+awP8BXO8DVOSB0vYU1mYDx/fdhfP99WJOJn/k5lo+eY/QvfIzS//gjtP7bTxG8msHYAcaX2IBf1byzkMMllE4DPrEkdJJP0wmN5C/WdElQlAmHw2qKYr34arVCKpVSHIpuHfIfKJi4XC5Uq1VFkxi9ICiYSzDrn6+urpDNZhGJRBCJRCRaEKC72+0UN+LPwWaaRCKBN2/eIJlMwuv1SgCwWq1aBBlRODk5wXw+x83NjV4vCgXT6VRAaNYGP6y3Zn374XDQU/zD4QC/34/JZILVaiXnDZtiKLp0u10tKayDfihITCYTLJdLZLNZgaEbjQYikQjcbjdcLhd+/OMf49mzZ3pqXqvVBDcmR2e32yEcDiumtFgs0Gq1FE+iaEexbr/fIxgMolwui8HCZp1Go4FQKCRWDCMbjMWxUplP/Pm0PhwOy2HCZXwymeCDDz5ArVaT0EfWzEMxabVaYTAYIJFI6Dyk0yAYDEoUo2hAQPRms0E4HBZvh2BcOgICgQCGw6HiM7FYDJ1OR8spzxNWsy+XSxSLRcVICLOmYDibzeRqoriYTqe19E+nU2y320fLLmvmGQehCABA4FYKkclkUsDdXq+Hk5MTLJdLxXDosGOMyjAMCaedTgeZTAalUgk2mw2BQEDV5a9fv4bP59MSzAag3W6HyWSiBqrtdot8Pg+/36/3gDFAr9crtxxjUIyc0dlENxDbmMhMsdvtSKfTWCwWuucsl0u5eBwOB0ajkRqdJpMJ/H6/rke6pA6HA+r1upr1+LUpiIxGI6RSKcUuKZg8f/5cTkW2YVHMYwPU4XCQCEjnCcWZ4+NjfPrppxJZCWkfjUaKJvl8Pjm92JBFoY3xN7J3DoeDBF4KEQ6HQ+dJo9EAcO+yZCsXxamHrBiec6lUCpvNBp1ORzyhh1FMOnUsFgvK5TJsNhtOTk4EKOfn8D5BpxcbrHa7nbhXvB6CwSBubm50XlIANgxDwiZdNy6XS9cdYfaM8sbjcZRKJUVsKdxEo1E5SDudjmDtdDLtdjsJ24VC4ef5O9Ycc8z5NRrjdz/D6iLwc32NL24ziPxB8xd0QAaM77+P1v/gBzC+FNTNMcccc8z5Fc5+h+g/+ALpf3ADHIDmXz5F+Z8PoPzPB9D8y6ewfFkQojEM1P5rYYzOLIABLJIGyv+cH6soUP9vHsEai/7KDv2dhZz1eo1+v49wOCwwbKfTAQA1SNFpQbs8RRsuHKVSCcPhEJFIRGwX2tbn8znS6TSsVivK5bKWF/Jr2LLC+AFhqh6PB5VKRaJJKpXC4XDQL+nkOVgsFi2lALSUUzQhD4RuFC4GjGvwCS5ZLg6HQ8dyd3enp9NHR0eo1+tIJpNaKLj4c9l2u92KGdze3qJQKOgpcLlchs/nEyeIbg26daLRKHq9ntxLhNOenZ09EogouFEoikQiagtbr9dqpGIlLx0MT58+xXq9ViTu+fPn+nfGP0KhkBqpLi8v8d5772G73T6q7+aiz9jNxcWFmoG4fI5GIy1jhKkyjsbXq1gsYjqdCgBM2DNjOu12W46sDz/8EKVSSUvw06dPAUCLGXD/RH4wGKiBh5/L1/9wOMjhwFgduR8Oh0NNUzx3WNvOhfD8/FyOoX6/j0QigeVyCQBIJpNYLBawWCw6n05OTlCr1TAYDDCbzXB2dgaXyyVRkEIZ3TJ2u13gXb4P/HqMDbXbbTWFZTIZLBYLRVvomJpOp7p+OcFgUMwdiq6bzUaOHFZ0EwB9c3OD+XyOfD4vQDLw06rudrut98/n80nobTabYiC5XC4kEgk0m01EIhG1X7E9bTKZwOv1igMzn8+xWCwUi6KwRwGFwOP33ntP9wsKs81mE+12W+4a3nc8Ho+EY7vdjqurKzGZDMNQK1Q8HlfrUr/fx9HRkdrDKEAQ4Ez3EZ04Nzc3ElBfvnwp1yFbwrLZrGKbnU4H8Xgc6XQa3W5Xgovdbscnn3yCzWaDQqEgxs7DewRB4sViUYImwdLkNbHdLZVKyUHpcDjQ7/ex2WzQbrcFhLdYLGJgnZ2d6TVeLpcYDAYSkOnq+vDDD5HJZPDjH/9Y7hjDMNBoNCSiM366Xq8xGAzw7NmzRzB1CmV0g9ExSL4V/3556Dgip8rr9eoaJ/eJLja60s7OznA4HNSsRQej1WqVkzMajaru3oQdm2PON2vO/+YrfHGb+VN97nZvge8LJ7Y3dz/3cdhSSdz+jd/E23/DjdHHK7z9332E4b/6WzDs727tN8ccc8wx5+ef3XiMw2aDwRMblvGfwouXcQOtf/n9Rx+7/Et/Bts/gXW/cwLGlzv+r2LeWcgJBAJ6MurxeNSydHFxIfgpF2Q+XafDgOyTQCCAaDSKq6srQW3JUyA02W636xd5Pr1lIw2bZLiQ8sn6YrHA4XDAbrfDcrlEr9cTG6LdbuPZs2fo9XpaaiqVCnw+n9pkHrpsotEoKpUK+v2+nvKztpgOCII1KbRw+SS3JRgMolqtalHj12UjzHK5lPvm2bNn+MM//ENMJhMcDgc8ffoUvV5PleAUBaxWK4rFImq1mqJqk8kEVqtVUGC/349CoaAll6JKq9XS4sYqb4vFAqfTqSfUXDp5HHRfsOEJuI/XzWYzRVLozGD8iBwgOqXY4EVHC6MldBfxdSSrg8fk9XpVbc/I2mg0wmAwgNvtlvgEQMJKOp3Gp59+Cp/PJ7GCCxjjFxTluDwzijUejzEcDpFIJNSQ9jCKxyXX5XKpMY1tRzwHGV9hm9f5+Tlms5lqlqfTqX6mZDIpsC5FtIegatY6E3jNprjtdguXyyVBjLE0nlMA1AhFcYliHyNGZLvwHDkcDvB4PMhkMri7u4NhGLqW+L90gbjdbjVw9ft98UXYWpfP5/Hpp59iOp0ik8nAbrej1WohlUrpPajX63C73RLPKBLQ4cfzK5lMqi3L5XLB6/U+Ep7YrMafhW6+1WqFs7MziS/8+t1uV5+33+91HdHBx9eLbg0OAb506vHYnzx5glqthm63i3g8LjD1Q3GB54jVakU2m5WomU6nxbup1WriRZFPNZ1OUalU1HxHVxnr5GOxmIQ5nv+RSEQiy8OGqYf3DYfDofedLh4e53a7FVQ4k8lgtVqJxQXcw+5dLhf6/b5g6xRYHnKfttstarWauFSj0Qiz2Qy/8Ru/AafTqXsEId7ZbBZ3d3dy9DFuRScUX7NerycBiW4xr9er64RCJ51X0+lUf09RrPX5fPD5fGrqorOHLCTGUvk+kslzeXn5lf5SNcccc369Zzcc4fw/3P6pPrf8/y4i87/5g5/7GGzpFK7/6im2mTUM5x6GARjuHTp/cY3hX/n45/765phjjjnmfLWZ/eAM25+RjNrbjUf/vnMYgPHHPw4Aol+ssW38ghyb7zDvLOS02201VzESMR6PcXJyomWAzBI6XACoPhu4X6jv7u5k//f7/fo8CgME1dpsNkFaH9Yd8wkpYz50dORyObjdblxdXekpeSaT0TJKWHGj0cAPfvADpFIp1Go1/bKezWZRrVZxd3eneu3pdIrVaoVMJiMGSCAQkAjF5bNSqSAcDisiw4gLXTetVgvdbhfHx8ew2WxqK2IlcCgUEozY4XAI4rtcLtUCRbZEPB4XJ6Lb7eLo6Ehxg91uh1KpBKfTqZ+r3W7j6dOnAhrz4+jIcDgccjvx/eB7w6WS7h9CZ/ne0IXx5s0b9Pt9fPjhh9hsNphMJorh0RnQbrclygFQ5TejR2R1uFwu+P1+9Ho9QY2bzaYabVKpFCqVio6BLJGHkYtIJIJarSZhkMBcq9WquBjbkFar1SPeDJc+LttOp1Mxn/V6rWOdTqdiAj3khiyXS0ynUwyHQ5ydnaFcLiOVSsFqtcLlcmGxWODu7k7g3larhWKxKMcAwcAUS5bLJc7Pz3F3dwefzyfxolwuq7aatdGMa1FYnU6nqNVqCIVCyGQyKJfLyGazqnffbrfo9Xo6L3hesSmOVehc4JfLJZ4/fy4Wk8ViQavVEliaTiqKTKyi57VKZxNFYV5T/F8Ku4PBAJvNRo4jwzAU6Xr//ffhcrlQr9clYjGyY7FYFBW6vLxUXA8AnE6nnEUEOa/XawBQnHE6nep+MhgMJEbncjkJw2SsvH37Fi6XS8IdhUI2KvFr8Xpiy1kwGEStVkOhUBCfqVarod/v672l8EW3FQCJ6BROydUJhUKoVqtyyHk8Hp3D+/0ebrdb59tiscBut1PzVaVS0fW/2+0Ug+Rx8hqlCEYBdrvdIpFIiE01Ho8lWvGc5j1+NBohFAphOBzCZrPh6OgIn3zyie45vK55/d7d3em6Pz09FRybjq7hcChXFp1G5Ca1Wi3FY4PBIKLRKF69eoV4PC4eEO/pdCuxocvlcun6TyQSOncY/zTHHHO+WWP97Aq7/2HwnZ05jYkft/+PYxT/t58B+5+/yW6fCGNdXP3MP+t+9+f+8uaYY4455nzFsaz3wOHrPoqvNu8s5NAp0+v1ZJF3Op2KFPGJJqtcnzx5gnq9LkYOn+jTGk8o50MQKn/ZZwSIT3oJz6QIYLPZtChfXV2p9pi8ELoJrFYr/H6/nnpHIhHFgabTKfr9PvL5PNxuN3q9HlarFWKxmDgLdBj1ej2cnZ1hNptJ/DAMQ1XVHo8Hz549E++BYFCn0wmXy6XK8263i/F4rCfvu90OiURCHBM+VabYs9lstEzRSTEYDAQYpcOGS8h4PFaNMBftcDiM6+trOSceVkCHw2FF2+r1upY5OpseglgB6HUcj8d4/vw5er2ehLv3338fP/7xj+F2u+HxeAQ+XSwWEuLS6bR4J3QqsemLESK24xCcy/9PqPRwONTCmUgkBOkdDofY7XbiwBDiy4pjuqG4oEajUZyenqJUKsH7JZjq+vpawiSFJAobPDfovGGzFM/p/X6vanY6blqtFp48eSLIcDqdFjtpNBrJaeNwOARbZSwMAOLxuMRMq9X6CCpLjlAoFJKQOhgM9O9kolSrVXg8HgBQ6xSjKTz/fD4ffvKTn+D09FRtTWSrMCrE5Z0Q2oculkAggGazCcMwUCgUFI2iQMBoHK93/jlrsSl28JrJ5/Pi4BC+6/f7kc/n8fLlSy3jdPGxRt7pdCIcDotzw2p5AHrvDcPA559/jlgshlgsBuBePI1EIlgsFgDu446ERDPeRLYU26f2+z3m8zlms5kcW9FoFMFgUKJUqVSCxWKRG4SCZCQSQb1el0DL64H/zhgS4fBs9ZtMJor8UKCk03EwGEj0Y8yJEVG/3w+Px6P/32g0HsXa2ELGe8/DexAF6f1+j3q9jkwmI0H95cuXiMfjEkk3mw1KpRJ+4zd+Q+9BOBxGIBDAfr/H559/Lrg0YceM0F1dXUmAoQOToGeK2HSQFQoFuN1u3dfm87nuBxaLRTFF4N6htlgs5BgkCJvctvV6LXcVrznCmeki49875phjzjdn9rMZ9j95g+f/1i2u/z/H/38ByF9cZZH87/aQ+1/9LvZf3lvMMcccc8z5Zo3jH/0IjvEf/++B8rs5OK0rwNX41VWPA19ByOHySD4JYwaESVqtViSTSQwGA1X4sgEHgASOaDQq4YW2ebJogHuQrt/vR6vVQjweV1SJiwZFhfV6jXq9jlQqhcFgoCpi/uLt8XhwdXUlAYiLlMViQSqVwmw2E4Cz1+vh9vYWx8fHgpCy/pxiUqfTUcSD9n5GvR62c/FYyPFhQxJdJj6fTwtVNptFt9vFcDiUcLPf75FMJnF1dYVkMqk2JHIxyIlh/e5DwK/VakW325VzZTweqwWHT7EzmYxYGdPpVM6eWCymJ+5cGp88eaL3hGyU2WyGZDKp2EQ4HIbb7Va70mAwQKlUQjKZlDhjs9lgs9kexb1YXUwBr9fr6VwzDEPnWyQSwWazwXg8ht/vh9VqVVU6XR+MaoTDYTE16ORi1IwxlFqtpjrv2WyG9957D5vNBoPBALlcDre3t0in0/B6vWi1Wmr0Wa/XGA6HKBaLEsMYu1qtVgiFQuLkUMDweDzo9XrI5/OYzWZystH9Eg6Hsdvt5CKLx+OqGGdl+WKxQKfTUWMXGUJ0+XQ6HYl6VqtV7rPZbIbVaoXT01MJMoFAQI66TCYjwZGV5bPZDIPBQMfGBZ3v2du3bwFAgFngXgSpVCo4OjqS04c8FrZvsZWN3BWKaalUCuVyWYs+3+fBYCAAOuMzFGrYEDYajXS9Afd128PhUE1avJ4AiIXEqCKdUxSEGeVMJBK6lnlujkYjCSHVahWGYQhWzCYxOvSm06likU6nU5XYZFXR6UNQNuHNbOHjeUYhiABtcoKCwSDq9brOZUaPCJZmy1UymcRsNhPA1+FwKNrIdrhEIgGn04l6vf4INMzqecaVptMp8vm8oN50svB4RqORBM3NZqPjZVzS7/fj5uZGbWlsWKM46vV65fgZj8ePINcU9ne7HZ49e6ZaeQqesVgMw+EQy+XykTDErzsajSQKMeIVDocRjUbRbDaxXC4Rj8cVq+Q1xwYyNimOxz/jb3VzzDHnGzG7Xh+Fv/67yP5NC6b/bu5n/vPe3+hg1+3907/YV5nDAYf9n+DN/5P++y97LNaf/mN8Tcdgztc2tqMCbMdFk9Fkzrd6Mv/PDiKvdsAewB6IfrGD5z//9NHHBH9Yg3WBx+6dPZD7RwPsf/IGFo8H43/pNzH+l34Tli/3pV/WvLOQw3gUYZxsUCGfpNfrabk6Pj5Gt9tV0xDbXrioEaI6mUyQzWaxWq1UbU0nRzKZFHeCfBTGYLjE+P1+AXPZaBKPx8VXIKCZsQIKAwDUSkPOCeGhjUZD7BzW9DLqxMYfRhYeLj2MevCJtsPhUNPWcDiUWPIwCrRcLjEcDnFycoLJZILtdiv3ks1mE7thtVopKsUaaODeOUCYLXDfYuP3+3F3d6dlm6IP69bb7bYaqNxut+IrdPZwkaTjhAs1Y1putxsOhwObzUZOjGazKSEiEAggFouJkUPHEhd7un1yuRw8Ho+idr1eD/F4XE/SKay0Wi0Eg0HEYjH0ej01RFEM4RP9YrEocDTFLr42D5dbLp52ux35fB7j8RilUklA3OFwKEDrbrdDLBZ7xIaKxWJ49eqVYLWsG+92u8hmsxIsWdEejUbR7XbF3qBjgA6G6XSK09NTgZQZjyJfxufzaVEmhLnVagG4F1Ho8KJQCUAgXwByqVHka7VaODs7Q7PZRCaTES+I7BrCXwk27/V6mE6nCAaDCIVCYqGMx+NHvBUyh8hQIRCaDp9gMCjAuMPhkIDn8/kwHA6RSqUwn891rdGlM51Odb6TWzIej/HkyRMJTtvtvVJOyDJjlTwPXC4Xnj59qqgg41yz2Qz5fF4CI+HKvL75OjLaw2ueojNFRYoJFKl4rT48r6vVKtbrNVKplETMSqWi6CEhy8vlUmIrW6YuLi7kPCKfhm6ihzXvd3d3anvj+3Q4HNQ2xbjm+fk5AOjeORqNEI/HYbFY4PF4UCqVAEC8msFggHQ6re/Dc8xqtar63TAMHB8fIxaL4c2bN2LqUAyNRCJYrVZ6z1kV3mg04Ha7kUqlFHn0er1ot9sSG8kJewhq5rlttVrh9XolzITDYd23GdOzWCwC4NNRSkF2Npuh0Whgv98Lwsx4GkXwFy9evOtfk+aYY84/q/PDz+H8z/7wZ/6zvS39wr/d/uUl8v/QgsP68a/hlp4dT/8P1V/49/unjfX8BJf/++/j4m9/Dxd/+3sY/Ku/+Ss/BnN+9WMNBGD58Nl9xfJyhX3QC8tZ8es+LHPM+dpm9/oS3r//IxT/vR+j+O/9GJ5/8CMcNutHH7OtVJH99/8I+f9yCk/jAE/jgOJ/2sf+s9cAgM6/+BFGpxaMTi1o/evfhfVL7MMvY95ZyOGiyV92+/2+nAd8AkrXxn6/FwCVbUnj8RjpdFrCTKFQgNPpxNu3b+WMIXzTarViNBphMpkI9kl+yMOKcX7fYDCI2WyGcrmsdqPlconj42M4HA7c3t6i3+/rl/eH7TehUAiGYai6d7VaKcrF2JDNZpNYkUwm9fOwQpmNW1zyuNyHw2E9pX/69KkWB0JIGSNjRIT2f4KMCQleLpeIRqO4u7tTvCeVSsFms+Hq6grBYFCV7ADEpOBi5nK5FOtglIWRDzZwccnb7XZIpVJyuzBCMxwOH9WYz+dzvQY8Rgp6+/1ekR7Cebkc8z0mmJbiAcG0fB3C4TDK5TIASMjY7/dyrJAvQqcTnR9s76HIweXz+vpacF7gfhFlg1QgEMB0OpWI8fbtWy3CrFPP5XLYbrcolUo4Pj6WYGa327VkAxAj6ejoCJ1OB9fX10ilUuI+USBhlI7iH4G6zWZTzBm32/3IKfGQVcToUyaTkYuAYojT6ZQL7fLyUo4TRrUY4eP10263xVpyOBxoNpuPuFLFYlFw6uFwiOl0iuPjY1Wd83VmpIfsIpfLhbdv3yKTyYhdFAwG8cUXX8BqtaJQKOh+cnl5iXQ6rWuZ56LP50O5XIbL5VJ0iss8GTeMeAHQ9yiXy3KLsWKdr918PpcAxFgeBTyfz6f3yuFwoFQqIZVKodvtSkBwu93Y7XYIhUK4vb3V+cr6ef4Md3d3ePLkiVrmEomEGvYWiwWCwaAYRXa7Xa1WjD2xbp73NMYe1+s1Wq0WBoOB3IpOpxMOh0OAbYKyGRkjVH632+nao/uPX5P3Rn4/8rboFAoGg3JuNZtNOBwOtQTyWiE7i8K03+9HLBaTm4YRx4cupM1mA6fTqWY5AsT7/b74QgQvU/ChwM1IHxlFdNhst1u59MjyYWMgXYd0xHm9XsXm+v2+HGwWiwXj8dhsrTLHHHN+8bPfwf0Pfoijvwd4Xzpx2BtI/Rd2PPkPutiWKr/ywyn/t1KAbwvDtYPh2mH0BLAV87/y4zDnVzwOOyyTBQ7pGA6RIIxGFzAM2LIZ2NKpr/vozDHn65n9DvvlEvvl8k9koh1WK+D3f4Lof/R7iP5Hv4f9T9789M9sP/24ZdzA8nsnv7RDfWchh7+gcwEOh8MYjUYYDoeCU1KoILzSZrPh+voa77//Plqtlp42k49BC32v10OlUtEiFAgEkEwm4ff70W635WDgwsyID6G8BJNmMhktUsPhEJPJBPl8Xm6ZaDSKyWSiSITT6dSyANw/3eaCx9amSCTyqD2FEFOHwyFQrcfjQavVUusNo2PD4RCnp6dwu91aXv1+P1KplMSQk5MTNW3xCXq/38fp6SkGgwGazSaOj4+1HE0mEwCQs6lYLOqpN78mhSFGGDqdDur1Op48eaLFjxEdn88nJw5hvKxo5vvebDbF5qBDiUIUhS26f/i+UHTga/SwdYxw0nQ6jfV6jWazKRHqIYg4mUzKXcWPXywWcvbMZjOxlObzOabTKU5OThQ3eshpAYBCoSAxgq6IdrutJqrxeKyn/jc3N8hms4rCMDrCmMrd3R1OTk7EfYlEImi324rvNJtN2O12xGIxgbw7nQ5OTk4Uj6FTiS4kMm+2262gr9vtVpBXtguFw2EJFy6XSzEVukLIDSEfKBqNylGyXq91DlerVWSzWXGoSqUSXC4XotGozg2fzydOU6/Xk6NiuVwKWExGSzKZ1M9E19DJyQm63a4iSKVSCR9//DGq1SpGo5HAxw8bxeikODk5kbuJUR8AYsyQ3UKxMZlMChrM46Igezgc9LGJREJuJ4omoVAIs9lMTiwKqYlEQjBjDhlT6/Ua+/1e18Z2u8V0OtXHxuNxCTXxeByVSkWRPZfLpUYlCkpnZ2cYDAZic00mE7WB1et1Rcy2260YSJ1ORy1+6/VaEPV4PA6bzSZHG92My+USJycnYgzx2qJoy9isYRhIp9MSGS0Wi5x4FMH3+z1isZheT7qlCIFmfJPCLR1nfr8fh8MBvV4PTqdT5wbjiI1GA7PZDIVCAcvlUsDkbrcrgYr3e7fbrWs0HA7j9evXaloj12y5XCp6S2clHab8e4COHkbsCGx//vy5PtYcc8wx5xc9jv/iD5H7P36O9/6Xbfj/4z/A7u3V131IAIBNdo1dIvR1H4Y5v+TZdXvYlqrAmxscLBYgEtSf7ZMR2PK5r/HozDHnn82xLX76/73VA5y/9+ZP/uCfc95ZyGFjSiAQUMMInQDtdhv5fB4ej0dPNbmoAPeQ3NPTUy1KBG+SzWCz2VRLbbfbxY1hE5Ddbkc6ncZqtUK1WsVut0M+n8fhcMD19TVyuZyWT1rlE4kEDMNAp9PB8fExXC4Xbm5uAAC5XA7tdltPk7PZLG5vb/WEnzXTrCdmBIquJMJtw+GwYiPb7Rbj8VjATkbA+FqQAfIwykA+j9frlaOHT+ebzaa+Lr8/QbaJRAJ3d3dyx0Sj0UeNWnd3d1rS+LSbPwuXQS5tBKim02mJD1wwWUVO5xKXegCKWLDli44sMk3o6jg/P1cUqlarqS4+HA5LoAuFQno6T4GJriZCfAuFgvg2FotFcNKrqyucn58jEAigWCzi937v9xAKhZBOp5FIJOTGIlSabg6r1Yp2u41IJAKHw6FFdTgciony5s0bhMNhJJNJCZX7/R7tdvsRqJXcI77+gUBAziwus4yK8Fja7TYqlYrgtF6vV69dvV5HMpkEADmIttstRqMRLBbLI5g1YcgEBfv9flxfXyOTyegcYMyQIqfH44FhGAgEAri7u1OdOoHZjH9RkItEIqhWqygUCvrZGK+iuEJ3msViEdyYvCA6Zvr9PnK5HDqdjhhTHo8HtVoN6XRa33u1WiEQCKDVamGz2eD8/BzNZlMOC7Z6dTodtUZRRCVTJRKJ4Pb2Vkyu7XarmBsdKPP5HMXivYWYAOVIJKIGpEAgIPYRBR7Gr1ijzffH7XZju93KLfXQwUTRm6IwuUrAT/lTBHkHAgGcnZ2hUqmoOa3VaiGTyWC/34u7RP5Uq9WSiHd2diZXEYUfun9472DLHAWUhw42OpDy+bzuc4wDUuAhuJyRWMMw8OrVK6RSKWy3W3g8HjX5ARDDp1AooNlsqt1suVwim81iMplIrHO73bBYLIhEIphOp2g0Gnp/6dTj9eH1euUoYt34fr8XX2symYhrdjgc9N6QlcQI3Hg8Rq/XE0uNrlJyqAiXN8ccc8z5Zc1+MsH2rgwcvr66lMibLQ67n3JxbE0HrF2TD/atmC/dB5bhBNgf7gWd3Q6WwQQHjwvWWNRkJpljzleY8H/8Y0Rf7u7/+Tt/9EuF5L+zkFMoFFRBzEgQHQ+BQAC1Wk2LaDgclqATj8clzKTTaVUJc4lgcw4/PpFIyBlCu342m0WlUsFkMpE7gI4Nsj8ocLBKmo0yjBnYbDa12xDCudvtxN45Pj6Ws4QckoesFLJhKIAA9+LW3d2dnDS73U7uGeD+qfF4PIbb7UYmk0Gj0UAsFoNhGBgOh+L6sFWFFcqMlTxsrCFAmdyWeDyOV69eyU2zWCwUX3n27Bmm0ylarRaazSYikYgquK+urpBOp+USMQwD1WoVh8MBgUBALoJIJKJjYBU7eUNWqxWNRuNRXTwFHgpAZ2dnijvQEUEQKmHEbGAiHBWABDS6N1gNz2gGIye73U5AXrp0rFYrDMMQYJrH4/V6sdls0G63BYCNRCJyK7Eyu9PpYLfbafG32WxIJBJ4+fIl/H6/hCzWKDebTYRCIQDQU3+KDBQLGDci/JbiXigUUqSIrhRGdzKZjNgzXJ4Z3aJriAKjz+dTjIwAcgpxbrcb4XAYb968kQOJrVkPXWZ0LMXjcez3e7RaLcWc+v2+IjwPAeHtdlsRREYZGWOjqMRzjg1wrAtn7IruIAASwegoozPOYrFI1AQgxlEqlVIskVEb8qDoamNVPVuOGo0GAIiN02q19D0JYmajmd/vlxjB6BD5P4z08WswOkU3EN83xjXdbvcjNxvPD9Z0U3Ch2FqtVhGJROD1evV+2+12weZ57/H5fHIt0cVGZk4oFNJxUbyj8OZwOCRAM1pYqVSw3W7xwQcf4ObmRsKz0+lEMBhUlIrn+mazQSaTwWw2QygUksBD+DRdml6vF7FYDLe3t4KcO51OiYQPuTkUa+gIdDqdqNVqgthTpGFLG++XDxk+vC74nh0OB7nf6GojaJpNbKFQSK5ICnEEcbN10RxzzDHnmzzuf/iHePIfruH73InC37Xg/D9o/FLYQOb8+s62WsOhVIXR7OAQCQJWKw4uOw7ZhBmzM8ecrzCH1Qqev/8H8Pz9P/hjfJ1f9LyzkLNer+HxeBRBmU6ncpo8BAunUinxKAicPDo6wuFwwGeffQaHw6EFvN/v6+kxf5kmRJXNIpFIRPEeVtG22214vV7MZjMtI3TJ8Ek5n8w/5D+EQiEkk0mMx2M9zbZYLIqcUGxhbCiRSKDb7cLpdCKZTCqWRYGFjJXtdqsnx8PhEJvNBr1eD9/5zne0TKzXa2SzWfT7fVQqFTmaUqkUAoEAPv30U7mDPB6POEGMznAhcbvduLi4wHa7lWhE7kwmk0Gz2US73dbTcb5HFM5yuRzK5bIcTIfDQeIGY06MS7HpaLPZoNPp4Pnz56ou3u12ePLkieqSW60WrFYrUqmUYh7r9VrCDdvNGI2j+4LLHl0mrFS2Wq3I5/Po9/v6GEJ5D4cDzs7OUCqVMJ1OFdGiQJfNZnFzcyPIMYUFtqbZbDY95WfcrtPpiAPC1+f8/ByffPIJUqmU4luMYT0UyBgNslgsiMViKJVK4r9QlJjNZnjz5s2jRiSKVmSlELS93+9Rq9WQyWS0aDKycjgcJJpOp1PVuPOaYlU4nVzj8VjLL8/B4XCIWq2G+XyOeDwOv9+PUqkEt9uNyWSCRCKBWCyGq6srcakoXLFFjs4c4KeMms1moyYzxnBKpRJOTk6QyzmIErsAAQAASURBVOV039jtdqjX6+j3+zg6OkIymZTgRzdUuVyG2+1GJBIRE4v3HpvNhl6vh2w2i5cvX8Lj8SgGNhgMJOoyolWr1eB0OuHz+RCNRlGr1eDz+ST0BINB8Zfi8bgcHLvd7hGg+SGPyOVy4c2bNzg6OsJ+v4fFYkEoFEKn04HD4VBkslwu6xyh86TVaiGXy+GDDz7Q+X04HPD69Ws5ZXhfpMOFx+f3+xEKhTCfz9HtdrFarXBycgKXy4VOpwPgpxBgRkfpeiLQmMynbreL2WyGbreLjz/+GNPpFNVqVZFBOujohBqPx8hkMnA6nahUKo+imYwkUtil0MTvwRY8cosGgwEqlQrC4TByuRwGgwHG4zFub29xenoqV140GoXb7ZZzqdPp4OnTp5hOpxgMBoqQMnZFtxsdYXTZHA4HeDweRQIp9vHvBrvdLjGRDVfHx8dYLpcSp8wxxxxzvrFzOAC//xNk/tYP4fzP/xDbm7uv+4jM+Rpmv1zeN7Q1Oth7XDCWGxze3ODgccF2VIDlSxeyOeaY8+sx7yzkuFwuNfZEIhHFaBgXMgwD+/0eh8NBCzIFmlKppJjNzc0N0uk0Go2GnozSAcG2Fv4SzwWZT4YJvPV6veh0OuJFsM4YgGC4hITO53MBP+l4YCRiMBiojjcajaotiNXei8VCn0c3CD+WLhgCnckbYVU4IxqsJ+eyytYoAILZ0l3icDge1ZdTrEqlUopJEQg8Go3EjGF7ztXVFcLhsDgUgUDgEUNmt9upgYhPsymssJo4EonAZrOh3W4DgHgc5JFQHGBUg0/Z2X41n8/hcDjQ7XYRDAbFv5hOpwJWV6v3jQy53H32lg1GXFbdbjdcLpecInwfGMHy+/2o1+uIxWJijlCIonuBjUVsCJtMJqpH9vv9gqRSXGAshREpckBisRgOh4NcUQ6HA+v1Wk6KRqOBcrmMXC4nlorL5cJkMoHL5XoEM2ZFdDAYlCtmOp3KZULHGJvQbDabRBuyg+g6I7ek1WrptSerhe6e9XqNfD6v5X02m6lG++TkBPv9HqPRCI1GAx9++CHK5bJiSYZhaEFn2xfdUORBnZ2dYbfbod/vYzQa4cmTJ3I6zGYzRWTo2AsEAooz8nwql8tyMlH84LLNZruzszMAQLlcVpsVm/EouFBAoWOO1z5dfbyuNpsNnjx5gk6nI+cKYeCMKpJPlEwmkU6ncXNzIycdBeJer4dAIIDRaKRzezAYyHVFx+LJyYnEXb5XqVQKTqcTjUZDbi0KILwnkstCIbfdbivCOJlMxBpjlNJutyMUCkngo6BG0euh64pur2q1qtgiHTqEP6/Xa5yfn6NcLsPn88FiscDpdCqCdnJyohY5vi4AxJtxOBwCs8/nc+TzeUynU8HhCXemeEuhplgsKm5LAZfXx8NIGh1qbMVj05TL5RKPyOfzIRQKiTnkcDjQ7/fRbDbh9XqRzWbl+uI5yHsAzx/DMPT3hjnmmGPON30O5v3OHAC7wQBGrYWDyw5LIXv/Hy0W4CR3H7Uyxxxzfi3mnYWc0WikuuJ+vy9HBQG2XFrZAsIq5PV6jWQyKecOHSIUEvi0lq4aRhMAoNPpIBqN4nA4yHngdrvFm2Dsp9vtygXCp7Ner1fOnG63i0qlItbJw2Uxn89jPp+jVquhUChosSDjhBERwzDg9XqRSCQkRBAqy1gLADUzkcHAxhbWQ3OZ5ZNquobIzmAUi/Xhh8MBd3d3gg+Tp8H40Wq1UgSKcFA2gFUqFfj9fmw2GwFjWYlMdwnjBZVKBR6PR+6eXC6HxWKB0WgkfgbfUz6xt9lsWroYk2D99vHxsaJtFNpYdZ3JZFCr1TAej7WcPWxjAqBWJi6I5CrxaT9ba/L5PJrNJsrlMjweD4D7SFswGJSLyufz6bVgOxcr5lnnTlaIy+US4HYymagCnYLZ2dkZOp0OOp2O2plyuRwsFovEH4oHFLsajQYKhYLiNYwisZmHEHG6z1qtFk5OTjAajcSgofhTrVZVsUymzXg8htPpVBU0Acd0+qxWK/h8PhwfH+NHP/qRmpQCgYCEqel0CqfTiUwmg+VyKRYVW854vdDhBAAXFxcAgA8++ACxWAyvX7+WeECgL69PCjdsTEomk+LITKdTTCYTwc9HoxG8Xq+ECp5rdDOFw2Hc3NzofkA30mAwUCtYpVJBNBrFZrMR64bXfa/Xw8nJicQ9tno9dGbwGuTPRAEiGv3pLzBc8HO5nM7v2Wym6A8FTLLDnj17BpvNhkajgZubGwkiBC1TpODnPOS6UFBjWyCjajabTW41OsBCoZB4Sl6vV9f1cDjE1dUVEomEBPSzszMBhTudDvL5PLrdrsDAFE8JrybHh04dr9crXtJ6vZbrh3EqOvqazaYincPhUG1YvPYIFufryAYqApHZzLfb7VCr1RAOh+H3+/WzdDodDIdD3SsASKjM5XJieRUKBTmdeE5vt1sJZRQoyeBqNpsSnM0xxxxzzDHn2zK7wQCHNzcwNlsc7FZgtYZlsoDh88KWy8Ly5QMVc8wx5+ubdxZy7HY7RqMR/H6/BB3CTcljcDqdcuEQjsxFpNls6hdssnAY12JNNyNN4/EYx8fHqgU2DAMWiwWLxUJLy3w+RywWe/T0dr/f4/T0FIfDAZVKBaPRSMt+KBRCq9WC3W5HNptVlIRiC1kiXLpmsxmy2awcOWyEOTo60pJGkYAuFAAwDENuFC7Y8XgcX3zxBZxOJ0KhkJ4022w2zGYzjMdjPVF+KBSQ/ZHJZDAcDtHr9fDs2TMBeRkjIf/n9vZWolm9XkexWBTXw2KxIJ/P63PprKFrYrfb4ejoCJVKRY1foVAI8Xgco9FIjArgXiihm4Rfk/EfCntkCtGNYbPZ1PwEQE/PHz7l5/LGr03nwIsXL3B7eyuQrtfrRSqVwnA4FNCUcGuXy4VutwuPx6OFeL/fixVCF8dsNpNIl8vl5IBg7TABp81mE0dHRxJDLi4uFN2i+4eiCCMtFIgYI6ST6uTkRLGUfr+P+XyOJ0+eYDKZwOPxCGBMfs1oNJIbgnXVdLotFvdIdDJx6CQKBoOq/V6tVigWixJDlsulzjM6V+h4o+g5nU71cRTw6Liy2+24vr5GMBiUC49/znvBYrEQSyeZTGI0GkngikajEknpHKKbiE1GvGYqlQoCgQAKhYJiN2zXarfbioux5WuxWAgITAFytVohk8nIleJyuXStVKtVcakodoxGIwmrhOdGo1GJXbPZDMPhUGIi2TaTyQSRSETiBgUhgobZlkXXWzgclnOk3++jVCoJJk5hDwBsNhucTqeipIFAAI1GA4fDQQI0HWBsh3K5XIqhkflEhs7Tp0+RTCbx6tUrTKdThMNhRRkZNWw0GshkMgK507nGSnI28jFmxcif3++HYRj6O6Lf70tQpsDebrex2Wx0vfZ6PaRSKd1/0um04paMgjISOB6P1frmdDoVpXr79q04RGdnZ3rtWX8eDocl7pFH9rD+nU4x1qDvdjs5C5PJpJxH5phjjjnmmPNtm8NqhcNoAhwOOHhcOExmwOGAfSwI49jk5phjztc97yzkTCYT3N7eIhQK6ek9AP3STNAqW2oA6JdjLhv1eh3Pnj0DALRaLTkUACCRSMjZEolEBERm/Mfv96PT6aDb7co1AEDwUEYxfvKTn4iZ4vF44Pf71SRF58GbN2/w/PlzRZsISwYgVxFw77gha4ZP16fTqep+uRgy7sQn5FarFbVaDR9++CGazaZqzNPpNIbDoRYwp9OpOIjdbsd4PEa324XP55MTBYCeQB8dHQnQGgqFtHBy2eHT9Yd1z7FYDHd3d1qghsOheDTZbFbck2KxiLu7O6RSKcGiCSK22WwYDAZIJBJwOByKAFG4iEQiKJfLisyEw2G1XbXbbUWSuCSzpYwL/sP3fzKZIBaLwW63i2d0c3OD3W4nLpHX60WtVnvUaEPn0XQ6RTablbDHKIzf70e325WAxffX6XSiXC5LRAyFQjgcDorx0SXl9/slLA6HQ5ydnen9GwwGcpUtl0sEg0Gdb61WS81Jl5eXgoFz6X4ItA4Gg3KWdDodubDoYCNcmmIT29Uobo3HYy3avE7pEOK5TPFjMpnoGFgnTiAyuT3kG0UiEbX7kHVEsevh96FgSG5Vp9ORk43cGd4nKIKS+8KGNDr1CFdnQxivZS73FIFsNhs+//xztTmRxZNKpRT7abVaiMViAO4dbXTprFYrNUtRWPV6vWKykDdEBxj5PQ8hwgT3NhoNeL1eNBoNNe3x3phMJnXdUbBkZIcRQr5vsVgM4/FY7UoPr+HJZKJYFuNabMR6GH0knJ2xR4qobMD63ve+p3shOTfxeFzHzPsXBUaKXS6XC61WC6lUSmIY73cEO/P9pIBMAdjtdovbQxHcZrOh2WwqJsafw+l0CrBtt9sRi8XQ7/cRi8XwySefqOFwuVwiEAhIVKSA+lAkLJfLcmT1+31Bzim+8++RzWYjVlqn0xHLh5FIc8wxxxxzzPk2zm4wAK4rMJZrHLJxHJwOWMZzHKxWWM+OYfnyIb055pjzq5+v5MiZfVmfxYWbsRTybdhSRQ7OZDIR2DIejyOdTuP29lbuA3I6JpOJnDXD4VCMnGw2q+WY8MxcLqen49PpFJFIBO12WwtLPp+X24aiAx0TfIJOaGa73Razgssun16fnJyg0+mouYaLJOGxdDIAwNnZGcbjMfr9vuq7N5sNut0u0um0HBHr9VqcmMPhoKfPsVhMT30tFgtGo5FeWzJVWq0W9vs9ut0uwuEwarUa3G43ksmknjQbhoH5fC4HyWg0QrfbxdHREaLRKC4vL8XkoOhE50Wr1dL7R6cKocPVahW5XE4NXqFQSBDRyWSiZh06Cuh4oSBDZsxms8HR0ZGiM71eT/ERujECgQBKpRKSyaTiXlxgHQ6HWpL4Gvj9flVNk6/E+MtqtcJ4PFY0jy4UupOq1apEEbZSMSLCNqter4dYLKZl0+fzaRGczWaoVCr4/ve/LzcHa6hrtRo2m42ighRw6GLx+/24urqSE8bhcKDT6QiaHAqFxJ1aLBaIx+OoVqvweDyKDM1mMywWCwlegUBA8RK+7owElctlnJ+fq7Z5Pp8DuG8/IgcmEAggkUhgt9thsVhgvV5L/LLb7WLwxONxNdR1Oh0dK19Hvhaj0QhOp1PnGaM4XPb5v5vNBsFgUA1fZFDR7dPtdjGfz9Hr9fC9731Pjoz5fA6v14ujoyMAEHCbAPPD4SA3y2q1wna7hdPpxGw2e9R81+/3VRO+Xq/FG5rP5/D7/RK1gPvGKwprdM/wGmJUarvdYjweK7ZI2DJFGeCnvJ+HHCkyuCgwsDL95cuXiEajePbsmZxGs9kMyWQSpVJJPCyKiqPRCFarVdE0Nm2xZYycL54n9Xpdot1isdD9j0IPXS77/V5OODJ0CFz2+/2CSFO4paBI0QaA/huvLzozed4SIO7z+ST2M940n8/x/Plz1a2Tt0SgdCwWg8ViweXlpSJevLbI86IQxQhiLpd75KTja0WHI88Vc8wxxxxzzPm2zn42w7ZShaU7wsHtADZb4OoOcNiBk4Ip5phjztc0X4mRQ16N1WqV0MEYEeMSZKBwAZxOpzg+PkYwGESj0UCv18NkMpFrxjAMBAIBPe1n804gEMB+v9cTebaTtFotHQMhrnQDsN3J6/XK2bBarRTnIFSZv6izJv3q6gqBQEB8GrvdjlarhfF4jN/+7d9Gr9dDtVpFPB6HzWbDYrHA9fU1zs/PAUCxm+VyKXEGgECfD2Gh3W5XT8sZr7i7u4PVapWAcHx8LOGHUZD/X1YGK47n87lcHYw1dDodeL1epNNpMXwSiYQiUHQ93NzcIBQKIRKJwG63AwBKpRIGgwHy+bwiGz6fD8PhUEwfLoqMyhEYut/vkU6ntRARUMwn5QDEVmL9O0UluhJGo5EiZ4SYUvgjRNkwDLWYUZCgMyQWi0kQeMgHerjUUTyMxWJqKbNarRgMBoqIUGCyWCzodrsCFQ+HQ8WXYrEYdrudaorJvXE4HIowjUYjRcp4LrOyPBqNotfrwWKxiJ1jsVjg8/l0rtApwlppxgFXqxWOj4/hdDpVKU8WDWHNFFfovqpWq4qcsCnM5XJhPB4r1rJcLtFoNORYiEaj8Hg8j65Du92OL774ArFYTI47On2++OILhMNhcU3oQJlMJvjoo49UXU54+XA4VD034cP1el3ulX6/j6dPn6oZiqBcOpLq9bqgtalUCr1eT+wgsmEOhwOePn2KL774QkBsirsPrwm6xhaLha5TclzogqGDkK1XtVpN5z6h13wt6PQhJ6rdbsuFeHx8LCGM8GJCgSn8sr2LTrtyuYxisaj3lcIxod0UIWw226PomM/nU/16IBBAvV7HfD7H6ekpKpUKXC4XisWi2uh4P2YMFbgXnprNJmKxGNxut4DGqVQKjUYDo9FIAi/Fz9lsJsceWWhnZ2eYzWZ6rXlu8OMIWyag+OTkBBaLRa44RiMpyBEOzRY+crp8Pp8cZoS503U5GAyQTCbltmME9GH8lyI2AMHpzTHHHHPMMefbPNtqDUatjYPbCUsyDmMwhrHfwzjKmWKOOeZ8DfPOQs7HH38s0DG5AzabTcJGKBTSL9zL5VJCDQBFc/r9PjKZDAzDUDzq1atXWgDYhsMn3MC9E6bb7eJwOIjdMZlMcH5+Dp/Ph9vbWwDQL/SEAdNa32q1sNvtxE7ZbreKW9EtdHJyomW/VCohl8sptjQejxGNRgXJZUvOs2fPcH19DQBqfCHrh+wYRk0II2YMhkwf8mEYXWCtc7ValfOh2WxqkQeganaLxSIoMZkOk8lE8QSySjwej2JTgUBAXAzGS66vr1Gv15HP57XMj0YjRTeCwaBYHqwaZmvTzc0NZrMZQqEQYrEYnE4nbm9v5WYgW4fRt2azqdfhcDggGAwKfvswkrPZbCSOMT4yGAzQarVUl221Wh/xPSaTCY6OjsRuYoyLjJz1eo1qtSrAdbfbRblcxrNnzyQkOJ1OHSebvR5WzY/HYxSLRYF8yRGicyEWi6mlidEhcpn4PnS7XdTrdbkOKHBNp1M0Gg289957sFgsWK1Wqm+nk4uRFafTiXA4jHa7jcPhIOArfwa6ysgCKRaLqFargrmWy2WJq4zHTKdTMU3IuqETgU4swrUpVkwmE527fr8flUoFJycnEi/pzPF6vQCAN2/eKLrHGBih1YzgLBYLuN1uiZ2BQEA/M+NQgUAAl5eXigINBgM1TlmtVjVb0Znj8/lwdXWFFy9eALhnPFFoY2ySr/lkMkE+nxfHigLW7e0tCoWCwOqMmOXzeQmXXq8Xi8UCw+FQkHCv14tIJIJOp4MnT56g1Wrp2nW5XGpr4vvF+nO66fgaMFLI8306nQoePJvNYLPZBPENBoNwu91ybk2nU1QqFaRSKYzHYzx9+hTVahWz2QwnJycol8uo1+soFApIJBJ48+YNdrsdPv74YwmrbrcbFotFzjqv14tyuaxKewKTyfqp1+twu92Yz+cCTRNszUhhLpdDrVbTdbNer1Gv13V9k4lEjtJyuVSckZE9NnIZhqH3PxgMSiDk9UfWF91jvV5PYPhut4tUKiUXKSvJKaTy7xhzzDHHHHPM+bbPrtvD7uoOe78XsNmAahMwjHtnjglANsecX+m8s5DTbrclShwOBwCQ6OL3+1GtVsXNcTgc6PV6KBaLcoRQdOAv3rVaDQ6HAx999JEqoAkR9ng8anGhtZ1PoSnyUGQJBoNa+O12u4SPYrGI169fI51OS2ziE2o6IMgFWa1Welp9enqqp+wEDT/kwbBy+CHvBYCiKNFoVM4AcijoquCy32g0kEqlMBqNAEBP7PkkPpvNIhgMYjAYoNvtot/vIxQKSVwYjUaCMlPceNimw5rlfr+vCndydOgw4cLFBhpGNshmoUuG9cqEn0YiEbEyGFfj02wKGDwPCBl+WCdPdk0ikVDVON1Mk8kEg8FAyz7jJxQKuBT/43/8j5HL5XRuUOjodrsSAFknTCbLfr/H2dmZ4n6EXHMZ58I7GAxUwU6+DSu76UJiZTzjdYFAAD6fT66At2/fIplMqkmL7pNmswmfz4d0Oo3ZbCZI73A4xNHRkWDhZHVst1vYbDbFyLiEsmUJwKMIktvtlhhGV0osFpPL62Hb2uXlpeqxl8ulap7JJqFwxCU2Eok8EjsTiQSCwaBEPzKjGCckq4rtRRQd6IBiXIlxwFwuJ9dLOp1GtVpFOp2Gw+HAzc2NGvIoGJydnYk7w9fDYrGI30K3C/k9AOT8oNi03+/lHuF9zWazyVnGBqW3b9/KoUZx8OXLl3A6nTp3WV1ts9mQTqdxOBwkQtMdxZatfr+PfD6P2WwmFxchvH/wB38Ar9eLTCaja61arSKTyUik5r2ODYA8D46OjuT6obDB+1ckEhGfq1Kp4OzsTAI5Y5Hj8VhNgA6HQw1XTqdTPzfjZBSiKYRFo1EMBgNFyCjGUdhnUx+jjWyNesg82m63SCaTEiD5985yuUQmkxH8nPEsCleZTEb3l1arhXA4jFgshqurK7VukQU0GAzkinvoYPv888/FQSIjrFgs6towxxxzzDHHHHO+nP0Olu4AB5cDh6MMDrcVGLsdjNMiLObfmeaY8yubdxZyyAVhS8lisYDFYsHTp0/x2WefSRzhL8MPW64YJ2BsiREVxjK63S5yuRwOh4M4PBQJyEVZLpdqkGJlbSqVwm63g9VqVR30Rx99JGs+40JcoCKRCCwWi1xAZMGMRiM8f/4c/X5fTVDkgTA6xqgX4xPkrfAJu8PhECOGrgxWYy8WCywWCy39LpdLCzMhoaVSCYfDAfF4XKwXtrEkk0nUajV97Uwmg16vpyf+9XpdT835+jFOwYgMORb8dy7lLpcLlUpFUZhIJILBYKD4ERf2h3GbSCQipg2FKuBekCKLYzwey03AViWfz4f1eo3j42O02201xxDyTLAuW8kYYXrIGJnP54+abgKBgAQPAGInARCfhbENtlmVy2WBuekyewjvJqiX8a/1eo1wOIxEIoGLiwvVkwP3omU8HteCzePodDrI5XI4Pz/H3d2dWpMAoFKpCOTKuu7Xr18r6pNOp1UxT/Ejn8/j5uZGvCQKqOFwGK1WC9lsVrEgujucTqeq2KvVqngksVgMPp9PLBe3241utwsAAnhvNhvVa5Ozwtlut7Db7Vgulzg7O8NoNBKjyuv1qsa6UqmIN0P4OOHUFNfo8CPEeb1eo1KpiEm0WCx0vDc3NxJb6HZjjMxut+v65fXKemlysVKpFNrtNgaDgd4nRip5TBQbyHhhVJScF7KM2LZH1xnFoOFwiMPhgH6/L7GVogudbz6fD5VKRdE2CtX9fh9Wq1XuEXKwEokEFouFRL54PC73UCaTQbPZRDgcRr1eRyQSkWOFsGUKuZFIBF6vV0D1h7Bpv9+PXq+H6XSKRCKhpi9GE/v9vlw+LpdLION2u41kMilX4mQyESCY4mav10O73cZ7770nps5ut0Ov11Ps1G63IxQKSQziz9Hr9dTgR6g626ZyuRysVquuYXLF0um0mvXIiHoIde50OhK7KZoSmMx7D6vbAejBhTnmmGOOOeaYcz/bZgv7uyqM1RZGNgVjcF8kYhSzML78ndocc8z55c47Czkulwvz+RxnZ2d6qh4KhfD7v//7ODk5QTgc1jJQqVTg8/mQTCZxdXUltg4XI/4Zn67zafPd3R28Xi/i8TiWy6UiC9vtFoZhIJ1Oo9FowGKxIBqNqm2IgoTT6USz2USxWFTcYrPZIJPJoFqtolqtIhwOw+v1ot/vK87E5ipWCB8fH2M8HqPRaCCZTCoOwOWM9d782GAwiGazKYcQl5RWq4WzszO4XC64XC50Oh1st1tEIhGMRiP0+319LwJLLRaL3E+dTkcuD0ZHuECxFvny8hKZTAaNRgNnZ2dqiOHPz9gG/z85E3QA0XW0Wq30hJ7RGnIm+v0+Tk5OANzHUvg+xmIxLTkEVieTSS3fdrsdw+EQHo8H8XhcT+cHgwFms5mcE3x9crkcAKhli/Xyh8MBtVpNDA23261zkpDUfD6vZiXGnShKEMrMyA+ba9iUxEWOzKbb21uxQQiUJSyXsZV4PC5HDaMuFHKKxaJqlymYeTweuFwuiYh05DCyxKYyun8ohj7kD7HR6PT0FKPRCBaLRYsv44bT6RTD4VCgb8JqGXWi+OByucShabfbmM/nqn7nMY/HYxwfHyMUCuH29haRSAROpxPr9RpXV1fiz7DRqN1uS7jN5/OKpTzkl9Cdxvifz+dDr9fDYrHQMs8IF/lMfJ8pylF8ikQiAO4FxNlshkQiIWFlt9shlUqpxYmiI2HEjOzV63Uxt8LhsN5Luq9Ye77b7dBsNpFMJuHxeOD1ehUVLBQKuLq6wmw2g91uh9vtVhyIwuRsNkM6ncZoNJI7hucmz0ECjym6APc8F8MwxMoajUYYj8fI5XJYLpeYzWZIpVI69zqdDgaDwSOuDGOv5Cw9efIE9XodrVYLx8fH6Ha7chxGo1F0u11cX1/jt3/7t9FqtRRnovOJIGe/36/4EgVaturRrUW3jtVqRalUQiwWU/wxlUrBarUqDsoo52azkVPN6/WiUCig3W5LrKFjqNls6vPC4bDuuYSbe71eORVZdf4wOppIJCSOezweteAxcsW2Q3PMMcccc8wx54/PYbOGMZ0DhoF9MgJ0B8DhAEsoaDJzzDHnVzDvLOTc3Nwgl8sp3gHcx4nIPiB4kw6LhyBjtkvt93tEo1HU63UAUCyCfAoubXRn0Bkwn8/h8XjUPMInzGS6WCwWHZPT6cTFxYWemC+XS/R6PSQSCYRCIYkzfKJN1wcXazI72LBUKpXkIvB6vVrOk8mkFkbGDRjxIpCYLiJGdRiNeMjnWa1WSCaT2O/3WijpfqDQ8cUXXwgE7XQ6MRqNxHBgy9DZ2Rlubm4kVhDgy0WHsZLNZoN0Oi3IKkG+sVgMVqtVgGCPx4NAIKCGsMlkAofDIQfKbDbTkuv1eiVcrFYrAJCYkEgkcHNzg5ubG+TzecRiMTWe8bxhBTBZSaPRSOKKw+FALBaT+4lAay6SAFRjzNal4XAoCCzhtOv1GovFAul0Gu12W8yPXq+nOEe/39fyu1gsEI1GBVQlD4pNUfv9XgwmipoPj5uRF7rKCJIlL4SQacad6FbjecRK60QiAZ/Ph2azKdaT0+lEtVqVI4UxPTpaGIEkf4hOGA5FMp6LbJ1brVZih6TTaUSjUVQqFWw2G+Tzedze3spZlc/n5aDg5yWTSXGZttstOp0OTk9PsV6vMR6P9X7Q+ZLJZHSv4PvocrkUnySwmABcAIpV8r8zyjQajWAYBrLZLLrdLtrttkTERCKhWA4dgbe3t1gul0gmk3KmfPHFF3j+/LmYS06nU+cAr1+6wHiOke9EYWk+n0swpUhAwPLhcFD1eCAQQLvdlli02WyQSqV0XrJZipBxwrrp2jscDnKr0KXG6OLR0ZHuSbxWAKhpjU4zumV4P+B1a7FYkEwmBRym8EVhdLVaidFE9xDFa4/Hg0gkgmg0itFopNeRghWhw/F4HI1GQ04yvkZWq1XC3H6/f9QGRnYQG+AIsOf7Q+FxNBoJzk+IeTabxWKx0OcTxrzdbsWpokA9Go3w/vvvCwpN6LE55phjjjnmmPN4trU69rcVGIs1EArgUKoB0ZAJQDbHnF/BvLOQk0gkUK/X8fLlS/j9fgEuCSnNZrOqsU2lUlitVqjX6xJHuPzzSSdhws+fP8fr16/hdrv1BJ3LEQC1Fe12OwwGA4TDYYTDYcxmMz3V5dLIp/XxeBzT6VQcjEAgINcMYxedTkdOhlAoJEGEjhGr1SpnCJtWFosFUqkUwuEwKpWKGA92u13cEwB6sl4oFDCbzdBoNDAej5FKpbDf7yUOhEIh3NzcSACjG4DOBUbQ2ALENi8u5Q9jV5VKBQ6HA+l0WvyKTqcDm82mRY6vPfkZjC9EIhH85Cc/kWuDTqXr62u1uHBBqlar6Ha7KBaL2O12iuvM53PEYjE5bBi9YcSNNb/1eh3tdlvCGoWk73znO/jhD3+on5FV9KyM5/LKumu6OprNplwcD5fAQCCAzWajZd/hcCCbzapGej6fIxKJIBAIYDAYaAkdDAZ4+vQput0unE4nyuUygHvHz2AwkGOBzpNYLCaXBKvBa7Wa4lx0zgCQuJZMJvHmzRsJkYPBAPF4HA6HA7VaDYvFAoPBAIVCQbGmhwBmRmpYmUz3FQC9Vk6nU+9Fq9V6JDyQA1Wr1RCNRrHf71W/brfbYbVaFbXK5XK4uLiAYRhy2thsNrTbbQlLkUhEwler1YLb7Ua5XNZ5S+7Qfr9Ho9GQUMMlnZBgMmnIgKGjjg10iUQCAMRVYUMbRdFgMCiYOF0xHo8HnU4HhmGIg2S321EoFMRLIreLAmowGITNZpNQxYa7wWAAh8OB6+triY6LxQKTyUQOQwCKrT2MLtLtQd4Vo0d2ux3lchlHR0cCpfMYarUakskkrFYrWq2WgOKz2Qw3NzcIh8Pw+XyKfjGS2Wq18L3vfU+OMbrYfD4fgsEg3r59KwGEDBnyvyjiLpdLVCoVudkikYi4NDy33G43Op0ONpuNYlsUESmeEHpOJyTFsO12i36/r/szr2HC6TOZzKMmssvLSzgcDrx48QLValWwbDb3OZ1OhEIhxczYEkanEdk65PpQJKIzjQ4+viaff/454vE43G63KuPNMccccwDA4vXCdnKE23/nt3D5t34Tl3/rN9H8H/0Ali8fLpljzrdtDps1jMkMsBgwjvNAo4ODwwbD7zNjVuaY80ucdxZyWCMcj8cfcWFCoRBqtRq2260WrfV6rV+Y6RDgL9i0z5Pj0ev1cH5+js1mg7u7O3g8Hi3x6XQaLpcLh8NBld6M65AvQtGFCxMdIVz2U6kU+v0+qtWq7PO1Wg1HR0fYbreCpFKoocvE7XZr8V8ul7DZbHrqTLGBggWXKz4Bp2tmu90iHA4L3kwWCABFUY6OjtBoNODz+VRVTPbEfr/HixcvVDU+mUwkUgD3jV6vXr1CNptVG9Rms9EyMp/PEY1GMR6PFZOikycajapWfDgcIh6PY7fbwe/3K87kcDiwXC6RzWbx8uVLWK1W5HI5QY0dDgcajQbq9TqeP38uho3T6RSfhIyUfr+vxiOKe/1+X7Giu7s7Raz43wg//eSTT9RedHR0pAYzOkvodmF7TbFYVDSHbCAu3d1uV64Wxog6nQ4ymQz2+z2cTifsdjt2ux1KpRKKxaIibAQfR6NRNJtNAJDrif+MRiOxQihMEGRLnhMFwVqtJvcKYdh0YfC94BQKBSwWC8WuDMMQ94bLKJdbOnXoavD7/XA6nWi323A4HKjX6zAMA8lkEr1eT6BaxonIatlut7oOASgKRuGW0Zebmxu5LgA8Et8oOlIcSiaT4u+QVbPb7ZDP5/W5y+USzWZT1xcbm+iuCAaDj9rp6vU6jo6OMBqNVOFNkZWiTzabhdVqxe3tLQzDgNVq1bnD14IOQIprhDhvNhuMRiMcHx/jzZs3SCQSakw6Pz8HcF/PTQaWw+HA69ev4fV6YRgGotGomDzBYFA8LwpwD0XgcDgscSgajcp9mM1m0ev11OREphPdVtPpVK+Hx+NBo9GQQGa1WhXlotDOe/FDxw+FHLZGUZjhz9VoNMSVevv2LWKxmFhP5KBRGGw0GoKk22w21Go1eL1ecbe8Xi+SyST6/b5aohgB5D2E4guPiz+P2+2WuDeZTFAsFnXvZYMfXZiMUjK+1m635aCj4D6fz8VGikajWCwWePr0KUqlEpbLpRyk5phjjjkAsPvOOV7/z6PYptdAcAMEN5h8Z4Xav/GB2dpjzrd2to0mDqUaDnYrDJ8Xls4QiARhKea+7kMzx5xv7LyzkMNWKvIICNIltyUYDOqJMIUOsiTG47FECLfbjaOjIz3VJgthv9+rPYgA1PF4rDhBOBxGs9lUO4vD4VCduM1m0/dMp9NYLpdavPf7PdrtNgDg9PQUn376Kex2u9pm/H4/EomEXCIABFWmYOL3+1Vjm8vl1IJCCKjX64Xb7YZhGKhUKgLdjsdjpNNpLcUUwxgh4XLLKBZdBuv1GsViEYfDAZeXlzg9PRVriI6eTqeDQqGgtibCXQHofSArhqBecn34tBqAqn9DoRDa7TYMw3jkdODP6Xa7JbwxfhaNRiXIcOkl24duAzZKsQJ8OBzi/fffx93dncDIbA+j0BGNRvXEn66R8XisOAXr4xkdcjgcat7ieflQUOKTfdZQ033BhiW26zCedzgcHtUtu1wupNNp3N7eyqlBgYDHHA6H0e12FUUjbPrm5kZOLIoYBPZSnORrT34Uj73X6yma6PF4sFgskMlk8OrVKzgcDpydnWE8Hkss4uz3ex2fz+fDbrfD+fm5jo1LfyQSwXQ6Ra/Xk/vlcDjg9PRUAgwXaL4mvOYpCrAOPB6P4+7uTsfAn+FhQxrFPTrryEyig44tWev1WuczgcGBQEBMFwoVjUYDAHQ/oEjpcDgkxpGVQoE0l8uh2WzqfhSPx+WUoihEKDVZUoSmLxYLiUSEcZNBFYlEJHJdXV3h6dOnqlOnCBIOhzEcDuFyuRAKhRCNRtFoNBTD22636Ha7ODo6QjQaxd3dnV4zCuf9fl9MGQotrNoGoHNsv9/LQbTf7xUztdvtgtbTDROJRARgfljrzYjfdDoVLJjQ6lAoJK4Z3T6bzQaNRkNiYjKZlLibzWYlBK3Xa9ze3sLpdIonNR6PFX16+vSpHEaEqZMdxOY3vq90ij2Ec8diMSQSCcxmMzl5KNjTGTUcDhEKhVAul2G32/GDH/wAL1++FEvs4uJCYGreV80xxxxzDKcTd/9198/8s8kHKxheM0pizrd39osFLL0x9kEfsN/jcFsB7DZYY1HAYsaUzTHnFz3vLOSEQiHFqZxOJ7744gvsdjsEg0Hkcjk9vYzH4zg6OkK5XFakwe1262n/bDbDYDCQI4aRAS6qjHbQ5m61WtFoNNDr9fAbv/EbmM/njxgnZOxQ8OGT9EwmI7ZIOBzG4XAQjNfpdKLVaol5QhfOeDxGIBB4xESwWCx4+fIlnj59qsaa4XCI58+fo9Vq4enTp+h0OhIRstmsnAiLxULtKU6nE6VSCaFQCH6/X+4hRhYYOaGQQXDsdrtFo9EQL4cLVDgcxs3NzaOKYYpP5OE4HA6MRiM1CPFJNnkSNzc3ODk5wWazeRR/obvC6/UqpsXYGTlF2WxW7U88prdv3yIUCiEej6sNiA4Qv9+PZDKJ8Xisp+JsK2PzFB1AFJLovgDwCIxLNwedDhTKyPNgVMTtdsPhcCAQCOBHP/oR1us1Tk5OsFgsxHIhM2Q2m0kQaDabj1wPBAInk0m1bRHaytfmYZ09f5bdbodsNovRaKSYjs/nw2KxUKyIjpnJZKIKbEZVCNNlbT2B2++99x5WqxUuLy9ht9uRyWQwGAwkKpI3cnZ2hk6ng/V6jXq9jmKxiMlkAq/Xi91uh2q1imKxCLfbjclkomOPxWJYr9cSMihW0QG1Wq3QarXUuMR44MnJibhXfr8f19fXACDWztnZmfgxt7e32G63KBQKCIfDeP36tdrBCMONRqPodDqK2vHYKYTO53MJaBQveD3SFUjxgi1UBAlT4BoMBopjAUA8Hsfl5SXS6bRiiYxJ8X7ENqV0Oo1WqyWHYTgcRqfTkYuQLWzD4RD9fh+TyQRPnjxBt9vFZDJBOp0WDJhC5nK5xMXFBdxut64Nh8OhmFA6ndb58hBm7XQ64XK5HjnFHgKu6Z6iMEfhp9lsis3V6/UQDodRrVZxdHSkuCDvL4PBAPl8HsPhEE6nE7VaDfl8XqIor4vFYiGeDkVxwrRXq5WuLd5PAAgUfzgcJNDabDYEAgG5G71eL0KhEACgVCrJfUQ35XK5xPe+9z28fv0ai8VCQiZ5a4yJpVIpXWc+nw/AvaMqEolIhN/v93j79i0OhwMKhcKf+i9Yc8wx55s1htWKTWzzdR+GOeb8es7hgG2lisObK+yTEVhiURizBRCPwPr87Os+OnPM+cbNOws5ANQIQzcGANVfEzzpdDoxnU715zabTY4CxqJYHUwhyDAMVcIul0sEg0FUKhUUi0XxMux2OzqdDmKxmODDfNpNFg7ZDmwbKZfLODk5wWQyUd1uo9EQsPTi4gJnZ2fiUrA+mU4DPjWn7f/29lZLJSuSuWgSirper+UG4PLAhZPxHjarvP/++1qgttutnnyzpWa328HtdutrPXTIuFyuR9+DSxuhzFy8HwJvWdNeKBQEo+73+3otGSnbbreq6fb7/TgcDmrriUQieP36tcCgbrcbgUBA7Vfr9Vrg3dFopMgIQaxcrFar1aOGLLqIAoEAVquVOCUUDwhc5RP4o6MjAY5vb28VpaNDhNBZAILK8mdOJBISzCaTiYQ4ipSpVEocl91up/eYy3O73VY0g5XFFCoZpzo5OZH7h+8PF1pWHROgTMHK4/E8ih9RxGIshO8Hl/F4PC7mSSaTEbNns9mg1Wphv9+jUCjoe7HGmQ1ifA8egpd5jRKAPJlMMBgM5PiZz+c4OTnRuUfxkA6Zk5MTNZ0lk0m4XC7FwejM83g8ApszjkbGDOMuPp8P5XIZyWQS1WpVghwbklhJfnV1JS4Uwd6MBT0UZA3DgM/nU6U3IziEYvd6PQBQcxZFUN6LyFkBoHOs1+tJqCGwnG4UtkY1m02BiOlks1qt2O/34j2t12sJOw6HQ+weClq8n1IQm0wmgimTJcR7Rzqd1rk+n8/letvtdmi32zoPF4sFEomEIOvNZhPf/e530el0dG7QCcf7AsHJPN8jkYga9mKxmGJSNptNQO7D4YBAIIDFYgHDMJDP53F3d4fD4YBgMCjWWSwWw+XlJZxO5yOIN12PD4WlRqMhNtVisZATMBaLiR1EYH0+n0en05EoSf4a7zt8nRkFpsBssVjg9/vFCDPHHHPMAYD9coXY79p/9h8ejF/twZhjzq/pHHa7e2dOLAhstvcA5P3+3pljjjnm/MLmnYUcr9erZZa//D9sVeFTZUY2hsOhlpaHLTRchh9+3UqlIpcAoyuBQADNZhP9fl9PZimy8GvxSTMdGHza7vF45HBYLpfIZDJYLpdot9vI5XJaHr7zne+g1+thtVqJK3N5eQm/349wOCxordPpxGKxkHuF349uo5ubGzlGYrEYPv/8c4kajHWwRWs8HiuKMxqNcHt7i+PjY0QiEVgsFsznc7mKuFBSHGLDDeNIfJ2urq5gsVgUbyHkdjabIZvNKmZB4CjjWuv1WjEUuh3YlsMn19vtFm/evBGTiEIEAEUW5vO5uCusUOd7ywppNvNQLGM8gnyehw4DioOEuxIs7fP5FM8bj8eC7LL5hvXwd3d3yOfzmEwm6PV6avxi45nNZoPP50O9Xofdboff70ckEpGgQoHo8vJSogJFoNlshmfPnqHb7So6Q9cM3QIAcHV1JYB2NptVRITAYC7aHo8HbrdbsS1GX+bzOYrFouqcDcPA2dmZYLaMl7ExiG4IiiIUB9kQ5vF4kM/n4XQ68ebNG/F3LBaLPi6dTmMwGGAwGChOeHx8LP4IAEQiEbRaLSQSCdVQn5+fC3xOvlI8HhdAmk1FZCx98cUXWprp5qAbrN/va/nOZDJoNBo6zxgDJAOp0WiIBcQWK7rwgPtYGM8tgqGLxSIajQYMw8CTJ0+wWq10rzIMQ1ygXq+HfD6PXq+HQqGg95fRwWKxKAEGgDgzdIUxksOfn/GpzWaj2A9FN4oVPC8ppi2XS907yJFh/JQQeACK4yWTSYxGIwGDbTbbo/eX8c+H0U5ylMhGIk+GYiPh2gAEtud5HAwGxXxqNps4OTl5xK4ipyoSicDtdkv08Xg8cg/5fD6J0YFAANlsFu12G7VaTcfgdruxXq8fweEpRPHarFQqCAaDcr7x3sl2vqOjIzmOGLPb7XZYrVZ6ffh3R6fTQSqVksD3sPHNHHPM+ZbPfofYj0c4zP94TCT/n1iw6w++hoMyx5xfszkcsK3WcHh5gX04AMPvA/YHGAH/t5YjZdhssHz0HDd/87dw+K2PYE0mvu5DMucbMF/JkfP69WtZ6en4GI/HOD8/R71ex3q91sI3n8/hcDjgdrsFdF0sFvjud7+Lm5sbdDodxONxwThns5majKrVKkqlEsLhMIrFogQiNukwNjGZTATpJSSXDAcCZVlvTEdBv99XCwmjElxcyLggW4aOg4f11PxerJ02DENMkfl8ju12i0gkgn6/D+CnjBFWqlssFgSDQdUpM6LE70+wqdfrxatXrxTXuru7Q7fbRaFQ0GJD5kkul9Ni9pClQe5IvV5HNBqFxWLBaDSSiMJoz8Pa+M1mo6fWXHbOz89RLpfloOKT7HA4rOp3VmtfXV1hv9+LwZFOp7FYLBSrMgxDPBiKW/1+H/P5HG63G36/H59++imePXumhh8Acnrl83mMRiOJGKztXiwWODo6Qq1WQzgcFoPH4/HofV2tVkgkEuIyUfAyDAPxeFyCjNPpxNXVlXgobrdbrgC6XRhV8Xq9AO4jKvwZIpGIqrbJ5iBwO5fLqbFoPp+jXC7D5/OhUChgv9/D5/OpspmiSCKRQKPRkBPL7XZLrHA6nWqcWi6XuLm5gcPh0HXD6u+HLg2n0ylRjO1Fi8VCTjUykeim6vf7cpDVajUsl0tFYRgVYzyO0HJ+DwpLdEkxSsm4GCvf2UrGa8TpdErcIggcAHa7nRwTp6encDgcakCii4w8IHJzyLdyOBxyqdhsNkWyvF4v5vO5YLp0PdntdtTrdWw2G4kUdJvQiQXcu81GoxEGg4Hali4uLhCPx9WORMA6XSTJZBKdTkcORzpn2OJFUDrvOzabTW43j8cjNhR/JgraFDeLxaIcOPxcimeM0jFeFAqFUCqVdP/MZrNwuVziy1gsFrkieQ4Gg0Hc3t4iGAxis9lIyL+7u8P5+Tna7bbEQULWPR4P3r59i3w+L0g47x90OfH8jcfjcieSxfPw5yO7iKygYDCIzz77DJFIRPfSZrOJXC4nlw0dOXQU8do8HA5Ip9OPWF8U4xjbNMccc8zh7D99hfO/s0L49+7F9MPGgsR/aYfvVRf48u8Fc8z51s/hgMN2C8toCgR8gPXLlfPp8beSl9P91/4MrH9riKe/eQfHv9PGm792bDqUzPm5552FHFbwbjYbNTnRgTEcDvU0kzwRtpvQGUKBgEyEbDYrsOnZ2ZkcBPP5XK0qXCQcDgfC4TAmk4kgowQO0+lA1wwt82w6IbsEuK9QZzyCEQ7Wj7tcLpTLZdn6GdPYbDa4uLiAx+PRMscWHcJAK5UKPB4PRqMRAKhKnHERPlkme4ZLMwWnfr8PwzAQDocRi8Xw6tUrOJ1OHB0dod/vY7PZ4KOPPkK/38cXX3wB4J7lwVhIs9mE3W6XG4bsIX79YDAopgXBpO12W81JXKroSKBgQmAuF8+bmxu43W5kMhkAUNwsnU5LAKAbi8sja40Z0VitVnImNRoNJJNJQWmdTifW67UEPpvNhl6vB7vdjmg0ivl8LqGDosdoNMJoNBIkO5VKYbfboVAoaCmr1WpyHbCN6r333hPc1+PxCOJcqVSQSqWQSCQUkWPLF7/varXC0dERbm9vEY/H0e125QID7oGzfOrPZjXG0hhnI+Q6Go2q/ni1WqmKnO4sgm7ZPBQKhRQ/ORwOAoITeP3s2TO1LPV6PTSbTWSzWQlmyWQSh8MBjUYD0WgUvV5P5wcFPLozOKxUZ7PPfr9XLGc6nQo0zlhesVjE27dvEY/H5cyhY4eNSLxnVKtVOV/oFqGAl8lkYLfbtWATnByLxRSbIjw4EAggkUig2WyiXC4r3sY6dHJvGDNi/Xiv15Pzq1wuyxlCpxrvAePxWOIWXVMUyqLRqOJ/d3d34kERej2fz/Hee+/hs88+0zlIYdvtdqNQKKjVje+J3W6X+MCPo4jEeCWjfMA9kJ0wdYLNQ6GQXne6+fgzkbtDFw+byehiYcRsOBwqYka30Gw2ExSafB7e41wuF66vr5FIJCSysy3N4XDAZrOh2WyqNa/ZbGK5XCKZTErApLC/3W4VHYtEIhiNRhgOh0ilUnLlzWYzRb5yuRx+8pOfIJPJSHwyDONRXM1isSCdTmMymWAymcBms6kRjNFDr9cLh8OBarWKeDz+yGlnjjnmmAMAxu9+hvj/+cd49r8e4/m/20fw7/w+dpc3X/dhmWPOr91sqzUYyzUOdisOzvtYovX8GNZo5Gs+sl/djP7l34TvrzQe/bf3PigD8W/Pa2DOL2e+Uv04q2QpqIRCIS0rXKy4JJCZw5gKownkr7CRZ7vdotPp4L333lMEg+4V1mPncjmUy2U1ObGJilBZLlvpdFpP88mH4QLE6A5/qWcUh+BZRlcYG5tMJgiHw6opfvv2rRgubHCibZ/OnHA4jP1+r4Vvt9upipytNuv1Wq8NuUHz+VxxJIpf8/lcApjFYkGr1cKf/bN/VvXq/X5fYN5QKKQWHgKEya55yO2gMMKIE6MSrPhlLXgsFlPcjZXhdE6R9/NHf/RHyOfzSCQSchYAwHQ6VbtRoVBQdW8gEBDfhm4NtvREo1G5TsifoWhD7g9h0KyU73Q6EsbomOGiy4YwRj/G4zFyuZxeN/JFfD6fwNOM+4XDYbTbbcznc8WtyEmiyOj1evW6DwYDLJdLRTF8Ph86nY5iPoZh4ObmBofDAUdHR6oWp+thPp/rWCh8sbK+VCpJiOv3+8hkMlitVjq/WdVNwWmxWMjhkUgkEAgE8OTJEzSbTbx69Qp+v18OBjZ9lUolwWv5dbfbraDVd3d38Hq9KBQKEty4YJfLZfR6PRSLRVxfX+P09FTsmRcvXuD169e67uhcq9fr6Ha7KBaLqNVqODk5UW24y+XC8fEx2u226tDX67UEHdZy87WiiENHxxdffIEnT54gkUig1+vpzwuFAlwul8QYt9uNP/qjP4LVapUgStg3hYntdotSqfSoQY2tcrxOCXOn+4xwbAqadrsdpVJJIjedPZeXl1gul8jn89jtdhgOhwgGgxgMBmi324jFYpjNZjqeVCqFXq8n/hUbnejuo5hHWDzrwHkd8Z7GKONut0M6ncZoNMJqtZJYCEBxLzZh0VU0GAzk6KODjfcUOhF57wOg2BKFMwq8jGb1+31xcMjoAe4Fal5bFGgLhQIuLi5gGAYKhYKYTRQnnU6nRPPpdCrAczQaVYyQ7Wez2UyuNor4pVJJ7jq2ajWbTcUxO53On/bvV3PMMecbPIfVCrvXl9hdXH/dh2KOOb++8yUA2VhugO09Owe73X3c6lvizFnELPA5Vl/3YZjzDZx3FnK4bM3ncyQSCcRiMcEs+Qs7GRIUU1iVDUCshFqtpgXabrdLXBkOh7Db7YpQ0ZFCwaXb7T5yVbhcLuTzeSwWC1QqFeTzeQSDQbx8+RLAfcsWn7BaLBa4XC4BQdliRVs/G2fYLsQYVrlcFs8hEonA6/VKQFosForuOJ1OsWMWiwUGgwGy2ayiYIQJkys0mUywWCyw3+8RCoWQTCbRaDTgdrsVnbBYLOJOUPRhWxf5FPV6HdlsFsPhELe3tzAMQyDhaDSqtp39fi8nEl/DyWQiUG0kEpGLh6Bdxo/I0yDceTgcYj6f48//+T+P29tbHfNyuZT7huJHtVpFNBoVKLXdbss9ReHt5uZGMSWHw4Hz83MMBgN0u110Oh2cn59jtVphsVggnU7j9evXAIBisajYE1kz/Di6oxhN8fv9WC6XOnbDMGC1WhWdY3zDZrMpqnJ2doZut4tMJqOae3JyAKjpajqd4uzsTDyW/X6PXq8Hm80mFovb7RaIleIeo2FOp1NCTSKRwHQ61fkVj8eRyWTw5s0bgWXD4bCcZy6XS/EQ1qh/9tlnj+ruGTk6Pz+X+DgejwWLTiTuM7rD4fCRy8UwDHGTKOgxbkTmkWEYePbsmcTB2WyG0Wgk6HQwGITf75eIMhgMUCgU1HrF9iS+b3SjEI4L3HNZuHyTV3V5eQkAKBQKep3psAuFQjAMQ5EfwsbpiJrNZuj1eo+amwjPpYDVaDTg8/nkEmErGRlffH35flNs3e12SKVSaoGjA4pMKbfbjTdv3ui9arVa9zfhLwUZclrYYDUajdTSVS6XJRg7nU58+OGHGAwG+OKLL3B8fKwqe4qdANDpdCTU0MFD18zDiCnh8HT08L7F+GcsFpOQxns2uTG8hsjriUQiauXyeDx6LylWsrlquVwKjsz47W63kwDMvwvoxnrx4gVqtZpiURTIeX9lvIsiFqvpyV6j8HlycoKXL1/C4/GoYYsCN+PCHo8HiURCDCSKXOaYY4455phjzp9yGu17Z47bCbR72Id8sDj+BHD4N2wy/68+brqPY1QXv3sE1FpfzwGZ842Zd/4NlbBJq9UqFwMBuGSp3NzcIBAIAIAWyGKxqF/61+s10uk0arUaZrOZnnyTpUOwZ7FYxO3trRb+6+trMRjoKOj1emg0GigUCkilUnj16hW63S5evHghxwCrZck9IcSSbptoNIrlcin3Rblcxna7VZMJG7e+973v4fr6GvV6HWdnZ9jtdvB6veJK2O122fsZDTgcDooKBYNBRWVYLU1hhks5AB3HcDhENBpFsVhUhGE4HKLRaMDv9yOVSmE4HKpdKBqNIpVK6Sny3d2d4g1cxrkAMerBiA7rgRlfK5VKAsvS1VOv1+H1esWkMQwD1WpVzUA2m00NN4PBAM+ePVOtd6VS0RJMsDCPhXXoXJYB4ObmBolEAslkUi1mjPTRpUR3BM8XioTJZBLX19eCOu92O9TrdTgcDrx58wYffPABotGoXCWtVkuV8wDEJ0mn03p9l8uleC5coq1WqxxQbFEjzJZOg9VqJfcG3RZ0GTFalEql1MYWiURQr9e1iPNcORwO4t2Q4fPQzRQMBiUuRiIR5HI5NTAFg0EJSDc3N4qIkMtE8YKuidlsBofDoevG7XbDarU+ahOikOHxeBCNRnF9fY35fK6adQoRACToMn7l8/kwnU6RyWQe1VJTbOXPyEYrvsd8nckuodOMLVvpdFrLOq9ZAtFZqV4oFNBqteSu4LlNfhJFUMa/arUanE4nEokELBaLXDN0gQ0GAzmzKMg6nU4JELzuKR7z2u73+xiNRnA4HEgkEnLj1et1JBIJOcym06m4QovFAk+fPkWr1cLt7S28Xq8iXhRyDcNQZT2ZV/v9XnyuVCqF5XKpa5EMIafT+aiZ7eXLl/hzf+7PKZbJc3AymfyxWvrJZILLy0v8hb/wFxSN5f2NkafVaoVMJqP2NvKxTk9PFa+kiMfWNjJu9vu9WgcNw9C1BkCOwePjYzGCRqMRMpkM9vu9nKB0etF11Gw2dW83DEP3Qt5jGF+kw42xUHPMMcccc8wx508/u/EYh9EYB6cDh6MMjM0OODuCLZf9ug/tlz77l29w/G+N8cVVVv8k/miPnfn7hTk/57yzkMPlbbVa6Zdkr9eLzWajWm0yZywWC05OTtDpdFCv12G1WlX5zMpcxqzoZLFarWi1WphOp7Db7Tg9PUWj0VA1cr/fF9+FjorNZoN6vS54r9frVUyCT4QHg4H+jE08rPztdDqw2WyKBwWDQfFAuEzYbDa8efNG4kSn09Ev/WymYtyINdqMLIVCIQGE7+7uEI/H4fF4VJPOavFut4snT56g2+1qQQuFQnj16pWWn/1+D5fLpddqNpvh6OgIhmEglUphNpupep3vDdtrCDal64VgadYMc+lNp9OIxWKw2WwSMYCftlORTUL+Cxc1ukLY3EW+xXQ6xfHxsVxPdP40Gg3E43G0222sVisUCgU0Gg2BSofDIcbjMVwuF1arFSaTiRqbcrkcOp3OI3EwEolguVyKXdPr9RShI9SUPCBG4LicUWAIh8NIpVK4vb1FvV7Hfr/H+fm5qqUpFMxmM9hsNjUS8WdvNpuYTqfI5XKPRBy6GiKRCPx+PxaLhZwEdHGwmjoSiWC9XuPm5kafQ+GAjh2CX1kJTWcF32M6I8gI2e12cmcxakSBYjQaYTqdYrFYIJfLYTweK8pGwSGVSimGM51O5ZgLBAKKWtLVRHcD4ebBYFAV9GdnZxJW2NS2WCwE5CZLy2azKVpVKBR07OSnGIYhoYhV2JPJRDDvZrMJv98PwzAwGo1gsViQz+dRLpcRiUQkwjmdzkcxGzKm2u32IwYRxUK2M1GkZczw+PhYQqHH41FVOuNEs9lMr00gEEA6nRbUudFoSEzN5XKKBTEWRR6T3++XU4/16qvVSiIUBWkyXzqdDrxeL1wul3g7VqtVYgava/5MdBYyXtbv97HdbjEajZDNZiXmDIdDuRgBqI2NMUu+H9ls9tExdrtdnXdsAeO9ncLcarWSqEr3m9frVdyQjiTe9xl5m0wmqFarODo6QqPRUMNhsVgEcF8nz2ud7CWLxYLBYKD3nEI27908P/b7PVqtFsLh8J/m71ZzzDHHHHPMMefB7MZjGLUWYBhAdwBjt7v//9+C2d6V8eS/9yP94/2//cHXfUjmfAPmK9WPP4yXkM1Rr9dV880YDRkrfKpLF4jL5ZI1f71eaxkej8ewWCziyNBB02w2xYrw+XxqQ6JzgpEHLge1Wk1cCVY5z2YzBAIBjEYjOWbo6rDb7y19ZPEQjrnf7yUCsE43HA7D7/ej2WxquWJcgbEDLh582j0cDlWZnEgkJKLwqT0AGIaBRCIhBgT/e61WE/+H8a7JZKL6YcZxAoEAyuWyWBLFYlGxKcbcDoeDlinyMvi0nuIBQb2EozIqxAUxGAzqvSfwlk+8Gd3iUsr/Th4Il2XgpzXNjUYDwWBQ0RE6Q+hcsNvt4pwQkEtRzul0qtmm0+koZsRFmz93pVLB6enpo6WcdelOpxMejwfxeFxg0+vra/h8PokCrGDvdDqKDlI8tFgscpgQxhqJRARNprjB+unRaCQeC5lM/LrkpIzHY4RCIbWg8XXja+X3+9Hr9eSACwaDej8oPBJWPJ1OMRwOUa/XUSwW4XQ6xUwhWHk2myGTySimwip0LvJkotClRn4KOULtdlsRJjowzs/P9bNzMX/oxOj3+3j27JmWZwpdhNd2Oh08e/ZM5zWZTb1eT+comU8P3Vyz2Qzvv/++Yn6r1UpCis1mUyzK4/GgUCjA5/Pp6/d6PTlbGAEKBAIScRgXY2vTcrmUOPwQ5DudTiV2UuDM5/OoVCqIxWJ48+aNrjkyaKLRKLrdrirlQ6GQBHKKLHSRsLmLUGlyhHicbrcblUpFAiSFT7qC6AzivZT3QAo7AORuyeVy8Hg8uLq6wunp6aOWNF7Ho9EI3//+9zGfzyVujcdjgZtPTk4wm810v91ut8hms4Let1otpFIpdLtdNZhFo1EcDgdFNXl9x2IxxUIXiwUMw5D7h/cdOpFYe06mUigUUnsXr6vtditg/2QywWg0UmU6W66ePHmCUCgkx5455phjjjnmmPPzzW4wuHfjxMJAf4SD3wPrk9NvjaBjjjm/yHlnIYfRnWQyKffN4XDQL+CFQkFLP90U+/1e7TD8xZ/RGgKKCS7tdDrw+/2IRCKCHvNJKD+Ogg/bZtLptCIM5FuMRiOBVMfjsZYWRp0CgQBKpZIcKqxt3u12CIfDmM/ncjLwWIPBIH7nd34H+/0exWJRLAc+tWUMwGq1IplMYr/fy1nC2AafLLO1iiJSr9cTE4eOCQKN2dCTSqXQbrcRiUTQarXE+CBkdzweIxaLKWrFpYcLbr/ffwSV5VP7aDSK0WgEu92uphvCbtnIxapsv9+vRdfr9SKfz+OTTz6Bz+eDxWKRSMCln5DQh4vbfr8XV8Rms2EwGCgiROYNhTO6qyiW0WVD8CrFMzpTlsslgsGgeDaEvu52OzkchsOhooDT6VSiB9vTnj59qoro6+trRdL4+jMC5XA49PNSnLm8vFTdcTgcRq/XE/CVizmFQbaEUSxhXIquAgJ9W62WoimsMB8MBsjn87Db7ZhMJsjlcri6utK5T4GDcSUyeQ6HA66uruTWorCXSqWwXq/RaDS0CAMQh4TslXA4jLdv3yKVSiluwteSkT2CyykkjsdjQc7pzonH4wIV9/t9vPfee+j3+xLbKHJweaaQtNls5FChY8jhcODZs2cS+Oj+A6DFHbiP6x0dHYnHQqEqlUqJY1Ov1xUnJJTc4/Gg0+nA5/NJiODrYrPZdO3wfHa73Wp8Wi6X8Hq9mE6ncLlcqNfrePLkie5tdEjxc71er8Qv8oDYcJbL5dT2x9pvRsr49ex2O9rtNr7zne+gXq8rClWpVJDL5SRg0/1jGIZiWi6XCzc3N7BYLMhkMnLgJBIJLBYLcYsIeWes7KEQ/vbtW8xmM13vjJIuFgskEolHTj1G5hhhpAsrl8vBZrPp4UA2m5VgA9yzfOhM5HvZaDTkPqNQ0+v1cHp6qigoRU4Aek09Hg/8fj8mk4miVoxkud1uBINBtcKR82aOOeaYY4455vz8s78uwZjOgWgIRn8E2KywBs2HJuaY81XnK8GOnU4nWq0WCoUCYrEY6vU6+v0+8vk8BoMBWq0WIpGIog6EVTocDkQiEYzHY0U9WJNNcC9dLowh0M3C6ArjSFzCaYPP5XJihFA8IecCgFwTDodD8axQKCTAKjkXVqtVx8iqdIJT6fihS4CcE/JS2NQzGAzgcrkQj8cfuQeA+8W4Xq+LScG4iN/vRzAYlKDDmAnZGfv9XmweLv2sfXY6nRInAGjZI7zXMAy9dgAUFaN4wtiRxWLBer3Wws+Kai6uoVAI1WpVS9HhcECtVkOhUBAgmsteOBwWU4euLcYxttst6vU67HY78vm83BLdbleuG9bLM7bFyA3dSozFUSCjKEhANp0JZNCQ+9Jut/W0n5E3unMMw0AgEECr1ZI4lM1mJTgMh0Ocnp4ik8mgWq3qHEkmk9hut/cX0pfAYYoJ5Bp5PB4cDge4XC61gxHA6/V6JRwOBgO1GpEfReAtADkI/H6/ms3YmEUWEqN9fG32+z0ymQxKpRIMw9Diz/eWteasf+71ehIeyK16yGyJRCJy+rDumgIj6+cJ5KaDh8swAORyOTSbTcTjcQyHQ1VnU3CjANVut8XCIo+FzKVMJoNer4f5fK7joxhAlx6deHS7PWy4YiyM95BcLieRrd/vw2q16menyLNer8WEIky82Wzi6OhIIsByuZSAuNlsUCwWcXd3pzgo4dbT6RS73Q7FYlHvYSgUUt17IpFAt9sVRJvNegDkaIpGo4pAAfcuusVioYidy+VCLBZDq9VSzI2unXA4jFarpXMwHA7LHUVBmUKL3W6XM5LcIrfbDZfLhYuLCyQSCcHofT6fHC2j0QiFQgG3t7fI5/MCjVNomU6nePLkCex2u6Ku2+1WUdtsNovFYiHBnCyjaDSK6XQq5s/D2JjFYtF9+Pz8XPFDtrYxZupwOPS50+lUghmvsXQ6je12q/sGGwPNMcccc74VY7HCdlTA5f/l45/5z8V/8Gdg8XhM94Q5P9ccVitsSxXgcADs9nsIci4Nayj4dR+aOeb8MzXvLOTsdjs0m0289957ijlwgeOST9Ck3+9Hp9MRJLZeryMQCGhRKJfLqNVqsNvtePLkCRaLBU5OTlAqlR65dAiCtVqtaDabyGQymM1mSKfTekr96tUrFAoFNcm4XC6MRiP4fD44nU6JBM1mUwthIpGA3W5/VKW7Wq0UByPnYTqdwjAMLWs+n09L52w2Q6VSURtKMBhEPB7HdDrVElQsFnFxcYFQKITlcol0Oi2nht/v19ev1Wp48uQJAKDZbGK73SqKRYcM4ysEJ3Mpa7fb6Pf7Wh5Zb84WGFZwl0olBAIBuFwuJBIJeL1e/M7v/A6KxaLEGH5dcj76/T7i8Th6vZ7eU34uhQU6QQjDpivD5XLB5/Mp+uJwOARGPhwOcgQlEgnMZjO1PaXTadze3sLj8SCZTMLj8aBWqynCw2ad9XqNWCym9zgajaqCfLlcyvWwXq9xfHwMAIozXVxc4OTkRLGYQqHwSNgg64NV0Pl8Hj/60Y/EwGHEaDQaYTAYYL/f4/T0FH6/X409gUBAYgcFSgpTl5eXcjhQnKOrAIAih+TyVKtVLecEJ3e7XZyfnyt6QmGJ19d0OkU2m5VDajAYyClBoPF2u8XFxQW+853vKAJIKG2z2ZTTzOv1Pqqfp1jBtiCyf8h+Ib+ILpZEIgHDMHB5eYlcLod+vw+Xy4VisahWIkaAut2unHTtdlvV44QYU2ANh8NYr9dydpEFxdr2o6Mj7Pd7XFxc4IMPPlCskufU1dUVfD4farWa2pvW6zXi8bjqrVntnUwmAUCxoaOjIyQSCVxdXWG320m0IjuKMa7z83NcXV3JEbharcTJIRyYLUvBYFDRJzZs/eQnP5EbjMycbDYLj8eDt2/fyhlENw8h2hSAQ6GQ4qIUtiwWi37mFy9e4OLiQq19FERSqRSq1Squrq4UPaQAfHl5CZ/Ph7/4F/8iLi4u0Ol01PLUaDQkxPLeANw/BKjX63qdGAWs1WqqYAeAdDqNxWIhgDQbtjabjSK3Xq9XwjS/F+NbfHhgGIY4bgTO7/d7OBwONBoNrNdrBAIB2Gw2HB0d6cEB74GMwhFwXSgUft6/Z80xxxxzfu3HGovi4m9/D3/2H77FX/+v/MOf+c//4s//J/jN3xui9Nd/C8aXUVtzzPnTjrFY4eBzY+93w9jtcChmzPPKHHO+wnyl1io2noRCIS1V/KWaT8xnsxm63S7sdrtiCoVCAZ1OR06SfD6Po6Mj9Pt9xXlYVT0cDuXgYfyJC3Cv11O9Lx0vXBoYreHCRncMox/RaFQigMvlQrPZRCwWU4PLQ6At3RGBQACxWExfnxW1BBZToODCwAWQbSiEv0YiEcTjcbx9+xZutxvValVxDQ7jFIxg1Ot1/SxciGjzPxwOinU4nU6k02nVKV9cXACA6pPb7Tbm8zl2u52+DmM1uVwOpVJJT+WBn4odFJv432azGY6PjxXVonBCzgYbf7rdLpLJJBwOh4QfsnwIKi0Wi3j16hX2+z0qlQqi0ShCoRBCoZBiOR6PRzwSAFpw1+s1Tk9PMZlM0O125b4ii4ncEjYvkQVDd9XD+nmv1wuLxSLoNd9nHisB1vV6HcfHxxiPx9hutxI7gsGgRItcLieXEl1oZMw8dP2wMp6L9Xw+x9HRkeJub9++ldOD1x3ZUQAk6jEOeH5+LmfBarVCr9eT6DMejyUgEG7NZZ9L/dOnT9FoNMQTAu6FpEwmI7bKaDTCZDJRxIutYavVCrFYDG/fvpXTKZ1OIxqN4uLiAqPRSHBhfizbtRhpZF38p59++ujzKXJSeEilUhKlRqORqtjpJGPczmKxIJfL6Zw3DAONRgO5XE5w43K5LDHX7/crskOxjV+HEUcKhH6/H36/H5vN5pFrCLgXYG02G2KxGGazGTwej7hbXq8XxWJRLXcEFJNv4/P5dKx0RLG1DIAap+hmGo1Gasaik80wDLXItdttxcMo5BwdHSlSGo/HVc8N3DuiMpkM+v0+er2e4oCpVErnHI+HghJr1A3D0H2e7qVQKCRuF3Av6OXzecznc72+bMlyu924vLyUS4YuSwpz5BsRbjwcDuH1etHpdPDixQtMJhM0m02Ew2E4nU7M53OUy2Xk83nEYjEJ2GxQY/SOImC/35dYSSg6AJTLZcTjcczn80evlTnmmPPNmt1vfwzLh8++7sP42sew2fD6b5zg3/7tv4eco/cnfpwFexw72/if/ZW/i/L/5Hu/wiM055s427sy0Ll3F6PRNp1e5pjzFecrtVYRTjsej/X0mU+E+UsvAZwEdVLgYYuQ3+/HcrkUhJiRGy4cALTQDAaD+4P8cqnyeDyYTCZIJpN6qt3v98Vm6PV6agfiL/iEmPp8PgSDQbhcLrx8+RJOpxOr1Uo/E2MIgUBAywqFk+12K7fFZrNRvKJYLGq5oKBTLpcB3D9dbjQaOD4+RrfbxWAwkNuHldGxWAwul0tPiZ1OJ37rt34Ls9kMk8lE0FZGKMgNYYSJXBPDMNBqtVAul9WiQ1bJBx98oGYkRt0oYLjdbmw2G0XP5vM5xuMxnj17pqWLry8bk+x2u5afXC6n2MdoNMJqtRJXhYwLm82GVqslRodhGOh2uygUCuj1eigWiwgGg7i6usJisUChUIDD4UC5XEY4HEa1WkUqlZJ4MhgMFEthrMPlcglk7HA49P5QvCmVSlqoN5sNnE6n+DIUGij40W1lt9sVBRoOh4JCA1D0ijEij8eDZrMpxxAFg2g0imAwiN1uh/F4LCguI0RsmaLgcjgcJB6VSiVFC1nR3e/3VUfucrlQq9XEgmk2m4I+n5yc4Pr6WnBZHhsjWQDEQKnX61gul8jn8+j3+5jP5xI9ufROJhNxf3i+Mfo4GAyQSCQAQBX1fP1YX05WyWKx0Nd1uVy4u7tDPp9HKBSSwEkX32KxQDgcFgyZjU3kSyWTSVxcXChSlMlk5NCYz+eqp04mk3L60EnmdDrh8/nQ6/Vgs9nkxKIwQmffcDiUSPIw/kS2E3kyBHyv12tcXl7i6dOn4vmwHYwxJovFgna7rVghQeaMCFKoYvSOTsXFYiGHF8Xkhw15bJ1iBKnVakkQ5fGenJyoZY+iO++tvGb3+73uIZPJRLwmngtkbDGWGo1G5cQZDAY4PT3F5eUlPB6P3le+xvx+dMxRiA6HwyiXy9jv9wiHw+j3+wgGg+IN0RnV6/UQjUaRzWYFuR+Px3jvvfcEh6dY4/V6cXd3J4GOfy+Q3WOx3P/V12w2JUjxIUQymUQymUSpVFIU1xxzzPlmzujEiUXW/zP/zOL3w/LRc7T/zR+g/W/+AOt/7s/A+g1ssbMGAnj773+Mv/bn/9Ov9Hl/9V/8z1D5az8wHRTm/HxzOAAAjEgYuLiDJZ+BLZX8mg/KHHP+2Zh3FnLIFel2uwiFQlpIg8EgyuUyVquVIJHb7RZerxe93r2qX6lUEIlEAAD1eh2pVEotSKyS9fl8cqqs12sJQalUStEsuglY4Wy32xGJRBAKhXB3dwev14t+vy/3CXkqXN7X67WiEYy1kHnCJ/m3t7d6ok4mBBcPv9+PQCCgymfGQAKBgI7r+9//vhgytP2Px2N4vV6EQiHBZyeTCebzuSCfBPyyxpiVwKvVSuBhukH4WvR6PTFzKAoRwko3C+HHmUwG/+Sf/BPxJ7hYU4ggqJi8nnw+r8gOh1XgFOgAwGazYTabSVDg0k5HUqvVwsnJieDWTqdTEONCoSAhiSIM4zds5UkkEgKREjL8+eefa/kajUY6D6fTqaJ3PId2u51cQc1mE16vF6enp1iv17i7u0On08Hx8TEmkwnW67XOHavVik8++QQvXrxALpdDvV6XE408FZ7v9XpdcSAyeh66Jtg89nDp32w2cgkwxkTeRyaT0fnhcrnEG2KMqV6v4+joCLPZDHd3d3A4HMhkMnJa8RwnGJrxNYphPp8Pm81GzhzGH+lS2e12qNVq+vnodmFD12q1QjKZ1MeyRny/3ws+HY/H0e12dZ9Yr9f6vnSN0aHD6vpYLCYnFR01jCr1ej1dP2ThOJ1OVCoVHB0diZFE0LbL5RK/h6JSq9USgysYDKqRrNfrCZ7LamqCoCmM3N7e6n5AqHmtVoPf78fV1RW22y2ePn0qts5DxgqvNbptGKFj1I3xTzbWUXR8yKqh+McadcLd2ZDH14huM4/Ho6YtABgOh4pQTiYTeL1eXYvD4RDtdhvxeBzJZBLNZlP3eXKzKLgzHst73t3dHRKJhFrCyBsio4n37uFwKEfXwyaqer2OcDgsDhOjbIwkkhNFx+F4PJZo+ZA/BPzUNUrX1GKxkHg0m81Qr9cRj8d1HqZSKTHV6ETMZDLiRpH3Q7HaHHPM+eZN5P/0e3D+3//wj/13WzaD5r/2AUr/jTBWYWAVBlp/xo7SX30O64unX8OR/vKm8a+8j3/7L/w92I3tV/q8oHWG/+m/8nex/cGLX9KRmfNtmF2vD6Pexd7vhiUZx8HlxMHvhWF3fN2HZo45v/bzzkIOn4aOx2MEg0EtTK1WCy6XS6JIOp2WHZ4uHZvNhkqlgkwmo/pYVsmSRcK66/1+L1FjOBwK0MuoABtgVqsV4vE4NpuNWCkUTMbjsWI0PF5WMjMKQwgoIw0+n09cBb/fryXn6uoKiUTiUUMVvxbjLj6fD6PRSBBjPsGdzWYYDAaIRqMSfvhz8ok7cC902e12OYIowASDQT2NplBGSCujDATG8ik82338fj+i0aie2o9GI5ydnaHRaGiBpOOJzgBWWbMGmk/cKdLQAUJmCZ0Mq9VKThuKJBSGGD8jzJTMCwJl3759C4vFIiGGkF+Px6PXvN/vP3KEuN1uiYTn5+dyEvEpOyHLpVIJJycn+u9833q9Hnw+n1xdo9EILpdLogZjSxTwGPEB7mMu19fXiEQi4oZQBGCNstPphNfrRaPR0JLIpXswGMBisSAajaJer2vh5kIeDofx5s0buFwuwZsZR2HTlcvlwps3b5BIJHQcdKpRvOE1xoWcsRuyivjzlEolgW53u52AucFgUOcCm9TIj2H70Xa7FbiYHzMYDJBKpeQ2YtSHkUg6JChSrVYrxTXp3qJTjo1swWAQw+EQyWQSw+FQrUkUbiiObbdbNXgxQtdut3WdUVwiu4nMJgo/FDYpqNH9cn19jUKhgN1uJx4TI5g2m02xTF7fFF0ofLGtj/BnioEUx3i+BgKBR2403m/oEGSNNx2Lw+FQjkO6jCjMkG3zsB79oQuIMVO3262mNgp2jJpR/CbwfLfboVKpwOFwPBL/KJ6wIY6iK4WXhy6jQCCgn4sMGt5/CZV2OBwCxjNmS7B1t9t9JNDweuV7xYgl4fh0RmUyGbkUCeMmc4z3w0wmg7dv34qpQ54RY6fmmGPOt2fWZ0msfob55mAFeh9HvjEREFsqieS/UPq5vkbtv78BLNZf0BGZ822cXacDS6uPfdALS2+Ig8cJy1Hu6z4sc8z5/7L3p8G2JXhWH7bOPM/zdM+d35BDZVdVdlV1E0S3bXWARCM1Bj7IWLYA2aBAgY0MIRwabckQgKwACxOtELZxYAIUDgspDHyAMKEAVbayujIrhzfd++5w5nmex+0Pt9aqezsru15mZ+WrzDz/iBfd9fK9e/c5Z+/97n/ttX7rZ35eWMjJ5/NwOBxqdrLZbLi+vsZms8H+/r7qiNkgxR/YaWu32+2qx+biTPjkbWin2+0WW2O73Uo0Yj0xeTaTyUSLeqPRgNVq1YLIY+r1emKMPH36VGBbLlDtdhunp6eCu45GIzx48ADD4RDNZlMNUmzD4mIzn88FSGYcjE4kVveSp0KQLp/sOhwOXF1d4f79+wKCRiIRMSi22y1qtRr29va0ZM/nc/R6PcRiMUUV6ABhc1WtVkM6nYbT6RTQeDgciu/Q6/XErgmFQigUChLOKJ4QYssIEd0PrOXNZrOKRtBVwyfqjM2xWSedTstJwkWWcNlut6vYFaMOjHUQmppMJlGr1eB0OuWQYWSKT/kp7rHBiHEqOlcI4mbjWigUQjQaxdnZmV43+SR0kZTLZUWhKL4Q9svzhE1IhLYyrsOIjMPhQKFQkMuLsGGbzSZB45133pFYAgAHBwcYDAYoFAp33CD1el0Cjt/vl8NiNpup1ahUKiGVSkmMoEPr9kwmEzkWCJ4lNJgcEQBqsGKjEq9bOqwODg5gGAbm8znK5TKm0ylee+21O9E8xsjC4TCq1SpyuRz6/b6cbuQdUaBg4xzdXxSMnj17hkQiAYfDcUdAY3SGYh7PWwqpi8VCCzh/bzKZYDweIx6Pq42NTg4KWTw2ClW3GWC9Xg/ZbBZHR0d455134HA4sLe3h/Pzc6zXa2SzWdRqNTx8+FDCK4A7AGg6oHw+n4Dx0WgUp6enGI/HqNfriEQiyOVyquBm1TY5S4Spn5ycCJzNyvnBYKA4XqvVgs/n0/eeTqdym1DEWSwW4kMlk0nF9yhwMIpGQWg2m+H+/ftyyTgcDrGc+HVmsxkSiQR6vR76/T4ePHgAs9ms9qdut4t8Pi8R/LZQuF6v8bWvfQ3lclnOSn5Wy+USkUhE4iBw83Dh4uIC6XQaX//613XcV1dXqNfrCIfDePDgAZbLJer1uu6N5IwNh0McHR2h0WiIf8ZrbDgcSuSj+L+b3ezmqzGWSBjVX3R97H8f5U3Am69+jkf0Uxy3C/9q+n/4HX2JP3z6DkzmL4ewtZuXN9vhCKbVBkY4AHN/DMPt+NIIprvZzU9rXljIefr0KQDoh246XqLRqIQZ1sWSvUIALl01NpsNqVQKhUJBTJr9/X25XxqNBiaTCU5OTvTUncsdnxLTQcPmEbJwGF1hTKlSqUioYJuNz+cTz6bVauHo6EjxC7po6Pa5HSWhSLFcLuUiIjeF0afNZiMLv2EYGI1GSCaTgh9TXCBsmYuCx+MR+PU2C6fZbEogo5jCZhouhS6XC36/XyJav9/XcjqZTDCfz3FycqIliW4A1gMD0Ou83XjDGB1dHHQVUdCiWMUmLgpQHo9H4sZqtRJImpyWUCikz4nvMzk7ZNIwzkBXDN87ugm63S5OT09VZ2wymdDr9RAOhxEKhQSd5Z9n9TfPWS6QhBrTVUTost/vF1TWbDbDarWiVqupSajT6SiqRGGSQiMARalSqRTsdjsuLi7kFmE7EGvE+RoZDSSINhAI6BxgnMXn86FcLqNcLmO9XuPNN9+U6BOJRBSHoYBDlwLdcowiMTY3m83EE+JiDECRPZ5zjPgQuFwulxEKhSRkhsNhXFxcwGQyYTQayaGTTqf1XvNaz2QyKBQKctIQvkyXEcG5jM5QUCIU2W63o9frCW7MCI/dbtc1m0wmJeCNRiNFnFgdzvOGYHM6X9xuN9rtNtrtNnK5nGDKdEwxCvfee+/h+PgYo9EIw+EQuVxO8HSKNow+DgYDvTav16t4G3+Pr43nUSAQUEOZz+fDwcEBrq6uEAgEJGLynkPnCT+/crmMfD6vNkA6a1i3bhiGBCq6aFarldrgKGKQJcT4J++z2+1WzWoUh9lARScmXTKNRkOsq1qthkajoXss6815H6XTbjabIRaLoVAowO12w2634/DwUNdIoVCAyWSCw+HAfD7HG2+8IefR9fW1wMhsvqOzk/yswWCAeDyu95acr2azKeZVp9ORkLNYLPSeUeTczW528xUZkwnGb2cwMQGG5YV/fN7NbnbzArOdTmEUqzBMJhh2G0zLNayZNMw/dFXvZje7+eh8ItgxAD3RdzgceP311wVI3Ww2srfTpk7oKeM8XA7tdrvcHnyqHgqFMBwOtTBsNhs95SVfZzqdCtDKSEm1WtXSuFgsVFFLsCsZHNlsFtfX13oSzSfO3W4X6/X6zjKXyWQQDAYVL+KiQ0fBfD6H3W5Xcw/BoMlkUsJRNptV+wyXgsVigel0KgeMYRgSemj3Z/MKF6LNZoNcLodwOKzWGYotfIrMJZDMD9b4TqdTvPfee4jH4+LseL1eRda8Xi8Wi4UayAifTiQSEuqePn2qaFqv19NSyagU416sSebfG4/HcmJReOOybrFYBLzlk306JsjBIZMmnU5jsVigVqtpAR2NRmIo8bNgZMLhcKDb7Uq0oyjicDiwXq/ldprP52g0GnC5XAJVt9ttLBYLNfxkMhk0Gg3BfPv9PiKRCMrlslwMw+FQ4NhkMonHjx8jEonoWqFDhCBot9uNRqMBv9+v6BtFLrvdjkAgAAA4OztDOBxGJpORwyeVSgG4ceOUSiUtxT6fT1XTdDjwPaLbKxKJaHmfTCZycBBiPh6PsVqtUKvV4PF4kE6n9R7Rleb1euH3+1EqlXSdsVHKarUinU4rtvabv/mbEgRyuRwuLy/R7/dx//59OYcYCbstJqxWKy3sjNnwnLfb7YhEIri+vkYsFhPvhpG78Xis1+twOPTZ8bOnyMxhrIlV5+RSkUFD1yHP3dFohEgkIjAxP0+e40dHR/j+97+P6XSKYDAowDirxumAotuPAvDtenHgBqYNQJwlDoU3xt94b6GYzPsrY5qtVgvJZBLtdhtutxuJRALdbhfhcFjRR8Mw0Gg0xCaiw5CiEUXQ8XgsPhgdVrzvMxZI9xrP/dvRPDrgeK+lyEfIOKOuPp9P945CoYBwOKxzknDkeDyO99577w4/jS1nbIcLBAKYzWYoFot6byjMNxoNhEIhhEIhnffATUSW/wb5/X7Vzrfb7Rf9Z3I3u9nNl2E2W1gWH/+fTWvAPN9FLnezm896tqMRTNstTKs1jKsSthE/TD90Ru9mN7v56LywkGO1WmVb52JP7gGdI3QcmM1mdLtdPTU/Pj6W3Z+LeCqVEsjT7XajWq1qaTCbzbh//z5sNhu+973vKbqTTCYxHo9htVoxnU7VXkLXDJf77XaLYrGo/0auTCAQQCQS0VPYer0uRgK5NaFQCJPJRLwPMiUcDoeWTzpQGJOhAFEulxEMBsVFIXuDT4b5BJwV21wY+ZSe/z8dCwTXTqdTtSZNJhNEo1E5nsiXYYNVu90W1PTo6AjRaBTvvfeenFGBQABerxe9Xk8Lr8/nUz08XzuB1larVe4Q44dk+e12K97KfD5XPXK/39fTdi5edB/Z7XZUq1XxdtiUQwHIbrcrckJ3TaFQwHA4RDweRy6XQ6fTQb/fV7yJka1kMoler4erqyskk0l4vV4t1uQGffDBB2r72m63ijSZzWbxm8hHIdCZ0Fs6IOi4SCQScgCQNUTnUiKRkABnMpkkAoVCISwWC1xcXCCbzSKZTMo9wWptCjRcbCm00U1QKpUQCAQkzLHpazabwWKx6JrqdrsIBoNyHDFKRTGEzhAurGyWo6OC9dCRSASj0QhHR0dqcWu1WlqSb7dtUZzjIh8Oh3F+fi5xJBAIwGw2o16vq2WJIgCvb37vyWQCi8WCk5MTDAYDjMdjAXbJYKHQdpsb5PF48PjxY3Q6Hdjtdjx48ACj0QiNRkOfHeNAXOwptgFQhObi4gK5XE4xTopJnU5HEHQyYxgv5OfHz9lkMuHk5AQXFxcAbkQYXl9vvPEGLi8vEY1G0el0VAdOEaler0toIQOGjsHFYoF6vS5oMaNULpcLlUpFrWQU1OlUyuVyEoYuLi4UBVyv12LKtNttMXg2mw3ef/99XS/RaFQRJ/55fi/GbSl0875EPhrPNbPZjHg8Lu4V33efz4dms6nYaiqVUssf30sKjqVSSaK9yWRCKpVS3LDZbCKdTuP58+eKxTI+SUGS7CTeg0OhEKbTqa4Zns+EpQcCAcXHdrOb3Xw1ZtPrIfndj49U+kpbGO8++hyPaDdflDH7fNj+rjfu/DL98OfN3bzYmLoDGDYrzLEozKMZTF73z2TEyhIJAz//2ss+jN18xeeFhRzCbFerldgXrPSezWZ3ohSbzUYNKuTDJBIJ2eQZ12i32wKjsv0kkUhgMBigXq+LQzEYDOQmoIgRDAZRLBbFJeGSWq1W5T4Yj8c4PDzU8kioLP/+fD6H2WzGdrtVdIMWf4I56TIajUYSXgg8LZfLEkj8fr+OZbvdYj6fKxJCrg0jGLlcDtFoFFdXVwKu8j1dLpdihFitVuTzeSwWC5TLZdhsNhwfH2M8HqNarQrKzJgTXSysC6bolU6nUa1WFSljVIZV4dvtVk/rKYQtFgv0+30tcnxS3263xSkZj8eqep9MJvq7R0dHqNfrqNVqyOVyKJfLMJlMOh88Hg9CoRA+/PBDrNdrJJNJ1TLzyb7FYhHPZDabodVqIR6PY39/H7FYDM+fP8dqtZIYxUWT5w2FF/I8fvmXfxlXV1dyUNENUavVEIlE9Hk5HA5EIhG0223M53OJcnStsImInzHZKvP5XAsnuSR0alCkcDgc8Pv9qptntI5V82zvmc1mWC6XinoAkChIJhJZK4lEAqvVCp1OR3EgOjPoSuL5PRgM5Pih44LOMLqEeM4Tkp3L5TCZTFAsFuV044J+enqq42m325hMJsjn86pOp0gyHA4FuKZLazAYyGHSarXUtkRxyWKxoFqtCiZstVrlnojH42g2m3fan1jNzmjMYDCQiEDnE50liUQC6/VasT22Z1GcpIuGcSo6+ijyEe5MIYJODrp0WId9fX2NYDB4B4zNevZwOCym13K5lLuNzj++NgpkjIXxa9Ft1e/378DXyWLidcuY1Wg0UvtTKBQS2HswGCjqmEwmxbXiPYHXDwHAbrcb5XJZoPnz83McHR3B5/PBbrejUqlIkCFk2e/3IxgMot1uS7Cha5BtWHRQ0q3D64sOKAqabKtjEx0AAa0plCWTSYGhKa6n02kUCgW1YS0WCz0QiEajcLvdgitvt1uJpnyAsJvd7OarNbYnRbjrBkybW7+5BSwLIPLPKi/tuHbz2Y/JZr/z61N/HasVnT/wKiq/7L7zq/OvvQnTN3bNXi8663oD6I+wDftg9AbY+t2w5nMv+7B+NGYLOn/sO3jyF45Q/LPA+V/7Flp/4jsv+6h28xWdTyTkMJLE5YKxCLaN0K0yHo+1MLK+my02q9VKy2kkEkGv10Oj0cDrr7+O8XiMQqEAq9WqJ8psoSJbpdvtqq1nNpthNBqh2+3CarXqyS6fFLMBJplMwufzwWw2o9PpwOVyyTlCvgaBsSaTCfV6Xe1DZLCwjYbVw+VyWa0pw+EQwM1Cx5gHl3mKBGw3YpXxaDTC8fExfD6fomeHh4ew2Wwol8tqc2m1WqjX63L/sAnrV37lV/Do0SNBbH0+n7gs2+32jkhkGAb29vYEU+Xn0Wq1JPrw6TSbvsLhsJZhOj64eHMpp9uDSxyFsGq1iuPjY1Vch0Ih9Ho9uSJGo5EWdi6bjB5FIhFBfhlBY9SEDi6KXQDUaMRmKEJ5l8ulRAyv14uLiwukUikEAgFVeVMYiUQiePz4MQzDQDgcxvX1tVxkhHJz2HzDRXY2m8Hn8yl6BtzwidLpNLrdrlgl/H3GAwFI1PH7/XKbMZrk8XgQi8VQrVbvMG6urq7gcDjEf2KkJBaLYblciv3BymQ2CgEQQ4SQ5X6/j/l8jkwmg9VqhcFgIJ6PzWYTeJosLIKwGRWkY4ERnUQigUePHmlB9/l84hixqpwCCB0aFOKsViva7TZsNhtOT0/lgqJDr9vtIhqNCtB7//598VTS6bRESraNscGI0ObNZoNYLCamDCN9ZOb0+33YbDaEw2Fst1ucnp6iVCrBMAy4XC6cnZ2pVYuxLjr+QqHQHWfT22+/rddPHst6vUY+nxeMmtfG3t4eer0ecrkc1uu1omp0OdlsNtjtdpyfn4uFxDa5QqGAbDarexkFULfbjcvLSzmYGGWlU4bvDwVKuqh43yXwmO6a4XCIdDqNSqWCWq2Gw8NDVCoVCc0EtbfbbTGeRqMRAOjeRPErk8mg2+1KSLndEMVjp7OPUSyKoOSC0ZGVTqfVWDYej5HNZjEYDNBsNnXNsPJ8sVggEokIJp/JZCQG8vq2WCxq5vv617+Oi4sLsbZ2s5vdfLVm0+ki8jffxv7frcJbNOAtGsj+0wky/+e3sS6UXvbh7eYzGvPr91H6333zzq9PK7qM/+VvYLz3UefIJGPC6MgH0w9/lt3NC8xqCdNkDsTCME3mMDyun433z2xB809+C9H/WRGvnJZxFGvj4etF9N7YwJpJv+yj281XcF5YyHE4HIr8kDHg9XpxdXWl6BEjNYRsWiwWtNttpFIpiRlc+LkgsN3FbDbD6XQiEAig3W7L+k++hM1mw2w2uwM75oLKdhVWcrdaLQAQD4aRo0qlgng8LhEAgOCeBwcHKJfLWK1WYnew5trlcglcPBgM9Hcp4BDOTFGITgyKX3a7/Q4kN5lMCljs9XrlILq8vBRfiE/m6S7h+1ar1QTH3dvbg2EYil/wF501FNtCoZCcOHRUzedzHBwc6Ek+3SJ0iPAp/Hq9FpuGQsztini6FsjAYHysVCrB4/EIsJzP5zEcDtHtduFyucQMGY/HCAaDGAwGd6DBbrcbk8lEFd2MMxFSDNywYviaKSjQAUDHx/7+Pi4vL+/UXpPHQhFkPp/j9PQUVqsVvV5PVcqM2pBH4vP5sL+/D7PZLLGCLV3k2xAae3l5iXw+j36/r+PheUy3jtPpVJ02HQxsr9put4oDlkol5HI3TyMomnCR5TByRKGx1Wrp/SQTZbPZiDFFILTL5VJckpEWiiGsSadQUK/XkUgkJBJRlLstOkYiEWSzWQlGXOjJ47kdTWRMjfcJAHjw4AG+973vKTJEJ5xhGGg2m8hms4hEImqFo4uLkOZ0Oo3BYACbzQar1Yp4PK7PmedusViEz+dTXIfXdDgc1mtgTI/CAl0e0Wj0TqtZo9HAYDBAMpmEyWRCt9vVNcD7j91uh8PhwPX1tUQ2RrYo/PL6IrOHMSbeC5PJpJwudIkxKklmD50jjKPR4dPr9eD1euWQSiQSqNfralsjSJ5uRp/PB4/HI0g6I4+5XE4RLLPZrPsfwdGZTEZwaQKpj46OcH19LdGTLWD8v2azGW63W+1QFMf4+ijMW61Wtb+5XC41xzHWOJlMFOP0+/1y2zCqx3+feF+nSNVsNgFA0HiXy6UIMGPCOyFnN7v5is52g/XlNUJ/6y2E/tZbMH33PRjrLxcbx2h38R/89//K7+hr/N3/9nfD2Gx+8h/8GRvz6/dR+r1hGFbc+VX+FwIwv/HwE38947fZqHr3zLDEoh//B3ZzZzb9ATYX1zBcdpg2WxgmE8z7uZcu5pg9bkT/QAlW8/bO779yv4TBd36GXEO7+crMCws5rPcdDAZa9AKBgNqSRqMRnE4nms2mfp/xDS6rHo8Hm81GT0Cn0ymy2Sw2mw0mk4mW3VgsprgAXRGz2UycjH6/j263qx/G2UzCVpzVaoV0Oo1Wq4V0Oi2HCevBu90uvF6vRBvGwBaLxZ0ICxkZjPDwya/H49ETXy5b4XBYjgIuZHQGkbPTbDa1MDCy8t5772lRpSOAtdt0jzCeRZjvaDRSIxEFB6vVqugK33M+cSZkmbESflbAzTLu8XjQ6/UkRjHO5ff74fV68fjxY/GD2IpEQLLH48F0OlVLEtkhFPUYv/J4POj3+zg4OECr1VJUb39//474Va1W0ev1MJlMsN1u8c1vfhMffvghzGazIm/VahXAzdJP7gWFmn6/j8Vigddffx21Wg3NZhPxeFwxGrYsRaNRcXzIbaKTajgcil3CeBodYs1mE6vVCl6vV0IRnTR0XHEx7Xa7igQmEgm1DHEhXS6XaDabyOfzOjcZl2H8ilDWy8tLhEIhiTd0OBSLRUQiEazXa6RSKQSDwY84GujGoBDg8/kkNFKkcblcaDabqmHvdrsol8tq8qLo8eGHH6p9iHEgRswajYaYU8FgEJPJRAIPHXUWiwXZbBaj0UjX7GQy0Wfzgx/8AF6vF91uF5FIBPF4HI1GQ5/V5eUlEomE2EkHBwd3XEMEIfP8YcsV3V88rwmhjsViEohY7R2NRlVPDUBNZnROlctlOQV5X6DDiyIC40omk0ltZ5FIBIFAAJPJBK1WS+Lfer3GfD7X92BUlPywy8tLRRfZpkb3C4Hw6/VagiLr1ylmeb1elMtlvQfL5VKCVLvdvhNtYoMcI45s7bJYLOJN0f1CIZqiKAUmNvy5XC7UajU5K2OxGJ48eSLuDLlh8/kc7XZb/7vf74vLxDhaNBrFdDqVOM17Mf+dSCaTglGPx2O9DorEFF0pjFcqFRwcHMh1SUHW7/fD5/PpnJvP53La7WY3u9nNl202wyES/9SK2ir0qf7+01ka+/9gAvyQofhFGZPNjtFJANsfk6Ta2oDS7wnCcnzwwl/P8so99I9/u6qz3Xyq2RgwpjOYas0bUedlM+s2G1R6gZd7DLvZza15YSGHbVNcoLxeL95++22xQ+7du6eYD9ubbgsdFAz4ZJZxitFopMgFG4Sm06kahSKRiCIGXAp9Ph9cLpdAnoz3sAErl8uh3W6LrcFmGwJtDcPA5eWlQL9cotmkxO+93W5Vrd7r9RCPx++4a46OjrBarfDs2TMANw4gNq5YrVZFk5bLpZqpBoOB4jhscWLL08HBgZYlLhbpdBrBYFCL/cnJCQzDEJ+H0RLWIXOJIdeDyxnbqJ4/fw6Px6PPioIEWS50iTDOQFGG7wEjYvP5HNlsVtEwPrUmW4ZRt2AwiGazKS4ImRp0UVCUCwaDcLlcqFarignRAUb+DN8PRoPK5bJq5FlDn8/n4XQ6UalUJM4Fg0EtiIPBAA8ePECtVsNms9ECHw6HUalU0O127ziV6Gogf4UOLZPJJLAw4b1+vx/X19dotVo4PT2VEEiR0uFwwGKxYLPZSPS4d+8eGo0GHA4HbDYbHj16BKfTqWjaeDy+Ey3juccJBoMCs3LB5Z8pl8twOBwS03iN+P1+xaK4zN8W39g+5Ha7JRLSDReLxSQe0vVBnhJFrVwuh+fPn0tc5LnB+nAuzHSnMBrndru1iDPmRoYMRcFsNivRZL1eo1QqIZFIYDwe63O63YDFOFWn00Gv19Pxsx2J7Ce65/L5PGq1mhxMdHHF43E5Nigmx2Ixgbvb7TZ8Ph96vZ7azhhfYpyK4gK5PYy2+Xw+wct53yEMnIwYOsXYVEaR7/r6GtFoVHXmjK2x0Y8ONTrZ6KAjJJv379s18IR8k9vFyCKvAR57MBhEt9uV0+/k5EQV7iaTSaImxX+73a7/vd1uxfwhRJ0iIBv3rFYrstksvF6vHF0Wi0UtdzzfgRsX0mg0Qjweh8vlQr/fl/iUSCTgcrlQKpXUKGcymSTWEjYfCAQUXWNEFbiJRO5mN7vZzZd1An/7N/D3/vr/BNPtJ3M7PJpl8c//o28Dv/H+T+nIfnpjzmfQffjxwsvWBsD24m5M02AM++gzOLDd/GgMA6ZaEya7HUbuxnFsDvhf6iFtp1PE/6brI79/Vo/BezF+CUe0m6/6vLCQwwgDYzkA1BZEEGcmk0EsFsP3vvc9tRmRw8BoBp9M9/t9xONxuWLojGC0huIHADEOyKthxSxrgcmV8Xg8iMfjWgym06lEBrvdfkcUiEQiingsl0uYzWb0+32JD7cbS7hotttt1ekGAgE8e/ZMbUE8zkwmIycSAFxdXWmJMplMyOVy6PV6ank5PT1FpVLRgsNmHToe2ITDphZWgLtcLvh8Pmy3W5yfn2uJC4fDWkTm8zkGgwH6/T6i0agiIuQa0cVCoSWXy6FUKqkK+Hb8gRXH5PTQ6UP3gd/vh9vtFttjNBphNBrJpcIFOxgMCjhKODHdDXTddDod1TeXSiUt0Pl8Xks6Y09msxkXFxeqxqYTo16vIxwOI5vNotfrSTzLZDKK3q3Xa5TLZb0Pe3t7AIB6va4adMbDTCYTJpOJzi8uogAUqWK9NR0WXKopLLG2mXXo7XZbjgYCnY+OjtDpdLDdbgXUZrX4ZDLBq6++iidPniCVSuHi4gIej0eQaQqdhMYul0tkMhnM53MJE+l0WvGpwWCgRilGvhgzOjw8RL/fR6fTUUyNVeIUJRgPjEajEinp/OF5FY1G9bmwMYviRSQSwbNnz9RYRpcMz4NSqYT9/X2s12tFasi9ItOJDjXGOw3D0LnrdDrRarUUq7FaraqnH4/HePjwIZ48eSL2FfCjuGQ4HEYgELgDkF4ul5hOp2r3mkwmyGazYtA0m01dI7zOKYyQKUWXICHDm81G0GmHwyG+C0G/ZANtNhs5cdxut2DuZMSEQiG0223MZjN4vV45Tfg9vF6vPi86BLfbraKwFKiePn2qz2w+n6PZbOo8DofDcnzZbDbFwAiSZ6OZxWIRnHi9XiOdTmM6ncJkMsmNyer3QCCARqMhQZLQ+sViIe5Us9lEKpWSAMeHAfF4XKKixWJBKBTStUMxmKIYo5/8PAnG39vbg9lsRqPRUEMfmWDkN+1mN7vZzZd9Yr/+G/gbv/4vY7T96JL646a78eKt//Dn4fr7b/+Uj+ynM6atAWx/8p970VmXK/BWPz5eZtrgC+da+lmYTeem1MC02sDcGWIb8cOaerkFBO7vXeLZ2/voz13oz12oDAI4/j8udk12u3kp88JCztHREWq1msScZrN55wdoMhHOz8+Rz+cRDAYxHA71xDqRSMBms8Hv9+uJ/KNHj/RDd7fbVeSDURSfz6dFm4wcPlFltMhut2M0Gqk5hkITIzJ7e3tIJBKYTqeCo1osFrRaLSSTSVSr1TuLBpkYBGKuViu89tprKJfLmE6ncDqdiMViWmBtNhvq9bpEE8ZZCoUCcrkc7HY7YrGYFmTWuHMZpejE9yASiWCxWKDZbCKRSEjQIUuGT9IfPnyoJZggUC6rjEHN53OJXVz06Xg6ODhAs9nEcDiUeNXpdJBIJBTb2Ww2YtSQs0PxiQvtYDDQ63O5XHITUdDiMrRcLgHcwK8nk4mcPWz6Iq+DLh0u3/1+H8fHx5jNZlrqWJGezWZV2cyGql6vh9FohHQ6LacBBRjG6BjFIJTb7XarIn0ymaiBjCwQ1i8bhqFI0Gg0UmsYnQCBQACFQgHJZFKRw3feeQeZTAZ7e3sol8tYr9fodDqqRe73+4hEInKsUVwBoGuLothtJtFisUAul0O9Xke1WhWcmnEScmsolJElRLAsvweFDp/PdweCWy6Xsb+/r3gUoyx0a5HrRIGOIuxqtVJMLZfLodlswuFw6LocDAYCbrNym2Dp25yfdrstfhavG7fbjbOzM2QyGcWj2KTEGA5jkfP5XFFEMoHocstkMooiUZjg12+1Wshms7BarYpvjkYjzGYzeDweCSVkz/T7fUU5KfjR6cM2LL7/rN9mvTjPn2q1KgYZG5t4PTLWs7+/r9ggAEVQCVhnrCoUCiESidxx/nU6HYTDYUwmE+zv74txRMcOXV/L5VKRp/l8jslkgtdeew1nZ2fY399Hr9cTbHu1WiGbzcrZ5nK5dM7yc6DA0+l09L7MZjOkUilFvmq1mhyeFMgCgYAEt8FgIDcU425syiPDLJfL6X0jh2c2m6myvdfrKTJFWPd0OkUwGMTjx491D/X7/XdE62azicVigYcPPzkrYTe72c1uvlBjGEj+tbfwf/urvw9/8cPf87F/bLp14P/wzu/D/+d//ytw/TdfTBEHANaX14i99/G8I9sIMI1nn+hruv/+b8J/9VF1yLwEcv9ogHWl+omPczeAMZvBsJoBmxWm5RpwOV/q8WzaHRz+O2/D+6tleH+1jMQfvMTm0bOXeky7+erOCws55JwYhoF6vS4g7O2YxWKxwIMHD2C1WsXbYEMJQboUWwqFwh1OC1twTCaTfqgvlUqwWq2qMqbzYzgcwuPxoFqtCpjK5p75fC5uBADBMLmA0S7PRhxCVck9YbtJIBBApVKBYRiKaXGJMgxD0SSKDqyOZm14MBhEqVTCgwcPUK/XtWAwLsZ64Xq9LgCt1+vF5eUlDg8PEYvFcHFxcccRwPp2Hi+re91ut5qjNpuNqsoZFwmFQuh2u0gkEnIGnZ2dSRSjOMXGGoo9jOT4/X40Gg1BqovFohwwJpNJDqBCoYD9/X2sViu971xY6bbweDwwmUy4uroStygUCglcutlscO/ePS3PodBNbpusIDqOGL/j50mXB+MbjMSQoURnxHg8xt7enoSRer2uxZUOsNVqJUfHYDDAYrGQQ4AsIK/XK5gw43GVSgX5fB6DweBOXITnCBkoPp/vTsU8OSAUOLh01+v1Ow1rjBLFYjFFnQ4PDzEcDrX88/o6Pz9X8xodTmSVsOmNog/FFzqFKOSNRiPs7+8LSk1RjGIbK7dZZ99ut3FwcIBqtYpAICDXGe8R5LZQHDIMA/F4HJeXl3C5XHKbUIDh68hms/p8CMNlzIwxS0LEGcMMhUKoVqsSdCaTidwnVqtV9dYU+QjBzmQyePr0KdLptERoxj/JrLHb7RIeJ5MJDg8PcXl5qfshrxe2MAE3sHg6YtLptM4HCk8UbO/du4dms4mrqysAELSc3ByeGzzv/X6/4md0SJJdk8vlcHFxcYeB5HA4dC1ns1m1MrVaLTF45vO57keMGDWbTTly2EJFDg7dTPw9isgU/RjrYySTVe+8BzLaOhwOBQhnFXgkEhHXirBwt9ut9iu6/Si+83zodDqYzWYwDEMiOUHdrGG/DdGm0LtYLNDpdGAYhu5DpdKuoWY3u9nNV2AMA9Fffwv7f7yEv/qf/aEf++u//E9/P47+yAdfaBGH4/2wDuv0o79vmQPZ/6aCdan8yb7gdoPQf/0Bsv9kAtsPY1bhxxvk/kF359b4Hcym3YGp3sHW74bhsMFw2mH+YdvmS5vtBsZqqV+72c3LmhcWcm5zONhs4nQ61erR7/f1xHSxWMjxwR+0K5WKoJp8+uz1elUFzcWOLASKLlwIN5sNUqkUrq+vAUDwXz5dJX+D4E2v1wur1SpGDPkVLpdLQgvZOmyGCQaDqrtlhTcZCmSdMGKz3W6RTqfh8XhgtVolZBHwmclkZNnn30+n0/jBD36AVCql2IFhGDAMQxwKPonnk2m2AnF54kJJAWC5XGI0GiGRSMDhcOh95sKUyWRQr9fVcHV9fa1ljMv3ZrNRXTGf0KfTaax/2M7A183zgNwVr9er5h632y2ho9PpaEF1Op2oVquq7KaryePxwOfzYTabqV6etc/Pnj1DOBzWuURBhY4uNumwspwLOWuxyQ0ymUx477334HQ6sVwusbe3J6is2+2G1+tFNBpFu91GPB5XwxDhqLfbiPg+z2YzpNNpLBYLgVAZE2NFdzabxdtvv41UKoVEIiGmkdPpRCQSgclkQqlUQr/fF++H0TrghvcxHA7ldFssFhIb6FYJBAJyD1Cw6fV6cLvdKBQKOD4+xmg0QrlcxvHxsVwJo9EIk8lEURWTyYRQKKRltd1uIxKJoNPpCB7Oz5XRQV7TFD+8Xi+q1SoMwxAXiMIrOSiMR5ErQ0GGLiCKR6y5Z9uVz+dDqVSC3W6HYRg4Pj5GqVQST4UinGEYcpTRncP3mhDrcDgsJo/ZbIbX69U5SaYKmVbtdlvV9bwv8Jzj/WY6nWK9XmMymcDr9arRiQJOqVSS4H07okMHTDKZxPX1NSKRCEqlEtLptAScb37zm3A6neh2uxiNRmLtUAijwMcoH9v+0uk0Go2G7it8nefn54LQ04HG9zibzcJms+Hq6goej0eiE6Hf9+/fVwyK1eSMVBHObrfbJZzMZjPk83nYbDZ0u121YkWjUVitVp13tVoNXq9XzWx0FDFCarfbkUgkxFhLJpMYDAZimFF0JjOJ9fSdTgdHR0eK9/H7kwlGwZP/HqxWK4ng5BLZ7XZ8//vfx+npqUTZ3exmN7v5KsymP0D019/6sb8if/MtYPvFa6j6cbO+LiL3dy7h6OLOr72/W8T6qvCpvuZ2MoHprfeQ/psfYP//8gje//ZdbD98+hkf+Vdvtv2bn49NtTbQ7ABHOZh2jZK72c2LCzmEXdJW32w2BUelGMMftMlnIeiUCx1/UGfEgw4KLuC3G58Y3ZhOp3ryymWSMSo6L7rdrmqf6f5hvIPNMnS10CHABi1GQwhmJvCYEYzLy0sBNgeDAfL5vF5zp9MRX+O3MmOGw6HgpfwzT58+RSqVQqlUQiqVgsfj0bFTPCLXgVGlWCwmBwwZOHRBRKNRNBoNNbownkYeBlkVdCcx1kXnw9nZmUDBBEfrxPjh+8w4RywWQyQSkZMlELihtl9eXsLpdMLn8wm2zHp1i8UitlI6nZZgV6/X1WoUCARUj0xAKyG7qVRKoh/Bv8vlUotxpVIRI4ZuiP39fZTLZVQqFSQSCSSTSSwWCzFMbDYb0uk0+v0+Wq0WUqmUODDBYFDLP+vVyRgCIJYM35tKpSJHEqNYwI0Ycnx8rPr2wWCAvb09mEwmnJ+fqzo5mUwqlkd3022BgMs/rwtGVugm8Xq9ih/eblWjI4T8m0ajIYcJ+UZ0vi2XSxSLRUWx5vM5HA7Hnap7gsv9fj/a7TaCwaC4U/1+X3Etgq/pmOJ7TZjseDxWnGq73apCPJ/Py8XDqmhyWwidJgicrJPbwg+dghRt2ejU6XTEZSKTiFGnTqcjsYfOwNVqhVqtdod5xdprNuMxPkkHFaG5dJsxWhkKhbC3t4dqtaprm7BeABIF6Q7jtcZzq9Fo6BwIhUISfUulEt544w3FLym6rNdr+P1+1XXTpeNwODAcDnF0dIREIiHnkNvtVrSRcHIKz3TiBAIBManm8zkKhYLYMxTJV6uV3D5smmJMkc4lRu82m41eP6HivV4PnU5HMcnz83PdawaDgUR2nlPRaFQwbL/fr/v2YrEQY2oymUggzmazcDgceiAwHo/VUsf4p9vtloDGWvhqtSq3E5v6drOb3exmN1+uWdfqiP/179759YmdOD9mtqMRNv3Bzq3xGY2xWsM0mcNIhIHNFjC/8Pq6m5/G/PxrGP/hb2P8h7+N6a99C/jhw/7dfP7zwleCYRhqoyIEE7gBHrPBhLwU2tfZGsOFiJyUTqeDVCqFYrGoxZtPzv1+PwaDgRZrLgB0gJA7MZlMMJ1OVQW+3W4lBl1dXeHg4ECRqmQyiUKhIMgvl1W6W9LptCCZhKkeHh5iPB5LjDk/P8f9+/fRaDRQqVS0lAUCAVUIE0DKyFUwGNTTby5+VqsVqVRKAF8CbTudjmJAq9VKIhfZL5vNzROQ21Gf6XSqp8eDwUBP6BnPYaMLuTmsq2ak5nZ8iM6QarWKZDKJdruNbreLw8NDNTPRjcSn1h9++CH29/e18HF5o3DAz5+skcvLSywWC+zv72M+n+s94Gc8HA7h9/uRSqXgcrnQarXu/F/GXGw2m6qpb9cnt9ttLZh03tyOppDBRN4QIyQnJycYDAaqeKZbKpFIoF6v4/79+zg/P1esg0Lb/fv39ZlfXl7C5/PJUVAul/W+sGKZDCbDMJBOp7FcLvW0nywULtQUGieTieIyyWQSZrNZQuNms8FsNpMD5razhy4SuokYLSMriTHH1WqFaDSq9iJGkpbLpWqo+dkDuNMIRqGILjkKiGazWQLLcDiUu4hcKopxq9VKbJZcLnenrp2tUnR+DIdDfXZWqxWxWEzRpnK5jHg8rpib3+9XvTffD4pYrDF3uVwYDoeo1Wq6Dnl/MpvNqNfruHfvnu5F7XYbLpdLsG7+eULeyYOZTqcSbQhG5/XJz57HSKchm6ysVquibz6fT1yfSqWCeDyOTqeD4+NjXF1dwe12K+65WCwwHA4FZacYxMglxaTbPBtGFuk6KhQKuu7o+OJ9h/eVQCCgSCqdLPwc+e+Ax+MRpJ0sHr/fr+ap+Xwudg3B6IRjU3yim/Dw8BCtVkuCCjlFwWBQsUMKlhSems2mKuVNJhPi8Th6vZ5cd7xvk4HEyC0r7ckW4v1zOp3KubSb3exmN7vZzW5ewmw32F4VYZqvgGQUMAxYcpmdgPAy5udfw/n/3I3av7hC7V9cofyrG/T+tW/vHFIvaV5YyOFCynanw8NDTCYT/YDMp6IUFNj6UigUFEviU3La+vm0nlyE2WyG+XwOt9utJ6JkTBAqTIFjMpkgn8+jWCx+ZAmk64QwUy6Gs9lMT98ZC+CiTKcLY1hkgbApKxwOY7vd6klyvV5HKpVSnIwujuFwiMPDQ7l26JJh61Qul8Pl5aWWPjJ+6CKik4XLM5f9ZrOJhw8fCujLet5IJIJCoaDGJB4/3T7T6RTHx8fwer1otVqIRqOqGr9dHc6Fl5EnukTG4zHcbrcqmQ8PD/X0++TkRFyMy8tLxONxfV4ffvihHEDxeBzdblfLNt9HQnZZLc14HVujyHfhUsn6ZL/fLz6S1+tFqVQSoLjX68l9xYatSCQiJwodNYSmkhPC8/fZs2eIRqNybdCl8PWvf10Oonq9rrpoRp/YCrbdbmE2mxUjoYuMvKHRaIT5fA6n06n3im6L8/Nz+P1+QbLJOmJVO6NkLpdLEaLBYCB3XK/Xg8fjwenpKcbjMRqNhhgwBIBPJhO5xVj33W63dT2RQ8JoG1kuyWQS5XIZtVoNgUAAfr8f8XhcLiEyr9heRQiv1+uVyMRoG2OIkUgE1WoV4/FYizhBy06nU04yvm/ADdtqvV6rPYzCCWOJrHFnCxFblNrtNsLhMLxeL8rlMsxmM9xut4QKCjF0AUajUXS7XbWMZTIZlEolOJ1OiSer1UpRQIfDgevra5ycnGCxWIg9k8lksFwu1bxEgWe9XssNZjKZJLyRJ+T3+3F2dgafz4fj42MMBgPkcjk8efJEjhq69HiOUbgjRJki2v3791EoFJBOp/W9h8OhRPZmsyk3Jc9Nfk5sj6NIzyFMmIIlz9FutytHEF12s9kMjUZDVfB0U43HY73XfA0UhijWs52QfBtGA0OhkEQrNl0R+E0X1+npKQqFgsRfRk0bjQbC4TBKpRKy2ayu6cViIbGK9zwKYrvZzW52s5vd7ObljbFewzSawHDYYKo0YbidsCYTL/uwvlJjdrtx9m84Ad+PQOEms4HWLy2x/KWvvcQj++rOCws5jBgwpgTcRE06nY6ecNIlQrFgMpmoeYgRGYo8fBpfqVQwm820yHFpp3BD+zwXNTIXGC8i24ZPvbfbLQ4ODlCpVPQDfSqV0rLIJYtPg1lpTAs/nyIzZmQYhmDKXH7pSGBD0u0IxsnJiRp52NhEVwjrnNmUZDKZkEgk0Gq1YLPZcHR0hHK5jNlsJocRAMVMCFAlI4dxkXQ6LQgwRQbG2kKhEJ49ewaTyaSmJi6tpVIJ8XhcDAmr1QqTyYRKpSJ3DZdj1hGv12tcX19r6Wb8iIsZHUIUofr9PtbrNaxWK3K5HFqtFobDIQKBgP6uzWZTDT15POPxWPEzLq4UuejU4OLKaBnFDpfLpRgHm5IoIDmdTokUZDLZ7XYcHR2h0WhIdOt2u3C5XNjb25OAUq/X0Ww2YbVadS6w1YjNUZvNBslkUhwXRsjoUKFwyLgS+T2z2Qxvvvkmrq+vxXEym83odruKad2uCKegmslkJGgw+sS6aTqLKKQxvsOY1Wq1QrlcFs9ptVqpfj4UCul1klMTi8WQTqdRrVbVvEVWC10QrAK/7eKhc4OC0Hg8VnsSeTjkrlD4pNjGZZ7xLZ4LFALZyMSvw+ub1dS33WRkWe3t7en9onun2WwKyD4ajbBcLrFcLhEIBHQO+Xw+bLdbjEYjOUbIxLFarTg8PMTz588lyDA6t1qtcHBwoGa8eDyOUqkEi8WC1Wql98hqtSqyNhwOJXR3Oh1MJhOMRiO8+uqrqjWnK4VRNEaeCHeuVqsSXhlpDAQCuv8CN+1/ZG35/X6dw7zHtFotADdOrIuLC7FohsOhnFAUvarVKk5PTyXYUwyKxWJi5zB2dX19jcPDQwkso9FI7yOvZ96/6PIym80CfwOQOEzwOXliwWAQXq8X7XYbhmGoMY2RXsMwEAgEFNe9uLhQhTvjdoys8t+D3exmN7vZzW5283JnXW/crXHfRaw+/zF9lBtoMgEw79xRL2Ne+ArgMsiKV8Mw1FjC2Aihvi6XC+VyGclkEsVi8Q6Q1u12K54wmUzQ7XZhtVqxv7+PRqOh2trFYqH/5nQ6kUgk7gBY7Xa7lvnRaATDMAS7ZJSGLS4XFxeC/ZJjQb4JmRnhcBgAJDhVqzc1gR6PB/1+Hy6XC5eXl4jFYmI/+Hw+ANCCzWaf2Wwmd8pkMhF3gpBPPtEPhUJqOnr//ffhcDhwfHysRatYLOr7PX/+HK1WC+FwGG+88Ybgu2SOJBIJzOdzjEYjxaEYXaJgRRYKcBPR4uLKRY9P0Q3DwNHREWw2G54+faroEqMTXPb6/T663S48Hg8SiYTqgjebDaLRqCIZdrsdnU4H0+kUiURCEZZgMIharSYGEd1YjIT1ej0tfpvNRlwiuqz4PVlLT+cGIysUWZxOJ/b29tSgxveRgpFhGNhut3LnMIbVbrclFDx58gSpVAqvv/66eBpOpxPn5+fweDxIp9Nq1mKNM89xsnpqtZrgsDxHY7EY/H4/bDYbHj9+rEpvABIwyC9iBI4OBAJbyaehi+r6+hoWiwWxWAwHBwcC91arVQlThDTP53OEQiGYzWZFChlzIxeLkR2yc7jw3oaUs6mO/CE6p25DjQ3DEJCZwhzdTIwWEdLLr8vWOIqfFJKXyyUmkwnC4bAcc0+fPpWwx0Y2XnM8FyguZrNZna8EY/P6cbvdWK1WODk5EU+Kx0qgsslkQr/flzjD2m3GLBmT4teMRqOo1+sCXR8fH8NsNgt8znsqj2O5XGJ/f18QYbqHeD7eFkBarRZMJhOy2axYUvP5XCIxnXV0udERQzg1zzPej/laV6sVUqmUIqwOhwO5XE418bw30zmWTqdRKBRUCc+Wq/PzczkG6VSjEOV2uyVKbzYbicuE17NhjG5NwrX57w75WXTfEMDP95JwfgL5Kc6tVivBskejke77FJMZ29sJObvZzW52s5vd/OyMabmGyWaDYTJhGw3AvHPNfn6z3cLatX3kt42ZBdbR6iUc0G4+ESNns9locSLzhdEbwjO5OCUSCVQqFS2ehmFIvBiNRhiNRnqKbbFYUCqVJAjdhsEyqkUxpN/vIxqNwm634/z8HPv7+1gsFnIN8OtTwCBTh4BLPr1mc5LD4cCTJ0/UxMTlIBaLwev1YrVaqdWGAgnZMozetFotOBwOwYABqEnqNvSXUF46iCg4DAYDvPrqq4qUEAwbCoUUI2Pl7mw2w/X1teI7Xq8X9Xodw+EQy+USv/iLv4jHjx8jEAiIK+NwOBTnMpvNCIVCihMRkkqXiM1mQywWuyMSESpLdwIriek0IduHldq332cyTtga1u121QTGSmM6aDabjWC/txudyDbhL7qzyuUy0um0GosoqNG5sV6vcf/+fbTbbYlCXAK9Xi+y2SxarRb6/T6sVqvacHw+nxY9CgpcyiuVisCshF1zkadgQQGEooDD4YDFYkGtVsPrr7+uOBBdFP1+X7GzcDiMQqEgAcTpdOp9TqfTaDabep9msxkmkwlOTk4kKPR6PdVZM6bG///09PTOor/dbhGJRCQgBYNBAXNvf2/Cunnci8VCQHNGoCg8VKtVXXds4WK1PKNVdrsdT58+hc1mw8HBgVxRFotFtdrVahXpdBrHx8e4vLzEeDxWpOvy8lLHy3hQoVCQSMW2JIoVv7WljnEkxjrb7bZa1ihYTadTnJ+fw+l0Sigzm82KpzGGQ5fIfD6Hz+eD3++XyFCtVrHdbhGPx9FoNODxeGC32yU48rzjfbDRaGA8HuOVV17BcrnEkydPYLPZFBMEIHGMEHK6iqLRKIrFIgCoSY+i+JMnT5BOp1UBTqbObDaT4yUUCgkYT9E9lUphtVoJNr9cLjEcDnWd0KHn8Xj0Z8nGoXAC3ETjIpEIarWaYND5fF4wal4LFIoYZ7u+vr7DZqIbh9fzw4cPJd4YhoG9vT10Oh0AkDgUDofx7rvvAoD4bWTtRKNR3UPa7bZce4wshsNh1bXvZje72c1udrOblzyGge1lEYbfCwAwTRc7Ts7nONv5HKf/1wpsFbt+z1iZcfRfrWF6672XeGRf3XlhIafT6WgBYXX4+fm5WoEqlQrefPNNLBYLRUX49J1WfboKNpuNlnmKPx6PB3t7e3j06BHsdrueYNPR4HQ6VVdO0YHwYlYIr9drXF5eIpVKwev1io0QDof1pJkME7fbLQcNWS+3G6z4VJoOFZ/Ph0KhoIWHsGc+eb7twKCLh1BgunfIo+DSCtwsHBaLRc1NjDlst1vkcjmUy2XBbNPpNEqlkmIw9XoduVxOT61ZuwzcxA5sNhvi8bgEIq/Xq6fNVqsVyWRSsStWJnORZ+NXOp2+EwdiPCgUCgkGysgKALF3bkch6Iph6xZrfykGMKoVCATUWnVycoLJZILJZCJBIBAIqDWIAgvFJ7ZzBYNB8TtYVUy4LDlE4XAYnU4HtVoN6XQa8XgcV1dXaqXq9Xpqs6FTKZvNip9CIYtOELI+GOHodrtipcznc7RaLVitVrnKksmkxMXhcKjz6eTkBE+fPkUoFFJt9+0IDQUiwzBgt9sF+mbUh+1Yq9UKhmEISEzhdTqdygFBEZYiKKu16X6hSNBut5HNZiU63BbpGLsDoIhSt9tFKBTCxcWFIpbL5VJiQSgUktOOAhBfF7k5q9UKe3t7ePbsGabTqRq/WKc9nU51vQ+HQ+RyOQGlCThmbTq5RqPRCAcHB3f+3GKxwNXVFYLBoFxejPqRqcV4FCHv4/EY8XhcEGHCoikQsDXMYrEI2EynEZlYdLTxa61WK32t2ywtOp6m06m+FoVZXltkYrHVLJlMolqtIpVKoVar6fd8Ph9arZbYLwQ334699no9mM1mRCIReL1eNJtNTCYTZLNZrNdrObsikYjcVzyPu92ueEm8LwaDQTV93WYeLRYLQYYJE+b9IBgMYjqdotfrqVWQcGo2qFGcY5zRZrPpgQL5Zr1eT/dRwsV5vrCNkM4sm80Gi8UiwZQPI6LRKEKhkMSh3exmN7vZzW5283LHWCxgmi9g2myA/ggmmxXWVPJlH9ZXZtbXRRz/51c4+r9vEfquHQ/+cheWf/rOyz6sr+y8sJBDAYARocViIWYNOQij0UiMlc1mg2w2K64Ef6B2OBxayMi1YLsN4wBXV1fw+XyqfZ3NZqo855JhtVrVcFWr1cSYYKUthQUAgp1+7Wtfk2PGbDbriTCXWQpCrD9nFII8FK/Xq6UYuFlWuPAPh0PVMrdaLRwfH6NWqynyRQeEyWTC+++/j1wup+Pg8sHFhkwPNrIAN0/Zm80m9vf3xZM4Pj5Gs9mUo4HvPb+Oy+VCt9tFOp2G1+u9E6Hy+Xzo9/tyMLHWmtGgQqGAk5MTrFYrLfPkoASDQVQqFTGJksmkGB4ul0suIS5zdK8YhoFut4tsNqs2nsVigZ/7uZ9Do9HAcDjEwcEBHA4HWq2WPkObzaba+nA4jHa7LQg0I1SMWJCR02w2kc/nFSXh8g7cwIXpoOHiTpcFF1XGwygwlEolfdaE61KY2dvb02JN5wqjRVxsKSKSX8S4EoXF1157TQ42cpTIYuL53+/34fP5kE6nFSdkZI7LLWNpbI1bLBaIx+Nyi1ksFmQyGbz77rv6zG02m+IwdGtQjGTdM193p9PRUs26dS79+XxeQsHR0RGKxaJcDq+//jrOzs50DBSACLEm+4ci2Xa7xf7+vphBdO3x+AnEJfMpEAjoPOz1ehKN6dKoVqsS29j+RbDucrlEKpXSZ0wgcTgcVsyt3+8jlUohm82i3W4jGo2i1+sp9kcny3g8FjCaYpzZbL7DLyKfKZfL4dGjR8jlcpjNZnLNzOdzlEolBINBccB4f6AjajKZKILFNixC4ylUplIpibLNZlPnJwG+BDIz2keuWavVQiQS0f1gPB7r+OiGozOO35/iN68zilbz+RzHx8eKTbLNb7vd4vr6GslkUu5IwpbJJGLUkRBzAtnp/mRjG/ls5HHN53McHR1hOp0iEAggFAqJL0ZRj07Ay8tLrFarO6LUaDQSN41C4W52s5vd7GY3u/kZGrMZxmQCxCKA0/Gyj+YrNetaHeb/7l1E/4u3sDm7eNmH85WeFxZy1uu1npQynsKnpwSSEjZqsVgUx2FzD5cm8iK4TLNZiM4XLkYE/hKu2W639XsUlFhdS2gsOSSEJNMaT3GC9nkyaG6DaG8DNekioDuFtetcQOv1uhY6igkUSRaLhWJZjF3RHcOleW9vTwweRocoSPB9A4BisSg4rNfrxXK5RKfTUaUujyedTsvlwDgP2Q/Aj0Cq8/kcACSo8b30eDxiSrTbbXi9XsTjcYkFbG6aTCZiwXBRpnDDBYqLKwDVhpMZw4gOwdJkc5Ddws9+sVjoabzH4wEAAX4pUHFxXi6XYi6ZTCbMZjPBftvtNnw+n1xLPFcIGq7Vaqrbzufzcviw1eni4gLxeFyNQhRyCOxmJTIrwQkEZksWhSiKaDyfuLiORiPF7L773e8ilUoJzE0xbzAYIJ/PK2q3XC5xfX0tgDaFVYKm6R6hsJPL5XB2dqb2H9acsyadC3uhUBBXJxKJ4Pr6Wu8/l/JQKCS31Wg0wmazUWyL4GS6e+jUSiRuGgW4HBO2zFgfOUoE3DabTfh8Pom2drsdrVZLEHQ65chfIeeK5zrf929/+9t6L3ju8zyh64qfQzgcxtnZGdbrtRqiKG7chpmbzWaJfYVCAfl8HrlcTk18e3t7unb7/b4EQkb+EomEeDiJRAL9fh+RSETiCz/zbrcLwzAkitCNBQCNRgOZTAYAFDsKh8NyxRUKBUQiEQQCAUX3GFcjM4wiFt1e/F6Mnd3mn/E+AUDwdrqrKHTz/snYF+vbDcPQZ0QuViqVEiD65OQEl5eXsNls4kfxmk6n02obbLVad6Jlt1v9KNCwaW6xWOiBgtPpvHPv5YODwWAgYDOZbrfFUJvNpveE3283u9nNbnazm938bMy21YFhMcEcCu6iVbv5Ss8LCzmMW2SzWfEhisUi+v0+Xn/9ddW+plIp9Pt9MSyq1SrMZrOECDJECAUmpJeRFQIoLRaLfnU6HUUmDMMQjJS8D3JaLBYL7HY7IpEIut2uXB5kWZCtYzab5ZDhU2oAqitndTIBooSicmE9OTlBs9mUCMLIwHw+vwOWtdlseprr9XpxfX0t2O9qtVJFODkTdI6w1jkej6PT6cjdQuaM1Wq9E5XgsglAYE/GEbi4Uyiii6ndbsNut2M8HkssoyuCi1I0GlW0zWKxoFAoaDGkgBSPx/Huu++KvUO+RiQSQSqVwrvvvquKdQJvGYuhO4v19PxcKAi63W4xRSaTCbbbLUKhkJwmXq9XrKHhcKhoFeN9t6ucudx1u10EAgH4fD5FMuh6oOvJYrEgFApp4V+v18hms7BarXpKTyEgHo+j3W6jWCwin8/LTcJmNy7bBMySl0MGTrValSAK3ACeY7GYImKMuK3Xa0GGvV4vPvzwQy2jkUhEzV8UEwnopUhGERSAriM6GRirI2zcZrPB7/fDMAxMJhN9T34+dAw5HA5FbMhc8Xg8Or/D4TD6/b5EOJ67FA0psnQ6HRQKBZyenqoe3mazodPpqMKbLVnBYFDnUblchtfrRa1WQyaTkXA0nU7x9OlTPHz4EB9++CEcDgfu3bsnyDPjel6vF4FAQNwh1q9T1OU12m63kUqlUK1WsdlsxJ+az+doNptqYLu+vlblOUWfWCym8/X2/WmxWMDj8ej86/V6ataiq4mcMbKhGD+qVquYzWbI5/MSX51OJ4rFIl555RWcnZ3JbXZ0dASXyyXxhE62ZDKJfr+v96tarcLhcCAUCok55fF4dE1TdFuv13C73WJrhUIhfPjhhxJhkskkWq2WeE4UsygAsX1tu91KaKUwOh6P74iAFKzJVqPAQ7GW0Ucy1+jQ2t/f179BFLTpTmSdOt1IPP8SiYT+PaDji1+b1+ZudrOb3XwZxxIJw/Lw9Ee/Tg5f9iHt5jMeazYD0zdf1S+Yv9gPKLbTKUzTBbbRAAyXHVu/G5Yf/ty9m918leYT9bZ5PB6s12vEYjE95fb7/YoSsLmGdvdqtQqr1Yr1eo1gMCg3Tq/XQygUUm3tZDKB3W4X2JNP+ZfLpYQGugHK5bLiMt1uF36/H4vFAolEQrEGQlH5FJZPewlE7vV6SKVSEhP4tJcCEQBFXshVAKAlcjgcqiGLLTNcgG+3qVCUSCQS+PDDD5HL5cSx4SJoNpslZCyXS/h8PnS7XTmYAIh7kU6nxTGJx+MAIFEmEonA5XJpEZ3NZmpwCYVCcppwmeQxsxGnVCopIuRwODAajXB9fS1Rhks0hyIJ+Rp0OphMJtUfk0dBLgk/f4oOfJJ+eXmJVqsFn893J6LG959CEF1ZbrcbLpdLLTQETZNHstlsEAqF4HQ60W63xRbxer1ihzDGValUsLe3h6urKy2nhB3P53O4XC6Mx2MxouhSocuLYG6v1ysnDcUyLt2MO3m9XsRiMZRKJRiGgfF4jFdffRX1el3vK5dpgnIZx+NnyPgIBQK32y1hqNFowO/361jYPOR0OtHv95FM3mSIr66ucHBwoPrv2WwGk8mkxik2xjFqSIfKcDjE4eGhol5c0lerlZxLjNDQrcZmptFohGfPniluxr/f6XRU+Uzhg9EuHler1RJ4nDBicn4YReRrbLfbCAaD8Pl8qNfr+Pa3vy2ByuVy6Tqgo2+9XqvG3uv1otVqyZFCd5jP55N4RQceRchkMikgMLlB5XJZYk673YbJZMJqtRJI2+/3q90pkUjI2USWDN/PRqOBRCKBy8tLCYtsd8rlcqqXJxw8n8/rnvz8+XPVl9++NnmPKZVKchqylYqiHR1lgUAApVJJ9xW73S4HIR2Ji8UC+/v7GAwGEpDZlMV7Ce+nFosFg8FAnCXewygqUjihiHPbBchWxEqloveBgg//DSLcmNcXxe3FYqFzkBHI4XAIn88n9+h2u4XL5RKvKJlMypmzc+TsZje7+bKO2eNB4X91H0//nE+/nvyZKIzvfO1lH9puPoOZ/+rPo/6nfwGP/700rH+lA+tf6cD8l7uo/plvof6nfwGWWOxlH+Knnu11CabJ/MaRYzLt6q9385WcT9RaxRgS+Rsej0dxDraK0I7PpYDOnOl0ilQqpbiD2+2W0JPL5bBcLlGv1wWcrdVqcDqdipB0Oh30+30cHx/facMiA2QymQgKO5lM4PP5FMuhG6BcLovHYRiGfujnU+BMJoPpdIpGo4F0Og2r1apWFbatMC5ltVpxdHSEUqkkFgQXerfbjevra8RiMb1vdFkUCgU9hY7FYthut3L3cGEiAJnwX/Jv6HogCHg0Giki1mw2JSK4XC4JDawKJnyV9cV8ch0MBlEsFuH3+9UWQ0EsGo3C6XSqbh0AKpWKnmpvt1uUSiW43W4JGreBoaPRCLlcDg6HA+Vy+Y74RfGODiu+x3TBsLKcUS3yLhwOh+IpyWQShUJBwFe+371eD41GQ+4kj8ejeBNfx3w+R7FYRC6XUyxmNpvdqW73er1y8LTbbQlRiUQCPp8PtVpN7BE6TLLZrJbRbrcrkWk+n8NisaDVat3hnZCXwutmuVxqAZ5OpxIgZrMZut0u7t27h3A4rJgdgd42m01NYMFgUF/zNjCWzBsurDw/PB6PALnkPbEtja6jarWqWNHtCAvZIoyNUcQi3Jpuomg0ing8jlarpUgXBdtQKASTyYRCoaDGLIpxjHLR6RUKhWC1WlGpVGCxWOSI4oJOEYdOKp5ni8VCzKlAIKDrtFarwWazYTabYTQaIZVKSZykAMP/7nA47riqisUinj9/jnw+r5gWG5ssFgs8Hg+CwaBajwj5JSeGcbPNZqP2NV7voVAIyWRSAtLFxYWcLBROCagOhUJwOBwCW3e7XaxWK5jNZlgsFv1Z/h7ZP9fX13I/er03DRD1el18KJ4/hUIBXq8Xx8fHEnQePXqkWB/vxcBN9Csej8NkMilKx5Y1RmkBSEBhbG2xWCh+Nh6PBXVnUx8Aibv9fl//9pjNZhSLRTkmb3++s9lM7Jzlcom9vT243W7FG8nZ4fvESFsoFMJwOES320U+nxdbaze72c1uvmxjSicwu7e4+3uuDZ7/ESfMr95/SUe1m9/pWA/yOP9bX8fiT3aR+7UrvHKvrP9mNhnI/74r5H7tCs//Whr1/80vwOT44jFmjPUapvEPHbPb7RfeZbSb3XyaeWEhh0/+GSMaj8d6Qh6LxfSUk38uEAjoKTQAWd/9fj8CgQCurq4AQFEDPnFl00oul0OlUlH9dzKZFAeGT3LH47FYLQAEsGRDD6MJhG/OZjMd22QyweXlJdLpNDKZjISH5XKJbDYrEK/NZlOsazAYIJlMqt6ayySt9+v1GgcHB1pmuIBQ1HK73Ugmk3qCXiwWBWB2Op1qBSsUCmrkYnwGgNq9+v0+MpmMGEJ8nxmLonOKbBzCjRmt6HQ6yOfzePr0KcbjMYLBoGC4t6HMmUwGtVpN0Qq/34/79++jXq/j8vISwWBQ35dizGazkfMhHA7j+vpasZBarQYAODw8RKfTQaPRgM/nEzy3VCrJXcEoH88LijB8zRQvwuGwGpNYtU2hg6INY1AEEFPY4MJvs9n0XjebTUWJ6CJihIXNNnRLpdPpO+wauou47K9WK2QyGTx79gyRSETujMPDQ10vjLCw+QeABB1eS2xIYoU5a7p57fDaIjSXYhA/BwpNt2Nd9XpdHKHVaoVvfvObqFarcntRVGMMLh6PC5DLxZzcKYpmNptNbjNG9xjv63Q6iMViSKVSqFQqulfQTcHzx2QyIRKJoFqtSlgkH4cCYLPZxP379wXhNZlM6HQ6ctSxwYwcIDrQyEBiOxUjipFIRPFNRov6/b7ilfxzvV5P8Uq32y2e0Hvvvacond1uRzweR6FQUFzQ7/erIYpOLYqYBLxfX18jHA5LWACga4ifKY+fny/b99jk1m63EQqF4HK5BEQeDAZIpVKq2qboQphwuVzGZrORK4ksK8LG6dKjW4bCF5u+2MQ3m80kwNENORqNdG3cjjuSOUVws8ViEUyaMadIJILhcCgmEj8DxnPZ0kURiBHMTqcj7hPvH3TcURBm0xW/Bj/TUCika2I8HiMajcqxtJvd7GY3X8Y5/2OJH/8fPGsYLtvnezC7+UzG/Pp9XP4lPx7ma4i6J7/tnz1NNZH7V65Q+dPfgOmH+90XbgwD5mYPiIVgiYRf9tHsZjef67ywkGOxWDAej1GtVuH1ehUXWS6XWprZCDKbzfSU+OrqCjabDdFoFI1GQ4u2w+GA3W7XUkUmhc/nk3OHjIPpdIrNZoNUKoXBYCDhgGyc8Xgs9s1wOITf70en01Gchk99yT5h1Tgrhwlc5pPoxeLm6QT/G7ke5DpwaXM6nQLa0plA4CtjKsvlEh6PRzESwzBUxRuPx/Hs2TMEg0HYbDZUKhX4/X4BYrvdrlgPBMJ2u105cZLJpLgUdEHQucSFiWyXd955R0/l2fJEIYx11XSJABBI9ODgAO12G1arFYVCASaTCYlEQksznQhXV1dyerAqfbVaYTgcyhVEYC6jIq+++iqq1Sq63a7iQ1zOWGO+2Wy0dANANBrVUsvI3Gg0QrfblZtiMBhgf39f0YtqtSrxZrVaIZFIKN4BQIISmUzhcFgw6lQqhefPn8tZVCwW5fzp9/uCOefzeRSLRaxWKwmGFFnIXWq324rU0dkCANVqVWLiYrHQku5yuVCpVGA2mxEIBFTfzuYnLst0ZHm9XrWR0a1B1wQhwOv1Gt1uVwLh/v4+ut0u6vU6fD6fYnVmsxmHh4eCe9+GBpOhxEY0CiF+vx+NRgPj8RiJREIC4+2lnUKf2+1GuVxWK9x2u8XBwQE8Ho+AvaVSSe9lp9PBK6+8ovgS6+Oz2ayYW3TZdbtdCRJ0NbFhjddjIpHAxcWFInqMDfKamE6nErQoHtJlRBYLo38UYcfjMdrtNgaDARKJBDwej87H+XwuXlK324XT6UQikRCoeX9/H1dXV2LxEHRMkHwgEFDrk8/nQ6VSEYuJNfcUKdkCyPhZv9+Xi4XiO89b3gs2mw02m404UKFQSOwdnu+LxULiBt1lFHlcLpdeH91VqVRKzi3eW/lvBu+nqVRKzjXWgvO+xz8fDodxdXWl6FYsFpOY43A41IxYq9XkxKHQSV4UmWGdTgc2mw1Op1PAZx4TXY3RaBR+vx+TyUTcst3sZje7+VLOLo3ypRrL8QHO/x0njmLtT/T3cr/3GvV/8+d/Skf105tNrw/Tcg24nDBNZkB0J+Ts5qs1LyzkMB7g9XolrtyOTXHhTiQSCAaDuLq6wnQ6xcnJCWazGQaDAXK53J3KctbWcokkoJUcmXw+L2GAzgDWNpvNZsTjcQwGA8RiMTx79kygUcZwlsulYjEAVCnrcrkQi8XUStNqtZBMJmG32+F0OlGtVuWo4dfgk3MuNBcXF/B6vXjttdfQ6XTQ6/UQjUZRqVTQ7XYRDoe19PX7/Tv8GQoMrEmnkLPZbAQVZmsOo01csNj4Q3GErh9yg2y2mycoh4eHGI/Hd+JYrNpmBC0WiylCUSqVsN1uFV+6Hb0AfuQ2MgwDFxcXiEajsFqtinSYTCaxXsxmM0qlkmDJdALdv38fy+VSINerqytEIhExghqNBqxWq6JkXFDJ0+GiF4/HJQiw7p2RMMJ/uTgbhoFUKoXlcolQKIRyuSwhg84b8pmePn2q5qt0Oi3RhBXUZrNZIFXycxgVaTabMJvNOD4+ht1uR6VSgdvtRigUQqfT0TnGhZKQ3KurK30f8lPq9boic7fdC4wq0T328OFDMT0Ia6XwyAWbTJ3bnCRCmdnkBdwIVufn5wBuWFjb7RbPnz9HPB7XUs+Y0Xg8ht/vRzQavQOnJmeEMbVut4tOp4Pj42NEIhFUKhU5P8bjMQ4PD9V89uTJE3i9XoTDYcVcHj58KCcIxQEKLPV6HYeHh5jP5wiFQmq2KpfLODo60nnGyN9sNhOct9PpoFgs3mmZYnyQcU06wW6zvsLhsJZ9xsPID9putxiPx8hms2JosUabMUTGMemQGg6HMJvNqhKnUMRri4Bi3je63a6cT7ymLi8vBe4mA4nCA6vjz87OBGUPhUI6p/1+v+7BABQjJRfKZDKhVqthf39fkSrC1+PxOHK5HNbrNUqlEhwOh/hWvPZ4zz48PJSYQ+GJ7CUA4jU9ffoU0WhUDk/GwihaUiR1uVwIhUIoFouYz+cCtVPEIdx8MpnoPsxoYSgUQiQSwePHj+8wqCiM83UDEGyd99Td7GY3u/myjbv+MUrO0ArzeCdif6HGZMLVX/Difrrxif+q2WRg8HMLWDPpn8KB/fixZtIwf+2BflkP9z/x1zAWC2Cz/dFvWMww2XZx6N18deaFhRyTySTRhiBixl+GwyEcDocaUOr1utwu6/UaPp9PLgtWF5PJwifGBHy6XC48fvxYi/Z8PtcTWLolHA4HqtWqFi/gR3XK/X5fzgqHw4H3338fqVRKgMzbdeRkntANRDeR1+tVZIxuDwpXHo9HYNTtdqs6cFr+M5mMBAM6hmKxGAaDgUCdFCDYnrLZbFQLvN1uxeqZzWbi6BD6TG5Jt9u9E5+h2MTqccZ0GA1jc4zf77/T9HK7hYpiAp+WUxBhk5DP58Pz588RjUYVZbFYLKjX61p0bx9jo9FANpu9I7QQfM3PqdVqAYCWeDJuyA0hxHg8Hus12+121WV7PB7Y7Xa5BKxWqxxhrLAmCJhRJzrIyuUyHjx4oKWZroLbsFu6p1gF7nA4cH5+jtPTU7mEHA4Hnj9/jvv370vgIzCW5z+X9tVqJfcDACQSCcW72BxENxUdBoyUbDYbJJNJtUI9fvwYmUxG7JZGoyHQ92KxkCBBVwwdbRRGAGA4HKpxiNcNz38A4mGx0WkymcDlcuHy8lLtV2SSUOgho8Xn86kenIs2xVK/34+Liws1FsXjcVxeXsJkMukcJkybgspkMkE0GkWr1cLe3h7G4zGKxSJcLpciSHw/eW7cZjjR2XNbUCC4l4Bcnht2u10V96zJJmPHbrcjEAhIOGUUiVXpBwcHqr3u9/tieBEuDNwI44wyUZBlOxxdfoQQ9/v9Ow1bBBH3ej34/X5FPT/44APYbDak02nBnEejkcQZOggtFguur6/FNYpGo9hsNgIvj8djuFwuOSJ5Ht/mKPH9oluJTVZ0gZH5RY4TeVV0BNIRMxqN0G635dDk/TeXy2G1Wqm1KhAIoFKp4PT0VFEuitDr9VrnrtVqhcvlQrlchtvtVhMW3W4UxBgVHAwGiMfjimdNp1O9/5VKBbEvMAhyN7vZzW5+0qR//QfwfHiXj2JMLTj9W2Nsnpy/pKPazaeZ2e9/E4nA6FP//VcOqmj+Sv4zPKKPH2smjed/Io9n/1u3fj39t5Iwv/7puEyG0w4sVzCsZlh2rpzdfIXmhYUcAnLZetTtdrVw+3w+VQsHAgGEw2HFZywWCy4vLwXcvLi4QDweh9/vRywWw+XlJQ4ODuDz+VAoFLQ4jcdjDAYDsUUYTeJCAwBPnjzBycmJOC2Er3KpbzQaOD09hWEYagmiIMVlJRqNolQqCRrL9p5ut6voCBklFxcXiEQiiMfjgvKycYVLOuMO8/lcDB3GRyhe8c+s12uk02nM53MthgRF93o9JBIJVeb6fD4A0OukiJJOp1Gr1VSzzUX69kJVqVQQj8cV12CzE9khXGD4vlAgo0jU6XTEOiHvgwIen4BbrVZFatrtNqbTKbLZrCJsFCbokuATdq/Xi0qlougbFzpGkSgupVIpgXvr9bq+Jx1By+VSbi4KY7FYTMIMHQx0Z1DYuL6+Fo+D8RlyRBjR4NI4GAzE+iDcOhKJIBgMIhaLodVqKSbEc3cwGGA4HEpEIbQ3EAigVqvBbrfrHAAgMY6xJLq2UqkUnE4nWq2WFliXy4Wrq6s78RC6dxiBcbvdd1qE6ELr9/uw2+0C2DIGyNgSK57ZBsQmts1mg3g8ruNrNBoSvnw+H2azmT4HAAIp366Ypkjj8/kQDofVjEYHD8WqxWIhN4zZbEY0GlXUcDweIxwOS/zodDrYbreKVdlsNiwWC4lUjD7RUcd71nK5RLlchtPpRDgclsOG7Wj8O9lsVuwiq9Wqc4Fcl9lspljTYDAQsJvnZiqVwmq1wpMnTxSjo1DDeyhZWGS9PHjw4M49sNFoIJ/PS0jx+/1qzXry5AkePnyI+XyOs7MzgdKTyaSa49jIBdw4sPr9vuJKdAkGAgG5vuhWoehKMYguSQK+Q6EQarWaYpf8PBjN3G63mM/n4uBQxCREmK1fuVxOvCMK+IxP9ft9pNNpsaJ6vZ5caoFAAOl0WsDjdrstccrj8cBiscjJcztGyt/rdrtIJBJy5GWzWfGJ3n77bUUwd7Ob3ezmyzbb6RTZX/8A9/+THswtO5L/yIaH/6cKjO8/etmHtptPOM1vWOC1L37yH/xtZvNrXVjzuc/oiD5mzBac/1t5rHLLO79thFZ49m8EYE1+DLfpY8YoVmBYLDCWS2D7k//8bnbzZZoXFnJYv2y1WsUf4FNfxmLY2kL+B2GS6XRa7h0uqYx8EEhLyCeXPbo6uOwvl0ucnJyg1+uh1WrB6XTi+PgY9Xodjx8/xt7enp5S0w1BbkIgEBAU1TAMgVo3mw2WyyVsNhuy2ayqkOv1Ovb39wHcLNabzQb5fB42m+0O54TLrsVigd/vVx31bDbD1dUVotGovi5FMMMwxBThk+Z6vY75fI69vT10Oh1YrVbFuABo+S0Wi0in04jFYlowW60WptOpWBfT6RS5XE4L62QyUQSNCyh5K7fdVYy2EbpLNwo5Hmxx4sIcj8dhNptRq9XELfF4PDg7O1N0p1qtqhGMYorT6dQCyBjQ/v4+TCYTHj9+rKgHI2ez2QyRSEQimtfrlYPL5/NhMplgMpng4OBAr2M8HqPVasFisaDdbsPj8WixY7SHjJB4PC43lNls1vfm9+fifn19jUwmg0gkgoODA0W8CKdmZIbnVSKRQKPRkADS6XRUvV4oFFS/bBgGnj9/jtVqJecZAeE8D3ge2u12xWIMwxDThvGbyWSiBq8nT54gmUyK/UOuEiNmdFOwpYdiDyve9/f30Wg00O12FQkji6pcLssplk6n77wmvmdut1ssIIpBjHKRcTWZTJDJZNBqtTCfzxGJRCQukuvjcDgU4QQg1hGjMwRPE65+28FDPsvtRj2KQnQ1UUzy+/3odrtqOvL7/SgWi7p2bos6FGEBSMh6+PAhNpuNhCS6y8ilIlSYrhefz4f1eo3r62vs7e2h2WzqOiJ7ZzAYwOfz4fLyUjEouukohlcqFRiGobiZ2+0W7JiQ8U6ng/fffx+np6e6pzK2RDcSWwQJnqcIyGuUdeCMnPEcpasSuBFmSqXSHVeX1+sVw8jpdIoBRbGPTsd8Po/z83NxhRgdXa1WaDQauh/zs2CNPQBxw9hy9fjxY8TjcTmAxuMxYrGYrm0K/X6/X8fLc4zCGkXReDyObDb7af993c1udrObn+pYTo+w+JfexOJfevNTL+Db0Qib80sc/dm34Pt7v4F1ufIZH+VuftpjPdzHIr36HX+dtH8Iw/bThx6vgpsf/x98a+ATxpm38zlMlQZMPu/NVutyfnHBzbvZzSecFxZyGDdgKwxBkgAkDHDRoShjNpsRi8X0dNZms8Hr9aJWq6mtKJlMSvwBbpqZ2Cg0Ho+1gDJiYzKZFCMCblwIBFNy6eOSMJvNJC7RFUPnC584s3p6sVhomb64uMBsNsPx8TGsVqtqyw8ODjAajfDBBx8gEAgofkPYbCgUQr1eR6vVwtHREVKpFJrNplgRXBz5lL7X66mC2OVyweFwoNVqIZPJoN1uY7PZYG9vT7XlAFTPzGUsEAhoEWfryrNnz+B0OjGbzRAO31gMe70ejo6OVKnNina2LREse9tJE41G8fbbb2tp4vETVjoYDFQrfXFxgXA4LMZMNBqVY8vtdqNQKCCfz+u1sB2Mdcnk+AA3LV8UUCjkkWXSaDQEsN5sNuh0Osjlcjon6XiKxWISLjabjf7M7bYi8ojMZjNSqZQiGowekVdCpglbkxjJm0wmaLVaePjwISqVClqtFrLZrGrgASj6R1cL4biDwUB8Gsb46AZiDXc+n0elUpHbiOfzdrsVq8rtduPy8hKBQADJZFKxrfv37+Ps7ExOB57HsVgMHo8H8/kc6XQazWYTvV4PwI17ptvtwuVyodVqqRIbuKmlTqfTqr7OZrPo9/uYTG4aEVwuF+x2u2JNdGncjsyQo0NOy8nJCZ48eSIXEsUfAn4Ju+W12m63MZlM1JpEl9XtWu1araaq8GAwCIfDgV6vh16vp4ptr9cr9x0jdnSinJycwDAMTCYTiYr83MbjMXK5nFgrhUIB4XAYZrNZQjf/Djk6rVZLIhAFgUajcScyaRgG0uk0LBYLnj17hu12C7/fL5cOK8LNZjOur6/VlnV1dSW3SiKRgNPp1LkbDodRr9flUszn86jX64oO0VnHRiae/8+ePZP7xuPxqJ5+OBzeaUEjm4qCHyvH5/O53IB0UTL+xe8fi8XU3EW3Jf9toSPRbrcLZEz3k8PhQDQaRb1ex2Aw0H2B/9Ywtnjv3j20220sFguJPU+ePNHDB/470Ww2sb+/j06nAwB3mvs2m43uT4x/7mY3u9nNz9JYsxmc//E4in9oi+If2uL8f51F549952Uf1m5ewoxfieOV46+2ALcNegGzGVu/Gyb7jpOzm6/GvLCQQ/YEG6YIwOSSQbjlcrlU/Ik/qPPp7tXVFfx+PzKZjJ6ml8tlOSa44BL8yWU4GAzi/Pxc/AO2PxGyHIlE4HK5MBwOxQVhBTQrmbmclctl2O12uVYYgwCgOMbrr7+OTqeDwWCgmBHhrdlsVksYl+9nz57BZrOhWCyq7rbb7cqeT/Apj306nao9yWw2IxQKoVqtSmwZDodyJI1GIwwGA8F3Gdsxm81IJpNot9sIh8OYTqd499134fP5sL+/r9gAF1hCjikekJvi9/sVweD7TWZFtVpFOByG1WpFt9sV1JU102RsMP7R6/WQzWZhNpvR6/Xg8XhUG854yO04GFkldHDQhUFnymAwQDAYRLFYVB04XQnZbFbH8fz5cwkQ0WhU0On9/X2sVivM53O0223U63XEYjGJeXRr8HsQyBuPxyWYUAC8zddIp9NqlMpms3j8+DGy2SycTqeEiEqlgtlsJjYL25Gq1Sry+bzakBgrstlseP78uZwar7zyCp4/fy73DRuuyNJJJBKoVquYTCY4OjoSB4guDDJa/H4/hsMhhsMhcrkcptOp4K6LxQKhUEjRLV47wE2TltlsRiQSUbSk1WrBarXC6/Xi7OwM0Wj0DnPK5XLJwTWfz2G1WjEcDuHz+dBqtcQ9ogvltuuOzhWKgqyHZgxnOBzeAVAHAgFBdunqAG6WcafTCbPZjH6/Lxeg3W4Xs4qAXgByDWUyGbngGo2GRE2/34/XXnsNpVJJkGafzyfnGR1SjDjRPUfuUTgclhDd7XYVfWu324jFYhIc1+u1BCuysur1Oux2OxKJhOJbDx8+xPn5OYbDIQ4ODu7EJykAkQcDQHHFYrEoZw3bBdmI5na75WjJ5XJyLHY6HcXNbjt9ksmkmuB4r+D9jcLWYrEQI8jtduvewJp0VtTH43E4nU79HplAPp8P19fXEtfoskulUgIYj8djzOdzQZZDoRDOzs4wGo1wfHysa5ftZi6XC6PRCB6PR8L6crlELBbDcDiEYRiIxWLo9XpoNBpyQe1mN7vZzc/aWPf38OTP5rCJ/yiesk4v0X9gfK7AWgCA2QLTN16B6ZuvwvLw9PP93rv5mRlrKgnLvWNYopGf+Ged1R/vmDG37DDmnzwetun1YO6PP/Hf281uvujzwkIOAIE56VBglIBLHN0x6/UavV4PnU4HgUBACwTwI3gql55EIiFALX/w5g/a4/EY0+kU/X4fR0dH6Pf7aDabyGazqjPnkkbLPh09bCTh4suIRyQSUbMVnxyT4UB4a7fbFbyV8Q/GohaLhRqnKAAdHh7eAcTSwTObzTAej7WM2mw2xONxVKtVQT3ZqsW69vPzczlbuHzyNbHGnO6TWq0mKCk/HwBymxAme5uhQkeE2+3GbDZDrVZDLpfDcrlEt9uFYRhwu91yZ3BRfuWVV7RMsj3G5/PB4/FIIGEcZTabqW1rMpnAbDajUCggnb754cJqtYoTUiwWVZvucrkUEyF8l+6B+Xyup/l0iVxdXWG73eLw8BBOpxOlUklRt2AwiHK5LAAzXVH8+7c5QYyzMf7B1iw6YdgmdBuMysgXHWKMJvV6Pbjdbni9XlV/T6dTuY5SqRQmk4mOx+v1arkk+4QtPGxSomhGUKzZbNb5SmfQZrNR1TVjVhQ+eW1S4KjX62IZtVotLffD4RAmkwm5XE7nWTAYVGMdl1wu+hQF+X5xISdr6OTkBM+ePcNqtcIrr7yCVquFTqeDTCaD6+trcUrcbreaxNiURdcaj5kNSnRyvP/++3j48KHq12OxmEDqjA3xmqNI0Gg0kEwmdU2ZTCZYrVYJrZ1OR0wq3hvoyAiHw3IK0oFC3pTJZEKlUrkjPmazWbl6+DXJ/aFwx/eVXBiLxYJEIiF3Hj8PNmeRvUWBFIBq0JPJpGDDFCop/JbLZd0TyOOiO4o8oeFwKMGF1fH8Wo1GQw4tilT8vDOZDGazGWw2G5rN5h1OGEVwOpp6vZ7gyeQ48drkuVCr1ST0+3w+Vbv3ej3M53PUajVB0NkuuF6v0Ww2Ady4QynWUazNZrPweDz6/EejEXq9HvL5PCaTCRaLhZxXBJrfv38f5XJZsPLd7GY3u/lZmtrvzQCBj0ZpttEVOr+097kdh+kbr6D9x38ez/6UE2d/yoGn/2YI+PnXPrfvv5ub8Txt49HV5yzg3RprNoPq//QQ1384jsYfOMXy97z58X94u8HB3ziH8/lH+XOp726x+bQu2B/uBgBgjv1kMWk3Hx3jF99A949+B90/+h30/hffAcyWl31Iu/kJ88JCjt/vh8Ph0A/JT58+xdHRkcSDxWKhJ5usBGb0wWw2K3pzcnKC4XCoVqV4PC4HCp+Y9no9+Hw+CQ5spiGbZjAYwOPxwOv1IpVKqSGKUZNer6eox2q1wnA4RCwWw3q9FqiWP+DzNdXrddhsNrjdbi35FKhutyWR63B8fKyn8Y1GQ0sPAZvlclnAXkJZp9PpnSgVIaybzUaLutVqlethOBxiuVxKbGDcZrFYIB6P4/r6Wk/X0+k0crkcnj59egcuC9xElVKpFObzOYrFopw3oVAI0WgUP/jBD7BcLpHL5ZBOp1UNTL4N421kV5A/weXT5/MJfu10OtUUxcWx2+0iFosJTgxAT+pns5miTnQS0DXFCBDhtOQLkZ0B3ETN6IwigJqiHjkwdGs9ePAAzWYTm81GcO5ms4lUKqXPisIMWScUkDKZjJwSjUZDEF2yb26LGYxwEbLKZZlsFwqbDocD9XodkUhEDVWEQy8WC0V5BoOB3F2lUkncFjaNbbdbjEYjvcetVguxWAw2mw2DwQDtdluRreVyiXQ6LXGMgNtisShn3GAwQCgUwmw2U4yIDBfGGCORCDabDZrNJhKJhBxp4XAYm81GIkUmk5H7Yr1ei7fTarUkAIRCIaxWK0Vc0um0ImIE7VIgJpSXjqZgMAjDMFQpvr+/j+FwKBcGPwO2WDUaDWQyGQSDQTnU5vM56vW6mEPATdTIarWiVqsBuAHysn6c7ploNCrHFFuYvvGNb6BWq6FYLGI6nWI+n+O1115DuVyWUE0HDa9xtuMxBppMJiU8UfSOxWLi8TBO5vf79ZoYk1osFhJjyNNJJBISBX0+H87Pz3VvJkCckHCr1Qq3263WQULQF4uFhJTf6mhMp9Ny5twW1smYYWwWAK6vr3UcNptNsGX+O0HuVLPZVBvbfD6H1+tFOp1Gr9fDcDiUyE2hl1yuYrEoF04qlcJwOLxzzrD6nPc2xgV5HTH2d7tlrd/vf7p/XXezm93s5ks8JqsV3df86P3CEia2mHvXOPtfumD65qsv9di+arM5v4Sj9HLiRJZEHKU/nMcidPO/ZwkTGt+0YfoHvgXT7bIAkwmWaOTGsbNYYP8/fwTnhQPmjg2Wph33/+MuvP/k8ac/kPWPuDtbn+fTf52v6JhfvY/zP2JH53cv0fndS7R+aYnmn/jWyz6s3fyEeWEh5/z8HKHQzVVKoWEwGEh8MZlM8Hg8mE6nigIQMswYgGEYqFar8Hg8qgvmD+xctujGoEui3+/j537u51Cr1QS13Ww2qjgfj8fwer13arktFovcEyaTCclkEsPhUDXbBJUyMsDIx2w2w/e+9z3FoegAYT317SfhfCpP1gjZPHTg+P1+FAoF3L9/H4PBQAsHlwqfz4dAIKB2L0axCGomH4NMmmaziZOTE0UgGJEhvJkCUyKR0DLKeBhFkna7jTfffBP9fl9ujMVigXw+L74FHTSGYWh5TSaTuLi4UPSi3W7LlcGqb7aC8f2iG8lisWh5HwwGOD+/qbPkE/Xf9bt+l4Q31oU3m02JOFz4GNl7+vSp2CK5XA65XA61Wk1gZ4/HI2hps9lUBC+bzaLdbqtmHoCgtIzgECL9/vvvIxQKIR6PI5FIYD6fS1yiA4p/nu87gc50ObAthxymUCikCAirtClUUkygU4RxxGw2K5EQgNq/2NxEMaFWq6mVar1eq5UqEAh8xC3n9/v1371eL1wuF0qlkt4zXhvb7VaMkeFwCIfDIadHNBrF2dkZ/H4/7t+/j/fff18iJpuYttut6rlZ4c6oDRlKFNQo4lCcoTDF1ilGNu/fv4/lcinxo9vt3mleIpenXq/jzTffRKvVQiAQkPuDvJqrqyu5LH6r64MCFO8LjBZRdCBE2O12o1arIRKJ4Pr6Gh6PB4lEAo8fP4bL5VKzHvk5PBfIEKO7JZlMolgsIpvNolgsIplMYjweo91uw+/3y9XGdqb5fC6+TblcFk+mWCyqMdDtdsvxBdwIuTyPyfIhuJ7OpPF4jIODAwA34ijZPXwNFIru3buHarUKAGq76vf7cDqd8Hq9uLy8hMPhkKOQonkikRAXzel0otFoCLxOCLHb7cbFxQWOjo4U8yNcervdotlsqgmsXq/j9PRUbWsU3HK5HMxms+6bLpdL1ya/h9ls1rler9flRmJbWD6fh9lsxnA4xGq10sOK3exmN7v5WZnw0wWMxScy1X/mY9nLov1Ly4/8vsm9wdm/7sGP1J3dfFHm0bMsMPhkEaXm7z/C+rfqJiag84oFePUYAGA9yGP9P/o6rv/kPVz/yXuo/OuvYvXqAfL/6Q9w9G//Bg7/3FvYPL/CdvTp69PXlSqw3sI0XcC0+RiY8m5+/JhMaH0rBJNrc/u3MM4bsBwfvMQD281Pmhf+V4CsjPl8rmWhVqvpKTmhm4QUA1Bswu/3YzAYIBwOqyGIbVGsMWaMJRAIqFqbNc3NZhM2m0014+v1WlEYCgpOp1NPxsm96Ha7mE6niEQiCIfDgnQyPnR7sWRkLJlMCp45GAxQq9WQTCYVCeCCEwwGxYVgHTaXLcYJuByyBWcwGCCfzys2RncFocl8/YZhSLDhe0iBg24Jtsa02229Fi44w+EQh4eHeg9HoxFCoZB4NHzPGUFjdIUtVMFgUMsNHQcAFEEggJWC3Xq91jGRx8J40W1X0WKxwOHhIUajkY6JsONAIIBIJIKzszMcHBwoFjSZTFCtVgXCzefzsNvtqFQqAk0TmMr4iGEYai+7vQgzfsQGJApvFA6CwaAWUL4HbB8DIBGQIh85Gnx9FDEoMl1eXupzGo1GqleezWYCOrMZyG63K/Zlt9ux2WwkOLndboTDYS2/TqdTdeftdlsQ6SdPnogZBUBNYRRJg8EgJpOJKt/ZeEXxB8CdCm6KPsvlEplMBh988IEW9ocPH+Lp06cSR3h9ADcL/m0uVLFYlHPLbDaj1WrpfHY4HGg2mxK4uGCzvajX6+mewAgNRRar1SrxlpGezWaDk5MTuVNY/T0ajeT84/2FnxnvFdFoFJlMRvE0umTm87niT8FgUNcrXUX7+/uoVCpyNBFEzgjmbDZDNpuV44wtTO12W583eVSMIbGlj+cnhaHxeCy+C6N5TqdTUaZSqSRxDIBcQ3QC8trnOWez2TCfz3U/Y/MZnVpkLQ2HQ9hsNkwmEzGims2m7gO8NxiGgeFwqNd/uxmQUUYCzHn98V5AJ2ev11M01eVy4fnz5wgGg9jb29P5Q34Szys6evg1CZg2mUyKtYVCIZhMJrntCMXm8fDfNgqXs9lMrsvd7GY3u/lZGss/fQeWkQXG5q5YYqnbEfnvSi/pqHbzMufw73VRGQQ+9d9fb82I//fWTxRtMn/tARah316wsyYTKP/+DGq/8CN3zsoPVH7Zjd6vvf6pj/fjxjSZAZUGLIn4Z/61v6xjstrQ+cZHxa91bIXpvehLOKLdvOi8sJBD5k0wGNQCyVabRCKBbrerWBDdBuv1WiwRiihkLNDOzoWUENvJZKIoDl07ZC2Q2TGfz1VtXigU5NLhkptMJmG1Wu/AlunQmUwmEi0Mw0CpVEIikRAQ2OPxCLhMBgqrjrm0kZ9iGAb6/T5MJhN6vZ7AwKy1drlcqrrm9yVjhBBNQkdPTk50nKz/3dvbkyg1n88xn8+Ry+XQaDQwHo+1+NH5MhqNFBN69OgRPB4Pjo+PMZlM0Ol0YDKZJLYRXMq68ZOTE7Tbbfh8PlSrVS02ZN/w+1DEILj4+PgYy+USrVYL/X4fsVgM0+lUsS4u9GS9UORh0xbdFeRuEIxNpw/byBgDoVuJi+319TWCwSDcbrcqvykUsIqarCOeQ8BNtCuZTIqZFA6H8f3vfx/RaBQulwvn5+fYbDYS8ehcYYU0XTHT6RTJZBLb7VbnNuHcdIwlk0lBegEo2kYGCqNKnU4H+/v7auBhew6X2k6no3Yjfp4WiwUWiwXFYlGsIMJ0b0OkWb+8WCxQKpXwjW98A/1+H4VCQVDearUqFwQFWcYlKSZ4PB4UCgXV3LP5qdls4lvf+pbuDax4JhCdfKfVaoVUKoVGo4FEIoFWqyW3C9kprOcmm4ZNQrVaDa+//jouLi50bdFJYTKZ0Gw24ff7YbVacX5+rjgo40anp6cSaPgeHxwcoNfrYX9/H+fn53LFmc1mCRds3Eomk+j3++j3+zAMQ/FKu90u7hKbqni/Ozk5wWq1EsPFbrfjtddew/n5OdxuN54/fw6TyYTDw0Nxc9jIB0AiI0UwcnTsdrvYU36/Xw4ssq0Y9SJXiRXgVqtV91MAEnEYR3vw4IFEObrggB/B0Sk+sc59NBqhWq3iwYMHcmvRfUf+jdVqlbBGphAjeARdU7yKxWKKJjJyR5F7OBwK7myz2dDtdpHP5wXbH41GgooXCgU56sj76ff7ilMxIkYmF8/xZDKJJ0+eKEoaiUQEGt/Nbnazm5+lOf7z7+Dw/2XA9cwB1zMH3E8dOPnLz34m6sNd5R1b4/OezaNnSPzHn6y6m7M1TKj8wzyCf/vtT/b33n8Ke/+3f9ixPE5h5f/x/22SNsOaSn6i7/nbjXk6xzYagHGQgcnl/My+7pd9jNUSp//P+Ud+331uh+MfvfMSjmg3LzovLOTQ6u5wOPRD/WKxUAxmb29PrhryEeh8ICjWbrcrMsQFg24G8iFo0+fSz69rsVjgdrtRqVT0ZJU/0JPvwSUWgGIVfEI+HA71FJ1NQrPZDADE5+FSYzKZ0G63BUYlrHSz2agNx+v1qlqXC+j+/r6WYLa+sIGLzU+0+tOBMZ/PkUwmcX5+DpfLhWAwiF6vJ8FlNpuhVCrha1/7GsbjsSIyy+VSHAuKL1xI2BrE+Ai5HGazGX6/H7VaTWwXui/sdrtiJVyWFouFHDbhcBh2u/1O0xMZKxTiuIDz/efSRvBoJBKBz+fDZDJRDMlmsyGVSuHs7Ayr1Qr5fB7j8Rh2ux3X19eIx+PIZDIYj8c6VlawcxFjpIrtRRTj+L/JUZlOpzg6OpIo1mg04HK5xNPgOWiz2RCLxdBsNjGZTNDv99W2BtxET+bzOeLxOHw+H0qlkpwddKjx/aKw8PTpU6RSKaxWKzWJ0SUUi8WwXC4RCATEkmJc5+TkRJwbOrN6vR622y08Ho/cDjxHbTYbvF4vxuOxHFK8zlj5bbPZ8MEHHwiIy2NhdIqiUiqVkiBEkPdt9xUjWYyXDQYD7O/vA7gRqRj3ooBJ1xxFVIqWFBjorOt0OlitVjg8PNQ16/f7YbPZUCqVJHYFg0FUKhXdW1KplFxIPIeAH1W/M/aYTqfVfMfvWSwWkcvl1Jq2WCwU0yQjZrlcIhKJYDqdytXCWBLdThT0DMPQ+0q4NKOKz549g8fjgWEYqquniMZ7B4Uti8WCQCCA9Xqt+wejrA6HA6lUSucpRWPG2ehUouuE1z4ZN4zzMZ61WCzw/vvvI51O61yg6EgWFZk9BHnTFcf4GMUZCvlsRaPgxQhuo9HQcTLCSpg6I390/lFc7Pf7avaiY+zy8lL/RlGQymaz6Ha7En1Xq5Xu6/P5HP1+X4KxxWK5w9AZDodIJm9+qCQfjFGy3exmN7v5WRpjtYT1//d9ZP/Cd5H9C99F5i9+F5tO9/P7/u0uPI8/CqwFgPzfb92Bz+7m8xnzVRVP3s1/4r+32FiR/RvvAdtPGEkyDJh+u4/ZZEL9Fz6+/XHjBIbf/uTH+3GzrTcBkwnm7qePaH1Vx3JVvwOgNtYmpP/Z9JOfE7v5XOeFhRw20kQiEYzHY3g8HpjNZmSzWYxGI4kMtVoNTqdT0NzNZqOK8slkglarhXv37glYa7FY4PV6Ua1W4XK5FKHhD+VcMMmv4MINQC4YOhUoKAyHQyQSCbkw2PTi9Xrh8XhQKpVQq9UQCASQyWTEnXA6nahWq3A4HFr8GaVhROl2nIPCBAG37XYbk8lE3JJoNIpkMolarablhuDcer2uSBHjWmx7slqtel0+nw/hcFhP+b1er0QqVu8uFgtks1m43W4sFgs9mb7NK2Fs4PLyUkvxer0WO4PxMApmZrNZ4hYjDgBQqVRU9XsbwMzIEpuyWNVNCDQXTuAGcD2dTgWmns/ncDgcCIVCGAwGclulUinBgikOjsdjhEIhMUEIIG61WsjlcjqnKM4RtGsYBnK5nKJCDocDDodDMZdisYhQKIRkMonlcqlYH0HPFosFvV5PIhxjZ8BNhJANOFxkCXpltI7niMfjkYPEMAyEw2FFnB4/foxIJKIaZ36+ZDk5nU6JFoQnT6dTOUqazabYMvv7+3J/kTPj9XpRKBQAAIlEAvF4HJVKRQ4NCm/D4RAAdO2RDUWBrN/vo9Fo4M0338Tz58/x8OFDVKtVQWnX6zUODw8xHA4VBWJMqtfrycWXSqVUyU1hgtyU0Wiktq5AICBxh+4kNkptt1sJoqPRSODyBw8eyPHFaByr4yk8AZBziOcVBSVex6wOH41GYvhsNhu43W5F6/h16G46OjoSbPzs7EwOl2AwKFGb8VS6VwCIOUbwOAUi1sSTsXN9fa046GKxwMOHDxUTJbupWq3C7/dLdKcwxIp1RgaHwyFGo5FaBff39/Ho0SN4vV5ks1kUCgWYzWY1s9H92Ov1sNlsEI/H0Wg0BFi+HVtkU5vH44HH45Ezh8yaUCgkYbfRaMhFR1dSKBRSrIuRx06ng6OjI0WsCMi22WywWCw4PT2VCGkYBj744APxvui0CofDsFgsYmgRqnz73ysKQuPxGFbrj69J3c1udrObr/JshkNk//p78HzogKVph6Vph/upAw/+fAXb86uXfXhfydm0O7j3Hz3Bk3fyKPWDP/HPrzYWFP7hAZx/zMD2hz8XfdKJ//3nsE7u/p5tDEQ+3MD04fNP9TV/p2OMJzDGE1iCnz5q9lWbTaOJ/b/2IR78+Qoe/PkKHv67JZj/+Q9e9mHt5ifMCws5ZFawync+n6t+mE9uWV28Wq3QarXEJmA0YblcahnkD/cWiwWr1Ur/P3/IbzQaisYw1hCPxwU4XSwWiEaj2G63ckWwypk/mPPrUdwghBO4WWRZC84GE/IcCCamINXtdtFqtSRe0A2wWq0EdE2n06jX6+K3ZLNZ+Hw+XF9fw+12KyLh8XjEYIhEImpKCofDMJvNgjdz0YhEIlitVmi32xIiCJuuVCrI5/NqCnv+/DlSqZTauOLxOC4vL/XknQ4MiitckCyWGwssIaG1Wk3i13A4RDwex2w2U7Sr3+/D4/GIb3O7xpkuCNZTE0pLcGypVBIDY7PZIJVKCVh7m33E781IBcUcArcPDg7QaDRgNpsRCoXQaDTQbDZxeHiIVqslobHVamnhZBSKIuJgMMDh4SHm87liLXQccdGPx+OqvGcFttfrRTQaRafTUT12p9NRrTSjHGyWoiOAXA+r1Sp+DEU2h8MBq9WKw8NDfPjhh2pJa7fbgnaTy8M4CSukGZ1hRK7ZbCKZTIpD8vz5cy3MbD/iYkx322QywXw+x/7+Psbj8R3ulcfjUXtbIpHQNVOpVBAIBHB9fY1UKoVwOIzf/M3fRDqdvuPIuX2sPp8P8/lc7/8rr7yC8Xgs10yz2RS82Ov1in3CRjCr1aoWPDpQYrGYWpPYPHV9fa0mOJ/PJ27QkydPxKyJx+NyWzDmRTE2FArJGdjtdpHJZNT+tdlscHl5iWQyKVeU2WxWBfpbb72Fvb09NWElk0m0222dW2xT4nnN95kV3KlUCrVaDVarVXFGvvftdltR0Nlshs1mI2fgcrmUeJrL5bBerwX5TSaTul6m06ncixQTKXpT+GGUkVBtv9+vCCcFxMPDQ10PjJ/SycPoG7lHs9lMwisjTgTh53I5vcetVguJREKurdFoJHGfcbDNZoNyuYxoNIpEIiEBbDgcittD8Wm1Wsn9Rw5TvV6XIMv3keKv0+lUJJEuuWw2+zv4J3Y3u9nNbr68s51MkP4r38Xhn3sLh3/uLWT+4nexrtVh/BC2v5vPfzb9AY7/zG8g/R+aUPiHB5isfjyw/9HTHFr/7xzSf+m7WBdKn9pBtWm1kPuvCoi9u9avzP/jCdz/9f+A7XyOwMXHOzpMa8B7OfxU3/fHjbFcwjRdwMglYPJ6ANPLBYJ/0WYzHGJdq9/8qjde9uHs5gXmE53hfDJPIYE/kPO/3a4QNwwDtVoNBwcHerLMxZKNIBQgyHahM+X09BQXFxcCi9JFkUwmBTDmgk1GxmQyUaVzpVIRr8JkMmnBJmNhs9lgNpvJscKFmRESPlkeDod49913EY1G1dqyWq2QTCZxfX2tH/Tj8TgKhYKiFslkEuv1WsBdVqNPJhOsVisBSNvtNgAIWkrhqlwuw2q1IpPJ4PLyUk/Le72emDFcTOx2OxKJhOqNuWyRccJoxcHBgb4fhQw+Jb8NQeVnQr7L7RYj/qJAlc1mcXx8LMAyIyGEpPJzIQOl2+1KSOITd54PXPDI1GF8hM4hq9WK/f19XF1dKcZFZhIbv5LJJFqtFpxOpyDYDocD2WwWw+EQFxcXsNlsd9q1GIdiJIavwW63y5lCt1ev10M0GtX7TEGCtfcEcdMhwq/n8XgklpAVlMvlVEfOOuS9vT288847cDqdEs8ohvFzZeU3zz06i1gLXy6XVRnN+OLtFrL1ei3ALavI6WoKBAI4Pz/HdDrF/fv3US6X4XK59PVXq5XcCYx2WSwWVCoVRb4YA6LIQsFvu91KIAGgc5HxSTrY8vm8rkUA4uPQqUK2lMfjwWq10mtm5IkONTpHFosFisUiJpMJcrkcEomE3ldGARkTpRsjHA6Ll5PJZBRdTCQSKBaLEpTpfCPni5NOp/H06VMEAgHkcjkJXhT46JKzWCxotVqw2+1yizD+NZvNEI/H9RroguS56HA4YLfbVZG+Xq9ht9txfHyM9XqNTqcjMY4xVcbEeA34fD4JpIyvkflFFpRhGGIPud1u3WfI0GEr38HBAQqFgmKYjHYuFgtFsTqdjvg98Xgc8XgcnU5HIpLT6UQ0GsX5+TkePHiARqMhADyFwGw2i6urK3i9Xt0DO50OhsOhQMuj0Uj3YLfbjf39fcxmM1xfX2MwGOg9oOA4mUzkguS95Pa9mk7C3exmN7vZzW6+KLP9wWOk/9J3Yfv3g1j8u4mP/Hr4n1QR+xtvfSbfa12uwPn/fVu/Nr2e/lvgN6swfYyWY5sA2/eefCbHAADGeg3TaAKYzdgGPIDlZ0PIMVmtMFmtgHnHjtrNZzsvfIZXKhU5SvgkmI0lfDrvdDrR7/flfvB4PHc4MWRLTKdTVXG73W61LQEQU4E/iJ+fn+sJLaudCQD2er24uLiAxWJRtGU+n8Ptdiu2xOpqPjXfbDYC5bLhhJwbRjhisRgKhQIikYhEA5fLdeeYAoGA4k3k7rDenIIQ3S6s145Go3j+/DkqlQoSiQTcbrcEm0KhIJs/n4ozHkHHBuM5bLzhYt9oNCTKhEIhvP/++4hGo4qB2e12lMtlNXJx6WPMjNEhckTeeOMNAamXy6VcGYxC0XXDr2OxWGA2m1Gv17FardBoNJBOp5HJZCTokEvBc8LpvIGQ0Q3l8/nkrKGri1XhhKp2u13xWQhrNgzjzpJJvka73VYNOpuhXC6XFuZms4lEIqFGNAoHfI2bzQb37t0Tq2e5XOL4+FhRM1Yq8/h6vR4sFotq7K1WKwqFgkQyp9OJSCSCfr+vdiCXywWr1Yp8Po+rqyu5EUwmkxZrk8mkKM58Pkc2m9W1cFvcIVSYsaV//I//sWJRsVhMIhNdCWTqjMdjCVGM9h0eHuLp06dot9vIZDISOiwWi6DhFEPMZrPiiWxTcrlcGI/Heh8jkYi+H10oPp8PhmEI7Ov3+8VZobj6+PFjRKNRRdHYmEY+E2OTvBdFo1E1mI1GI0SjUQlsvCYZoSIwOhwOY7vdotfrIZPJqMo7FotJQOQxk8/idDoFeiYXjNcTG5EcDgfq9bruZXTEkS3E1ii73S7XDeNPPGe73a7upeSKVatVZLNZnYPb7Rbf+MY35Eik64ZMHja+hcNh5PN5PHr0SFE3cmsYH6VTjuIQWTQHBwdotVp3gPa8l/b7fSQSCZRKJUSjUZ0nvC4Zd6NwmMlkJFQRtr5YLBCPxzEYDNDtdvFzP/dz+Gf/7J8Jks2IGN2ShmEoAknelM1mUzwwkUigXq8LIl4ul+XOpKhKlyQdcnzNdM4FAgGYTCY0Gg3dG3azm93sZje7+aKN6a33YP7nP/jIr3Wp/Ll8/3WhhNw/HsFb/JHjx7IA/FdbpP/Os5/ON91uYe6OYGTiL1U8sUQjML96H8M/+E0M/+A3sf7lN2DN5266vXezm89gXljIsVqtEjXm87kW181mI2eFxWIRjPc2Z4M/vBNkzDYbCh1cTCm4sK3E5XJpObVarahWq/rf8/kc6XQaPp8Pl5eXaiNyOBzivhCkDEA14ZFIRIs0+TzD4VBui729PRQKBTidTlQqFezv74tJw+aTVCqlCA3ZQDabTS4hu90Ol8sFn8+HRqOB2WyGcrmMdDqNdDqtqmS2VK1WK71u8jJui1C1Wk3Vu6wIZ204lxO3243Dw0NcXFzIwcOllw6pyWSCXq+HWCymel2+/+TrhEIhjEajO1yQQCCAyWSixYvLj8fjweXlpcCm5FIEAgG0221cXl4q5pDNZvXEPBaLAbiJXzF+RFEGuHFrEFgaCATkxOLizqflbA6bTqeKCrHBJpVKycHRbrfh9/tRr9dhGIZiUXwCPxgMkMlkBO8msPf2Esy2ong8LqZMIBDAYDAQlDUej6Pf78v5wnOGcQ5eQ8lkEoPBQMIJnS5Wq1WsEMJfKYKSD3N2dqaYGj8LLqQAtJx//etfR7FYFAuKYtZ0OkUoFMJwOFSMjdBaj8eDzWaD8XgMAIjFYiiXy2IkJRIJVCoVXY9Op1OuK1a7E4jL9q7NZgObzSaBxWq1ijvkdDrhdDrFZmL0zOfzodfrIZ/Po1wuix91+5yl84dOGqvVKkEym80iGAzi+vpa1ebZbBb9fh+dTgfb7VataI1GQ21ry+VSwHFeh9PpFLVaDffu3dNrXq/XElsputHpxNeay+XE3mF7FgDFLm02G5rNJgKBALbbrergo9HoHVYWHY82mw2GYej9oohE4ScQCAhkDEDXzv7+vtqmBoOBoo5kDB0dHSnWRlfcarVCOp1WdbrP59OxUDwEoJa+Xq8Hq9UKv9+v94SxKPLNer0ejo+PxbxioyG5XxaLBeVyGblcDuVyWVwnCoEUU3hP53tHOPF4PMZkMsHR0ZEcXWzuslgscn7xfPH5fGImMXrL94RQd/LCeL/azW52s5vd7GY3n3yM732A8N/5PrL/ZILsP5kg9w86CPzt38Cm3fnMv9d2MIRpvcU24n+pgoklFsP4F4/Qfz0IwwwYZmCasKH7CxmYX7//0o5rN1+ueWEh5/DwEJeXl9jb2wNws1gQgHt2doZ4PC7gJJdrgjobjYbcIuPxWA0obrdbESQuuC6XC9/73vcEJa1UKnciFev1Gqenp+j3+2JC/NIv/RKKxSIWiwUcDodaWCgUDQYD+Hw+sUTm87kcNf1+H91uF9lsVssllz2n04n5fK6IT61WEyOELiLGaBjpoOvA6/Wi3W6jVqvhtddeg9/vV8U2F2EKJo1GA9lsVkBUsogcDofAzmy+YYU4l9FUKqW4zXg8RiaT0VNuLkGEdxqGgcPDQ7Tb7TvuCTYzkTWyWCwE6CXPg81XbIoii+K3uq7452KxmOJ20+kU/X4foVBIMRlGitLpNDabDbxer5w76/Ua5XIZ3/nOd9BoNDAYDFTxzeX4Ngw4HA6rppp1xWzEIuuHbBkunalUCovFQhE7spOsVqu4R4yQEIAbiUSw2WxUe/z8+XMkEgnVkVNM4ft1WxBjGxnh1z6fD8FgUJDvWCwmUe3i4kKfO6Hdm80G19fXAjLz/bfZbqomyU5iSw8/BwASQzKZDKrVqhZ1Cg6tVgvj8RiRSERAabYf2e12tFotXeupVAqTyQ3Vjp8F40jtdlt11eSZ8HXw2qdoRIcZAHzwwQdyf92G0tZqNQGB6QIhc4eiDZ0r5Oi43W5cX1+LO0XxmfEgr9cr8bBUKilGWalU5Dacz+dIpVLwer14/vw5otEoNpsNBoOBYmoUAikwHB0d4erqSrFGcpry+TyazSYuLi6Qz+clQFFoYBW60+nEbDbTa3S73eh0OopWMT6ZTCYxm80EvT48PMRisZCTjM42fj6MHW23W6RSKfR6Pdy7d0/8rbOzM0VXY7GYGqR4bW02G32uJycncgtR6OY1nkwmdY/p9Xool8t49dVX4Xa77/CdGHfkNb+3t4fZbIZOp4O9vT2xpoLBIFqtFhaLBUqlklqlKIp5vV79vtlsRjKZRDQaRblclsvrNufHbrdjMBioIY33SF7bjKjSVUmeUbVa1Tm9m93sZje7ebGxZjOwHu7Deri/cx/sBsBNy5rprfdgeus9bB59vBPHcnyA2r/9C3d+WY4PXvj7bKdTYP2jLJfZ+eOb1X6qYzLBSEWw8vyYNdsEzDJemByf33FZImEU/4NfwPlf/Tae/2ffvrkud/OlmBcWclarFfb29mThJ7S0Wq0q6pDNZvV72WxWP0jzCarJZILf75frg04cxofIZ4jFYjAMA9VqVcsYIbV+vx+tVgsWiwV2u10g0MViIZGGnJvRaIROp4OTkxMxOeggIgCTT5v7/b4qs6PR6B2xgH/2tnvh4uICJycnYkGQ/9Pr9RAKhdTeZbFY1ODU7/eRy+UkwtDpwhjHeDyWU+I2JDafz6NUKqHdbitqQPh0rVaDzWaTYEP3CYUVLn0EjRJ8TPi0xWLRckiXErkZhBQbhgG3243xeKxFlDGvyWSCfD4Pt9utpiE2FLG5J5VKCU7LeBEdB+VyWaBfupbC4TDW6zWePXuGfD4vJwKhp9vtFpeXlwiHw1o+6c4BcAeu63A40Gw2MZ1OcXx8rLiWzWZTRJDn1PHxsWq6A4GAmoECgQDsdrucUGw1C4fDYh2FQiFFjcxms9wPBFsTElsoFBQdYuMR279YSe/z+SReMSrDViA6MeiY4PtMADJFAvJbKIpMJhOdW3S8RaNRnJ2d6f0eDofiplBUGY1GckcRak5QLpdk8nAI56VDabVaKRbocDgkTgQCASyXSzx79kxcl9uuJMbdKE5QsLndWkUHoN1uF3+KQmooFEKlUhGUvN/vS1w7OTmB3+9HqVSCxWJBtVrFer3GvXv35GajeEUWWDKZlJvvdq01webJZFJV7Ht7e+j3+3A4HDCbzXj06JGg0nRC8Tq7zZyik4pCJGOLfA3kiLHRyWq1qgKdAHaKoufn52K+OJ1OAamHwyH29/fR7XYl0oVCITmA2BjHz5JCO12P2+1WzVKskx+NRnK2UfACbhyFz58/lwNnf38fw+EQHo/nDjSeLkvWsbN9azweI5+/qSTlfT8UCuHJkyeo1WqqHw+FQmpr478xuVxO/17QJUVxx+v1Ck5uMpkU0WW0lOdYPB6XUFQufz72893sZje7+TKM+fX7ePJnc3jy70fw+N+LYPCvfgvmV3cOhN385LGcHqH0B1JYu3HnV+nXUrCcHn2qr2nKpj7jo3yB72m3o/9K8GP/+yJogenkxcWp38lYYjFc/8n7WBwsgMAKRmiFp38qCcvD08/l++/mpzsvLOTwCSqBpWSbMLqy3W5xdXWlpX65XGKxWKBWqymG4nQ65RoZDAayzFPsASAwKJ/ev/LKK6hWq5hOp3JEDIdDRCIRwWMpusRiMZhMJgFSCdRdr9dqfyJPZrlcqvGH7B86isrlsvghAPS6KQiNRiNkMhk1yxACzcacJ0+ewO12q0WH9eg+nw/FYlEAZD7dZm0xlz/yVBgXYDyAwFouKD6fT2BhLqV8mk5GD+NqFF4ACIRMtwQdDYya0S3k8/nkEuH3Z2whnU7fnEA/5HfQ/UE3DJcy8lkYn5rNZojFYuJ7UMygQ8hsNqNYLCrSdbsqmnBXfq4AFP8AgGQyqTgFQa/j8VjOpU6ng9lshkQioVpwPuGnOMUFcLvdwuVyydHldrvl2HC5XHIRsEZ5uVyi0+kIik1BkG1YAMQIofOo1Wrh8PAQ/X5f9fY8b8gTIveDjWs2mw2tVkvOB5/Ph0QiIaYSBRbWoPM18JriEk73Ac9xtsnx9W02G+zt7emapbOp1WqJQ0V4byKRkOhHkScajaqSnHE7RnqAG0cb27gqlQomk4mcHnwfGblarVY6b7lcky1DITQYDMLpdKJQKCj6YzKZVG9OsbLdbqNcLuP4+BjpdFoNTowGDQYDvU9msxmr1QrdblfiqNfrlXjbaDQkkK5WK7XAsRlssVjoOqEzkQD35XIpUZWQdDLAfD4fms2mBGe73Y5Go4HhcIj79+/feR8Xi4WA1BTwvF4vnE4nms0mFouFnDvn5+dyxBGgvdlsxDgisJkcMjoq1+s1stmsIoXFYlEAd95TGAH99re/LTcjBRngJvJKQarZbOL09FTcH4pZrCFnYxldmHQoNptNvPrqqzCbzbi8vNT9IZ1Oo1aryYm2WCxwdHQkdtt8PhcI3eVyCc7NaJrb7ZaQGgwG0Wg05GLkQ4Xd7GY3u9nNTx7L6RHO/mgQCNz8u20yAc1/YYXeG8GdM2c3v+1Y8zmUfzWBzY8xqmycQPlXE7DmXqxF0qjUYRrPYBrPPuOj/AJOLIT58eLObxnhFYYPQy/pgHbzWc4LCzkOhwPT6RTJZBKdTketQgS7ZrNZiQN8Ys5FmSDPZrOpJp3bT73JGqGQwKfSfFKdSqXUiBKLxSSsMK5DCCYdMH6/H91uV1ERijqsgeYP9ISGUmC4Hdlg/Iliw2QyQaVSQTQaVTQoEomoZppNUGazGalUSoISF2lGbVgzzPaa2Wwm1gRbsbhUrddrJBIJuQ64FLNJxTAMDAYDuR8I/WRVNBeQVCqFarWqKmKbzYbtdqvK70wmAwByqXQ6HX0ttj0dHR2pyjcajYpvEwwGYTKZsF6vxa3o9/tIJpPw+Xxot9vo9/s4ODhAtVqFyWQSQwaAjmOz2UgA3G63ODk5wbvvvitXAV0aXDD59wiuJU+D7Tw2mw2DwQB+v1+AXzplKE7RIcS/w/ru9XqNUqkEh8OBwWCAfD6P6+trCVp06gA30SdGUMxms7gbFKYYFSKzpNFoyGHCa2C9XiMej8tBBUDv53q9ljOMANb1ei1X2v7+Pp4/fy4BkAwVOod43bLtzGQyKWpCNx2ZL+l0GqPRSBGier0uiC+/PmMn4XAYnU5H/B/DMOD1enV+eTwepFIpbLdbOYHoumk0GnJX0TnCc2EymSAajaLRaMi1xLgdI5KEEDscDrmIKGwRFs5GLQq0vB/1+3288cYbGA6HaLVaCIfDWK1WmEwmqqXO5XICQqfTadhsNrGBQqEQgsEgCoWCrh++Bop58/kch4eHYkItFgtEo1GdYxRJ3W43Go2GHGmpVArdbleOE5fLpQhpIBBAKBRCr9fTtU44OYXOeDwuaDTvzXRDEqoNQOcl3VOsLs9msxJCAchZRccirxsAcqj5fD74fD5ks1m02208efJEnxsdmmy/o1A6mUwwGAxwenoqoWk+n+u8JWCaouRqtRLn6fHjxwJ+G4YhRhEhxRR5GZFLJpMwmUz48MMPsb+/j1KphPv376uWnp+Px+NBs9nEZDLRucOGw1Tq83+at5vd7GY3X8R5+qeiMIKrj/x+43+8guVo//M/oN18YcZwOrB2f/x/X7sBw/njq9R/65hs1hvh0GwGrJbPNcb0ImNZGjCPJi/7MHbzJZhP1FrF6mTGLhKJBMbjsTgbfOpOEYSwYrITyLqwWCxiwIzHY8VI3G636qonk4kcM9VqFfl8HiaTSW1EtP9z2aXzhkwJMnu4+NAdwwWeT/krlYpgqHy67HQ60ev15Pyh8MDlgW4i1tuuVivZ+tleRUcMgayz2Qz37t2Dx+PBcDhEt9vV03G6WehSISOj3W7rab/b7UYul0MwGMTZ2ZnEKS5RFKr4tN/j8SAajaJQKCgeRbGLtfGhUEiskKOjI312bBzz+/0wm804PDzEu+++K6cQlyRCZtmWE4vF0Gw2xdIh4yIYDKr6eDqdyiFVqVQAQE/SKSiwWYtiz/X1NU5PTzEajfRaGImjQBWLxRTDIQSYTi/+306nA4/HI04KAdCMFs1mM3i9XgSDQQA3TixGirLZrMQ68nMIm6ZjimIOYbRcQsmDYc05hUqKC3t7e2pWIlOHCzPdLKxo5t8lx4WA3n6/r2p3VoQzckgHGt1RZrMZy+VSC+p2u8WzZ8+09DJKR5Atvy9ZRxSIKKLRFUL+CQU8Qpkp4FBcY3SNYgAAOVfYxkQWE5ktZGkdHh6i1+vJdUaBjowiLvylUknOoUQi8f9n70+DLEvs7D7s3Lfv+77ly6WWrKquRjcwA2BIDyVyRGrERVxtkrZCthxmWOGh+UEOW5RDJukIh4OUJixbIdGhGJsxIS5Bc6dM0XLQGtkYTg8G6EZ3dXctuWe+fd/3zR8S5yAT6G5UA41egPePyJhBV2XmzffuvZX/c8/5HbFsYrGYGrY2m42cRHT13Wy8o0jabrexv78vdhJwLbQxNkm4M11iBA8TVk4BjS6fYDCIQqGg15ki2+XlJWKxGHK5HKbTKY6Pj5HL5TCfz1XlTSYUgcB2u13XDkVACiZsoKLbz263i/nCiCKFzXQ6rc+fzWYIhUKo1+u3mup4fyTImxBywoQJmV+v13JF7uzsKHp1fHysNrub92U6N202m15/Qo/5updKJdWNM8ZFIDHdSbyXdjodOa0mk4nayxgLpmOQgs9kMtH9jnwoRkQNw8D5+fnL/jO5ne1sZzs/2/MhW4VhYOvI2c6nN4YJG7cDxmSGjcUEczDw8p/6lUfY/K4v3fowfZfp+LKzWSzhvZgAH2LotQ5WWF4WPuCbG6j8e7+Ay//91z/ww3x452Mdh47n+47DXLfB/1btR/pa2/l8zUsLOVyqQ6EQGo2GfuHm02QAqvHlUk0WyM0GEQB6ih6LxcTMaLfbaraZTCY4PDyEyWTCyckJotEoLBYLer2eHDt2ux2tVktP/SeTCYbDIQzDgNfr1fdhzXmn00EqldJyQnbMTTjwfD7HZDJBIBBAPB5Hs9nUU18AYoHQIeJyueQKCgQC4kJw4bHZbLBarVqsGKViJIYRltlshnK5jC996UtYr9eYTqcSi1g/HQgEFGEjKJpL5tXVFVKpFBwOh5gq5KzwZ+V71Ov19OSdjVupVApvvvkmIpEIIpEIxuMx+v0+0um0YlOLxQK7u7s4Ozu75Y5gpC6XywngC1yLLwC0EHu9Xr23dMZw8eUCRnGKAlwikUC324XNZkO328Vms0EsFpPgNBqNJLCMRiO9Z4Zh6Jzy+XwIhUK4urrCfD5HPp/H2dmZHDCtVgsnJydIpVIwm82KsrFBzO12w+12a/FnDI9gXcbsWAc/mUwUDaFgQAAuHVc8XtaBT6dT2Gw2ZDIZHB8fy6FydXWFR48e4eTkBKFQCJVKBdPpFPl8XlEUtg3RQcIon8fjUXvZer3GcrlUw5nFYsFsNsP5+TkCgQCsVisePnyIf/gP/6GYMGQ+scLdZrOh0+ng0aNHEkLJDonFYrpO+HPRtUTB6enTp0ilUnC5XPD7/RKy6KjjuRmPxyWMtVotRZvS6TQMw8CzZ8/kyKCrgo13FL+azSZef/11TKdTcZzC4TBarRZGoxHOz8/hdrvhcDgQCATEEKKbqdVqYT6fI5FI6P1ut9uIx+OKJ7F5iowar9eLyWSCVCqF8/NzCcAul0ttXHQQ8d5AMZDvH4VMitmHh4fodDqCm/Oexlhls9mE3+/Xe0/uDlvsGCWlw6hSqUj0rtVquHPnDuLxON588005ZHiforspmUzq/aeDJRKJoFKpyAnpdDol5IzHY4nsjAwyEkkOj9frRSQSQa/XU6tZOp3G5eWlmED8PIrZbL0DIBGP7DHywOLxuO51fFhAATIUCil5nKcXAAEAAElEQVSG1+v1JMBGIhHdg3jPoXNrs9kgHA5vHTnb2c52vjBj2O0w392/9fFp1i/bm59d1fN2vthjzOYwTz/8z81TwJj/oNvrg2bVu/5dazMeA+uPcQyvPUTvjgeDHcetj8nvewUm10fYhb5/1isY33wP/pPRD4g51tEajt989n3f2MDwT30VjX9yF6k/eImDX7z4wI/2r25w+h9/DeZI+KUPZfX8FOl/ZoG1ZLv+KNhw51dPsTy7ePmfZzuf23lpIWd3d1fLFX/pZaSBCwgAMU0YZ2k0GmJ7UHjgAmYYBgKBAIrFImw2G8LhsCqki8Wilh+CN6fTqdpnuNSTCVGtVhGJRLBcLjEYDDAajcSv8Hg88Pv9aqMiVHUymUiUIPOBDSv8pZ/iCB0KfPLv8/nU7MQ2q0ajIUGArwmBsS6XC1dXVzAMA6lUSk+Ol8slUqmUKspDoZBiMQQMc2ljm9RqtdJT5JuuBwppdBKxMYZPvckJqdVqcDqdEr3MZjNyuRwuLi6wXC7hdrvh9/txfHysn3u9XqPZbKqdKJFIKMLE5hcCiLkE07nF15BOFp4fZIK0Wtf1g+Qk0b2Sz+fRbDYlgtxkcrAKvNlsKpZBBwY5MgQWFwoFpNNpLfOEyVqtVoRCIf0sjJuw/Yqw2VarJah2LpcToJUujkqlgmw2qyjGs2fPBFWdzWZoNBpaptnyFQ6HBf0mBJfg3Gq1CgDY2dlR9IpiKH8mk8mEwWCARqOBO3fuwGQywel0KjbI156MEjo56JbjQs5q7263i8ePH6utiGLtarVCKpVSvKder99q/CFviZG76XQqAe1m9fju7q6YJIxZUZxwu90ol8sSzMLhMJrNpoTQXC6H09NTOZMYhfP7/TCbzbqOnE6n2uEo6gKAz+e71XpnGIYiTYR9MzJ2k8UFQGKn2WzG5eUlgsGgrgFem2x2s9vtqFarYu9YLBaJWwAUXzo7O9M1xwpuClKENNNBQ+cg+TsHBweKStntdpyfn8s1x3imYRgCN9PV1O12sbe3B5/PJ7GQzi/G/Hq9ngT72WwGv9+v95MCLYVhQoZjsZgE6na7jVgsJtcd79lmsxnVahXBYBD5fB7lclmNg6FQSJytSCQiJg/v0bxXTCYTtNttuN1ugYjX67XufSaTSewbCom8d202G8znc8RiMcxmM113AATVJi/qZhTOZDKhXq/rXNjOdraznc/7tP/M63j+HwT08ewvBjD9g1/+1L7/7t/+4Kf8jlM70O5+asexnS/eLC+uEHq+/NA/Dz1bfrCL5RMak9eLWdwFfIBxbOY3Y/PwY8KW1ytsvvUuAu934X820Ifn/3eM9eh2rGr8x34erv95GSlfHxbThytPEdcI979yiaO/eAem75oWXuY43H//m8j/b9/AwV+/QvobC0xfzWHx+78C03dZqdv54s5LCzkEhY7HY+zt7cFsNqNQKCAajcJms6k6ezKZwOfzYTAYSOyhI4VCCRtmCMUEIL4BYxgejwenp6fIZrP6PPIvRqMROp2OFirWjDNO0G63ZY/nE3Z+3Vwuh/F4rGgXf7Fnk0s0GsVsNtMCdVOk4hP2m61QXHxqtZrapBwOhyI9ZOmQW0HOy3w+h8ViQTQaxenpKfb29rBYLLTsMibChZpiiWEY8Hg86HQ6sFqtAvxWKhUtX4FAAO+++65iKW63W+0sFFIODw8xHo/RaDRwenp6q07e6XRiPB7DZrOJHXRzYXI6nRgOh4p/sQ6YsTOn0ylg8Gq1Egy6Vqvh4OBATI9Op6PmGVZEM/rGBZqMG4oZdF9Eo1F0u12xY5rNptg9XMr4ZJ+iis/nQ6lU0hJMlwC5Jvw5KBSw9p7vmcfjQbfbRSQSEQeKx8Olk/wkt9uN8XiMZDIJq9Uq9wsAxXPo8GLLEIXJaDSqhjHG5wAotsL3MJ1OKxrE84U/68nJiVhIdDnQAUERlNckcL24s5Ka0TMAcv6QlUJnRqVSQTQalXuk3+/jyZMnePDgAZxOp1wobrdbrCte12Rlkb9DUZVOLZvNpuv35n+jW47sG9aq8zgp4AWDQQl+ZHnxPKALjCIH3XB05/D+5fV65XC6uLiA1+sVR2U+nyMej8vtR+4RI3g3o14cXhuz2QzJZFIus93dXf2MdNtRTKjX66qBj0ajKJfLGA6HuHPnjkDrFLvYqkWI983YG2NE5+fnSCQSuu54Lt2MwNKhxHsbBSBWqDP6mEqlsNlsYLfbUSwWMRgMkEgkxCG7CXKmKL1cLlGr1ZBKpcTFunkfIaCc7z8db3R7OZ1O1Go1TCYT5HI5OUIXiwXcbjcCgYDilZPJRI5GOgbX6zUajQasVisikYjcVIS4Hx4eKnZqGIbYbjyXtrOd7Wzn8zz9P/M1NP57tx0LhgFc/aEN5n/gK5/KMawvi7j/V/uwX9iBkQUYWWC/sCP/119g1Wp/KsewnS/uuH/zGPYWYJ5BThpjCdhbgPtfHn+8L7ZeA/Fr1+0m5Idh/Wi+jsnnxTT0yTvK1k+eY/Od9/Wx6nS+94eGgdGf/CqW/7Mm7OYPF7G+f+5/+RIn/+Gjl/77htWG/p/5Gop/cgfVr9r00fwfPP7c8YO28/HmpYUcs9mMeDwOu92u5Z5PyD0eDwqFwq2nugR38kkoxRK/3w8AstYXCgXVdxNcGQqF0O/3sbe3h8FgoFabcDispX46nWopYDsTlz+6VMrlMlqtFh49eiTAZ7lcVg0xRYj1eq0n5a1WS7W8XBYpcHChHwwGiMViWurJL7nZIMPFLRKJSCjh4kxR6eDgQLwZLuyMDXDpicViGA6HCAaDSKVSODo6Ei/nnXfe0ZNnRgeWy6XcSXRMDQYDuS+4rB4dHWl5zWazODo6QiKRUNMQK5LJFyE7h9ENHjchrCcnJ+JM1Go1CUe5XA7ValUsC3JqKNC88847YuL4/X4tZYwm8Qn9ze/tdDpxcXGhKmyKLdVqVXE1soCcTqdEjIuLC0SjUcRiMbGLbsKTa7UaGo0G8vn8LbDyfD5Hs9kUH4gMInKFDMOQUOd0OvHw4UOcnJzc4iqR08N65/V6rciU3W7Hzs4O2u02qtWqBLBAICAhwePxCEBdq9UkGjHGyPOSrWPkyjAGSKeU2WyWY4yCDZvA3G43MpkMLi4uFNMJBoOKuzGiyKgKXSIUMDKZDC4vL3X+ktlDUbTb7cpdNxwOVUVvMpnUZLfZbOB2u9FsNhGPx1V/PhwOb7GPhsMhIpGIquyn06muXQq0BOn6/X6USiXE43FxbZrNJpxOp84Pgtknk4kiPqzH3t/fl4Mjn8+j1+tJOKXjzOPxCJxNQZXQ7EwmI9ELgNg4y+US7XZbziRGgvjztNttOBwOwZ/z+Tw2mw0ajQYcDocEF0K+eZ6ZTCbE43EUi0VEo1EsFgucnJyIt8P6+U6nI8YQxSg2ZRFcHgqFMB6Pb7lvzGazmr0ocGYyGVW5TyYTwZYTiQTOzs7gcrkQiUQkslK0poibSqUwm81UP85YI38OipIU9i8uLvSzs3GNbWkUglqtloQhNvo5HA40m00AkOjL+yKhy7PZDGazGYPBQJD67WxnO9v5vM80bIJh/kEoh2FfYxawfCrHsJnNsHp2jNxfeQN3fuV3cOdXfge5v/IGVs3Wp/L9t/PFnlWng9h//ltI/UdvIPVbUwSO1tj5xw3E/vPfui2A/LBZr2CU6tjYLDB1BzDaPWwW85/cgf+IY0mn4PtfFBB2jj/25zru9GB6fP+l/u7s9z5G78CExfeZeEZpA60/+zrMAf/LfVOTGcaXH8L4yiOYH977mEe8nZ/EvLSQc9OiPpvNFFki98DpdKJer2Nvbw/j8RjlchnhcFhREi7hBBjz6TRFD379O3fu4PT0FMD1U3M+LSePgh8UPlarlVgpXLLv3LmDWq2mX8TZeERrPY+fT9htNpsiLhQUer2eQLjJZBL1eh2DwUCsFC5d5+fn2N/fh2EY+tqE1jocDglM4XBYjBPDMMTd4JPj9XqtqmuPxwOz2Yz9/WsbH2McAFS1y6+RTqdRKBTE3mCtM+NBbBri4smGLC4w0+lUT8K73a7qkRmrGY/HgrDy6TSFrtlsdss5w6XLZrPp/a5Wq0in04rY0aVkGAaSyaSajqbT62Asgcn9fl8MI6fTKfgq401cFLlM3ox4sVXJ7XaraQuAFmQuqazaZuSLUTg2LbGBi24dRjk2mw2q1aoaqQaDAQaDAVKpFGw2G05OTsRLIZyYjBoAEqc8Ho/AyKVSCVarVQ1CjAV2Oh0JSKFQSG1OqVRKMTq6tChmMbrIcyCXy+H4+BjhcBihUEhgZYqpjMAMBgNcXFwgHo8jHA5L3BwOh3KY0dFGNwbFy263K5fHTVBsvV5Hq9XC3t6elniKip1OR5ypSCSi17hWqyGTyYjpUygU5KQBgEajofsPa7cJoXa5XDg5OUEwGNTXrNfr4rfQxeHxeNBoNMREoQDB8382m8mRQ3G23W6r9vqm6OhyudBqtST28RpmI1y/38dyudTryMgjY2l0s/HeQDA5j5nCJAWG6XQqN4/ZbMbFxQVisZicfYPBAP1+X24ocpvW67XuHYx3UsxYrVZ67b1er6KjtVoNy+USe3t74iZRnGS8bjqdKurZbDYlylBEIQeJ5ytbqHhtDgYDNQ7SPcYa8H6/LwHU5XLJ7eTz+cTcYjugzWZDoVAQc4nsIYq9fP0pTvG1zefzqNfrYo3ZbDbdO+nO2852trOd7XyM2Wxuf2xnOx9nNhuYf+MteP/ub2P17GM6cb5v1hE/Nn7vD3XkXH/fH+tbffwxm2AyfrRvuhPsoPRLoR/+LaJRdO98+M8+yhhAOvHDv87De+j/6Z/D1S/7cfXLPlz9oTAGf/prsCTiH+u4t/PJzksLORQ7QqEQqtWqRBFGqm62uJTLZWQyGTl0bjokbv6CzKgUXRTj8Vg8EpPJhEqlglgshtVqpVYeNscsFgvFqPx+vyJXFE5GoxG8Xi9SqRSq1Srq9ToACG787NkzLWx88k/GDp/K7uzsoN/v4+zsDOFwGNFoVG4MVtuOx2O5GvjEmtXRPp9PMbNer4dcLqeFipXVrIfOZDISUtgmVSwWb1UtV6tVtdDcjHiRx5HL5TAajdTOQoHD6XSqlvfq6krOi+VyKScJF1gKcmywMQwD8Xgcl5eXqpimaMXYGaMvFG1Yy97pdFCtVsXhILvl/v37qNfrclnE43GUy2W125A7QnZKtVqVU4buAzKGbj4xt9vtgvdyuWM9Nt0GZN5QlOLiyHYm/swul0vL6GKxQCwWg8lkwnA41PnMJiX+bBRtQqGQGsPq9Tqi0ShSqRSurq4QCoX0+qVSKdRqNV1fhP+SKcIln8c4m80wGAwE+DabzYrBOZ1OPH/+HIeHh3K1MbpIobNYLN6C0wKQS2pnZ0fMlqurK8Wj6vU67t+/j8FggKurKwGhCTJn4xqrtykkNhoNzOdzBAIBAdLJv2IkiLEwxtsWiwWSyaTcQ2T83BQ8B4MBHj58KC4POTQWiwWj0QjNZhMHBwfo9XqK9YzHYwG0Q6GQwM137969VcHNBiOeG1arVefykydPkEwmxYahuMwPukHYHEcBgvfJYrGIWCymhic289HVSEdQq9VSexjB6IVCAffu3ZOzaTQa4cGDBygWizg7O0MikZAwyAYrvt+EsfNeF41G0W63cXR0hFwuh8ViAcMwkMlkFOO82YwWCAR0n6G4WS6XYbPZxISiUMl7NEW+4XCIWq2GYDAIv9+vBi++F2azWYI0mVWLxQLdblf3JTro2BBXrVYFe6egyEgZ75+LxQI7OzsC2tO9xHsWGWFsQKOLiU16dP2NRiOJ2tvZzna283kfR2uNzWrbDLWd7QAAFktguYYxW2DjtMEwf/TKuyxXEHi/C+MDsHjGCjCNPvmY9dP/3Y8ngoR/uQTjKx8dsTLcTsyCH/lXfuiYo1Fc/pEwevsmMYTWNqB7x4Tin9nfNtJ9hvOxGDlsTspkMrKn8wkxq15DoRAcDgfW6zXq9ToePnwoKCsAxWDY4EP+B6usbzJIyL1Zr9figWw2G6zXa+TzeVn4uVgxWjQYDBCPxxGNRvHkyROkUilVpH+/yyaRSGjZbjQacoYwHuLxeBQluhmBoBhjt9vVBMPWK8KT2RpzeHiIfr+vhZKREwBiRJCLQ9ApQZs+nw/JZFJ8HUZZ+v2+FmAAesJN8WgymWCz2Qiga7FYBAPlUkToLl97xjOCwaBarsgmIrjWYrHI6TGfzwUjZf02F7pkMinX03K5FGuFbiqyhbxer6rLWbFMmOtN5k4ymVRUigsw68wNw1Brz3w+V9xiPB4LRMwoFZ0OZGDUajXFWygg8dylQELRaDgcyimy2WwkuAQCAUSjUdRqNWw2G7GUXC6XWCsUHMksoduCcbBAICDHEAHB0WhUcanJZIJ6vQ6PxwOXy6XPYyucxWLB3t4ems2mIOPz+Rz9fh/7+/tyJVmtVsG6i8Wimt/obrvpYOr3+8hkMqhWq3C73RIBCMimMOt2u8VX4TXgcrl0XDw3gGvBik4LNq3RMedyudDv9zEajXD37l2Uy2XVR9MNmEgkUKlU4PP5YDKZUCgU4Ha7EQqFxDDi59Ct4fV60el0cP/+fVSrVbky2u22vhbdQrzeyXjp9XoSruk4IfA6Fovh6OgI6XRaTVzn5+dIpVJwOp2C+hJsfdM91O12UalUJKCyqny9XqPVasn5FAgEEIlEUK/XJbxGo1E1e+3s7AiETtdeOBzGxcWF4Np0zlB4f/z4sRg7FJUYu6OAGQ6Hda0bhoGrqyvxtuiiZGvaer0WUHo+n+P58+dIpVKwWq2CmTOqR24PXWQ2m03HvVwusbOzg263q4r6yWQidyeFo3K5jN3dXf1b0Gw2EYvF4HK5UKvV4Pf70el0xLsiE4puQt4vptMp9vb2FAWl8Ol0OnWvZKvgdrazne183sf3d34b0W9Yf+C/G10r/M+6n/4BbWc7n+Fs5nMYiyVQesma7c0G6yfP4X/eu/WfXdUFAk9aWD09+sSP0ex8eS7OB43bOsfG8tGr/Kbbg6v841mNxl/JY/Mh6cylEzBef/Bjff3t/Ojz0kJOtVrVE1Mu5hQmRqORmn+4ULMyGACKxSKm0ylisZiaVnw+H/L5PAaDgSIFq9UK0WhU7Uh0m9BaT0Aqa2bPzs60VDHmwmatSCQiKz0bbPikv9/vS1Bi/flkMsFsNsP+/j4ajQbS6TSePn2K8XiMRCKB0WiEbreLR48eyRlxeXkJr9crQDKdOWzZYVSAwFnCeBlnucm9IJfj5hL58OFD9Pt9tFotxYDogCBcl8wTRor4HjHe5Pf7Bd0dDocSQxi1mc/nsNlsCAQCWK1WaDQaCAQCihqFw2E5fugQImODf8/tdoup0+12BXD2er3wer0ol8t6Ep9Op+W0WK1WErFsNhsqlYpeLwByDfF9DAaDODs7UxSFbqrhcCj3DuHJ5JGEQiGUSiXY7XZVH9PpwGWR4gWXU5vNhmg0iuFwqGW4UqkoEshrYDgc4t69exiPx2qworDEpfNmc5HValVLlM1mk5hBBonb7UYkEkGxWEQ+n1esxWazYTabqVGJbpJutysxjVE0CnnBYBDr9VouBMMwdI1RsOF5wTgYXU9er1fcIkKPV6uVHFt87+g2o/PmZgNWNBrV+c9GqPl8LgcLxUSXyyUXXzAY1M/SbDaxv78vdgnBvDzPKCDH43ExnXiu84PRHovFAr/fj0KhoPOZsTk2ypHfwvYoxjRvurIoaDIKRAdaqVSSWOXxeABALjdyhQAoEhQMBuH1eiXmFYtFCQh0LFJUJiC71WoJps7Xk2IRI258XXhvoEOPIspsNkMsFkO9XpcYT6bVTZZQJpNRvJExKf78jE6NRiNF31hnTu4PnT/r9Rqj0Ujn4k3RmUIoxXCC6iuVCjKZjN5fs9mMcDiM8/NzRQopSjMG63Q68e6778Lv98PtdmM4HOqcZ2SrXC4jkfiedZhNeqlUSu1ZnU5H4ulms1E0lk697WxnO9v5vE/o77yFyG/YgIEFGFiQ/icW3P/VItZPnn/Wh7ad7XyqY7hd2Div90es1zBy6Zf6vPWT5wj+N0f6sP7L937seNenOYbdDrPPB7PPB8Niwarbg/98/qE17MYSwPKj2zk7935QIOZszEDv7ks2aG3nE5+X/g2VS108HlezRzAYRDAYRKFQEJiTdvl0Oi3XSiwWU9SjWCwiEAjA4/Hg+fPnio4AUJwqmUxitVqp9tYwDAGG+US32Wwim80KtkkwqdPpRDAYxNOnT7UwZDIZdDodzOdzhEIhFItFtcdkMhk9dSffgjXLPp8PmUwGJycniMfj8Hq9ODo60rKUzWYxHA7R6/X0pHy9XqNareJLX/oSisWiYmJcVuhKYUyDsS2/3y9GBSMJhBHTHeT1evHkyRPs7u7CZrMp2sIFqdfrKV7ECvNSqYThcKimJDoZGDc4ODhQbCYWi2nZJ+unUqmI5cLKYAJr+QSfUQuKKhRWZrOZlno2XlGUunv3rtg9N5dY4DruM5vN1DbDOF+pVMLOzg5isRiq1aqiKIRlM+ZFoYKuGJPJpJ8fgMQnxr7I0WAMi0/gY7GYnFSEFFOsYrSNUT+z2YxoNKrjajabam2jEEmwsc/nw+npKRKJhGDfjFvR9XOz/WcwGCiGxyhOpVIBAAmPfD/I86E7DgAKhQLG47EEGragRaPRWw4yts9R5AAgUczj8WB3d1c8luPjY9jtdkSjUbk76MxgAxWva9aN830IhUKYTCYYjUZotVoIhUJym6TTaTmTKMAQSs5zlvcj/h2KzBSLWWtP/hWdfRTgCIJ2uVxIJBJYrVYoFAri4rBRidczWV88H9neFI/Hde37/X41SC2XS1QqFcWLMpmMxKJqtYpKpQKbzYblcqnjYMRtb28Pfr8fJycnAqMvl0ukUimcn59LrKYDbLVaSTQqFotiSPFapZDV6/XUPHfzHjefz/Ho0SMBpMkGI5ycQttms0G5XEY2m0WtVkOn08G9e/cUL8xkMqhUKggGg/D5fGg0GrpnkR9ERw9ByRQoh8Mh/H6/ft5ms6mmQ7aspdNpiTmEZBPWbRgGEokETk5OcO/ePX1ur9dDIBBAuVzWvyE8p61Wq+6dPCd4Xs1mMzx69AhvvPGG/s3Zzna2s50vwmxmMwR//Q3c+fPfxJ0//024/tE3sSwUP+vD2s52PrMxLBaYGl0Ys5eEHW82WLXa+th8gVy5Jrcb3T/1Gi7+wiNc/IVHGP2RL8OwWGD5b99E7O3FD/x9Ywlk/8UAqxcnn8HRbueTmI8FO+bTaS4PhmGg1+shlUrJUcAnq3S90LLe7/cVa2H0gu4Au90u/gIjWvP5HHt7exiNRrL3sz3IYrEgkUjoiS2/L5+2c5mdTqdwOp04Pz+Hw+EQPNnr9cqRQPcQ62v5i7zZbJZQQD7JcDjUE2o2t6xWK+zv72M0GuHs7EyME/I7uEAmEgl4vV5FXhixCQQCesLvcrnE4IhGozg/P1fkgY4HOpqA6whFoVDA4eGh2Cy9Xk+ATj5VpvjAWvRSqYRIJAKr1Yper6fli61Oo9FIQhCZKayKZp02cF3HTngsHQ98wk04LGNajFINh0N4vV6JIYwJAdATdQpekUhE79FyuVSbFQW7m4BjsokikYjiIna7HZeXl8jn8xL+GEdrNBqwWCyIx+Not9uC3T58+FAuB35dtjTxPM7lcoK0MkJHkTKRSNwS1Fibnkgk0G630e/3sV6vVQNNHgrFhm63q7/74sULRKNRxczIDaJ7y+fzodfrIRqNKgZG0Y4gWMZjQqGQ2rIYZaGDg41OFE0YcaMAEwgEUK1W0Ww25WqguMhYE5k7jCtSQCH8ms4nLs8UfBgXBKD/tl6vxS6hE4cODgqRvP7o9uB5TnYNRQ46sVKplBg4w+EQ6/VaETSn04nDw0Pd487PzyViAN+Lfa5WK4Gob4qyBCIzNkgnSzAYRLfbRbfbVQyP8b1+v6/31efzYTgcKmZGJg5FIb/fL1GYbU4vXryA2+2G3++XC+/VV19FqVQSb+xmhA+AGtbI4yKQmuwnCrl8DwhS7vf7ODg4kFhEwYrxrnA4jEajgWazCbfbDbfbLZfPdDpFKpUSW4lMJ7rdptMpXC4XkskkCoWCzlu3241wOCznD8VEt9utaCfr38kQY1MYXaE2m01OSILRR6ORopuM4pEdxvY/imq8//P83M52trOd7WxnO1+MWfeHMEZTIBoCTCasa43P+pA+8VlvDGD9vdhU54++gv7u91b71kMzJv/66wAAx3/zHUTeXV47c9ZA8PkKO/+kic233v2h3yfx28OP/PNR0oT+n/0aTG73lpfzKc9LCzlsSiFfgtwOMmUODg5wdXUl7ggrbSnINJtNLVy1Wg0+n0+OlufPn2NnZ0d1tIytAJBTwmazodvtqvmK7TjkyrBJhbZ4NmrRlWMYBt577z3VlZPjw4hOLpdDs9lEu91WI8p8Psd0Or0F2OTT5GazqSasQqGAaDQqNoXP54PFYtGizUYaRqdYKe5wOFAoFABAUYDxeIx0Oq3YGn/2Xq+H8/Nz7O3taXFmNIiOiHa7DQBygcRiMfR6PblU6CLhcjgejzEYDG6xeK6urgBAy+Du7q6qhYPBIBqNBhaLBer1OgKBAHZ3dzEej+WamM/nSKfTqjWuVCoYjUaIx+NwuVxwOBw4OTmRMMe4BxeycrksJspisUA2m8XFxQVyuRwcDoc4SdFoFEdHR7BarXA4HAL1np2dYXd3V86g6XSK4XCIx48fo9FooFqtqlqeMRPWyxPYDACdTkfCRyQSkdAyGo1gt9v1v7mI2mw2XFxcIBAIYH9/X7EnQmwpQBGGSxESgFgvfN9Y0f7KK69IOO10OnC73bi6ukI8Hpd7gU4Kgm4pXjkcDjUPscqcjWtk7QSDQblxeDzz+RyXl5eKXHm9XpydnSGVSkno4WLrcDgUR6PLy+fzaWkn8Lfb7eq6sVqtgkvHYjF0Oh2JGby+WGvPlik2pjWbTYkiFAHPz8/xyiuvyLlCZovNZkM6nUaxWFRUyul0Sihk2106ncZgMECtVpNTkC4kvidWqxVer1dtUnxdF4sFIpEIDMNAo9FAJBJBs9kUM4qC4mw2Ewia9ytC0wni5tchpJ33n8vLS4lOhKu3220kk0l0Oh0EAgEkk0kA121oiUQC7777rq7/Z8+eAQDi8biuM0ZN3W63zgfeTxOJhGKql5eX8Pl8itE2m02JI/v7+ygUCnK+rVYrccyA66Y9k8mEUCiEi4sL2O12vYaMry0WC8VdW62WYMjpdFr3G4rmNpvt1v0pHo+jUChIQKMoSWEOuBYbeV5ToPb5fPq/w+EQg8FA7iCfz4ejoyPk83lUq1VxvugU3M52trOd7WxnO1+M2SyuGTkblx3riB+G+/NXXGC6cv5Yn1/453ng20/1v0fJH1zrh6nrXWOzXML5T9/Ezq++hZ1ffQuef/Dtl+b+mF8UYB18+J8v3UBv34TCn38V4z/28zC+iwHZzk9+XlrIYXyl1+vBarWK90DQKduVOp0Ostmslim2L7ndbj05vn//PgqFArrdLgzDQDAYVC0tn77TnUH3BRc/tpUwQkD3h8/nQ6FQUCsVK4tHo5EWGDY5sV6cNb1er1cunlAoJCcA3TqMAITD4VtNS/V6Xb/k86n8dDrVgk0nB6GthJ52u10tpox2kNdDt1I8HsfZ2Rmi0aiqhMlvILen2WyKE8JGrdFoJLAuK7+z2awqkmu1mkQzApa5mBaLRezt7WG1Wik2QhcKo0tsLyPjhfXT/X5fjUyDwQCdTgfpdBoej0eCBquP4/FrSjtdW6vVCuFwWMwgPq03mUw4Pj4W3JURD4J679+/ryYlcnXY7rW/vw+HwyFALV/zfD6P0WiEyWSC8/NzRSei0agYOuTSEJxNgYEiH18vOtTC4TD6/b74ThTWgsGg+C79fh/hcFgxqna7jWKxKOGDldZOp1M8nna7LdZPJpNBqVSCxWJBOByWADAajZBMJuUko3NkMBioAp5tPMC1+BKPxxV7CwQCgoTb7XYEg0GJnIPBQMfodDrlQKKQslwu0e/3EQwGEY/H8d5778mlwygh41CMCQ6HQxQKBbnveB7dFGqr1SoikQhWqxWCwaCcb4xuWSwWXFxcyCHWarUwGAzk8CAHiU1thCgTjksBly1T5EFdXV2pupr3ArYdEaBOJwdjgQDEdeJ1MRwO9XVjsRgCgQAuLy/hdDolUu/t7QkKznY3p9OJR48e4Rvf+AYODg50TfX7fezu7uLZs2e6z4zHY8xmM7F66LwhOJwxw3Q6LYFuPp/LKUhYeSAQkCOH0bfFYnFLiAYggDidcsfHxzAMQyIZ3TKM0PG9BqDoExlcjJOR7+RyuQQXpguHTjtyp242T02nU8XwFouFXmvGDvlvER1q8Xgc1WpVTYGZTAbT6VTuMw4fAFSrVYRCIQwGA8Hat7Od7WxnO9vZzhdvjMUKxniGTTL2WR/KD8yd/6zwI39uqedH8jdHwPqj+Ta3Zr3CejrFejr9WJ+36nSQ+UcFJN6YfWRF+9p67QIyJ7eV5J/WfCyKI4URwnGfP38uoCn/nHBVwjW5yHq9Xszncy1rFBLsdrus/lyQqtUq1us1Li8vsb+/j3Q6LZGDTBzGEAh/bbfbeO211zCdTrFareD1ejEYDNRi4/V6xeUg26PZbN5qcWJkg0/1/X4/LBYLvF6vhB8AinsEAgE1TJGtQZFouVzqyTZjIVyQCHGeTqeq9p3NZtjb20Or1cJqtcJkMsH+/r7qh8mocTgcCAaDaDab2NnZwWazwWq1wtXVFdLptI7rxYsXmM/nuHPnDkqlkpwrfF8mk4kagOg8YQWvzWZTvIIuhMlkIncUuTJc1MiwuAlyJjyaLTg3wcSM53B5JAeGTiXGzjabjeJSnU5HnJRSqaS2plgsBofDgbOzM/j9fnGOKGb4/X69px6PR+dEPB7Hzs4Onj59imQyKSaJYRhIpVJibzBexmp6k8mEk5MTuXIYwwiFQuIZsX2I5yHBtYyUTadTJJNJeDwevcYELtORcnMxnk6nyOVy6Ha7cp1RWKSTiws3XRB097D2myKs2WxWIxzfU7ZNDQYDiVnAtQuPsb3j42NVplOAA67jWYwyLRYL+P1+xSh3dnbEcuH1R6fDcrlEKBRCs9kUNJewcJ7rbBhqNptIJpM6ZkaC2IZHUDFFN9ajs/r6ZuMbxWXWVTOaVigUEI/HEQgEBF3mfYHvE3/+Fy9eIBaLSYjgdTmdThEOh7FYLPR6M761s7Ojc8AwDFxcXKDRaCAYDN4SYYvFIv74H//jePfddyW28FoIhUIYDodycpG/xdcwnU7DarWiUCiokp28JwrMrPR2OBxina3Xa+RyOdTrdb2ufr8fJpNJYg1b3haLhdxFdCBStHa5XBKz6Yrh60fnG1u2KACSFxWPx7FcLlEul/XvA99LCjLNZlN8J96rg8GghPqTkxP4/X7Y7XYBoC0WC+r1OkwmE2KxmGKQbC/b39+/5ZSjMM9mRUYEt7Od7WxnOx8+htWGyb/586j/yi9g+fu+DJi2983tfE5msYQxHH/WR/GBs6pUUf2beYwWto/9ud2SD8ZvvXPrv1kmP/j3pmED9V/5BX0YX34Ik/fjw4mXlwVY/8Wb2P21U+z+rTLMH4EO2rh/PKfRdl5+XlrIYeMO2Q69Xg+ZTEbsAsMwVL/MiAcjDDejJIw2cBE1m80C6nIRI/+CT9+Hw+EtsYGRIEZGGJtYrVZyRvD7AxBTwev1wu12q8WEEGQKTmynWS6X8Pl8ePLkCe7cuSMYLp0ldOhEIhFFqSgusRmKNd7lchk7OzsAgEqlgsFgoKhZKBRSbIPNXoeHh3p91us1ut2uYgzkYTCyQYGGEZijoyM9wU6lUqqyZpsVOSUA5MaJRCK4vLxEIpGA1WpVNGK9XsPlcqFSqQiaW61W4fF4kEgkMJlM4Pf79XScUTCCfflEnJGIUqmERqOB/f19CQF0BjidTolAZrNZ9dZsGqMr5eTkRKBSiknANauHHKXpdIp0Oo3JZKIoCN1ErG1nhbndbofH45Hg1u12Vducy+UAQLyn+XwuccRms4kHBEBukcViIdGQIlalUtFyyepuco9SqRSKxSJ6vR4ikYjOG8JquTAbhqEaeafTCcMwbkVfyJqig4ztZrzmKGrRbQR8b8EmWJZOt4ODA1xeXqJSqWBnZ0dRM3KwuED7fD6Jl3T33LlzB16vV61BdrsdV1dXihYSVOt0OtFoNNBut5HP55FMJnF8fKxIEWvZzWaznDEUbMrlMgAgnU5LKGOkk0ICxa9QKIRut4tarSZGUKPRQDKZ1DXd6XTQ6XTg9/t13j979gyZTEauIYodbC0jtDibzep9azQaEpXpNCLbZnd3V1EuOl4Ijr+6upKDyO12S+Sx2+2K0M3ncznCCFWm6Nbv9/HlL39ZkTOyslqtltxwvPb7/b6cixS2CCW/6YLJZrNoNK6z5MvlUhycZ8+eIR6Pw+l0otVqyYlFEadWq6la3GKxqHmQLie68lg7z/gdGU9k0pBVQ8cmHwwwHkVnHv8NaDab8Pv9CAaDaLfbtxr1ZrMZTCaTzi2+Zm63W+41n8+HUCiEer2u65QOofV6rfvfdrazne1s5wPGZMboD7+G5mMLZkGg8gt2LH7ptc/6qLaznc/9bJZLhH/tDYz/yxQWq5cXP99/nsXhf/DiB/578m8/Q/i9FcxTwNYDrH1gYwFmwe99XP0bflT+J6/Aknm5Fi8AML7yCJtfePX6I+hD77U4Vh+Rnir8G+EtK+dTmpcWcsgUoOhAQYTgYDp16KphrIG1w+FwGOVyGYFAQE9YAahFp1arqSWIYgCdGMPhUO0pvV5P4F06YRKJBAKBAOr1OlarFZbLpRYbNizZ7XYtsVw06BKIRCJot9uYTCbodDqKYty/fx/lcllsFi523W4XXq9XooBhGHLO7O3tyZFCjgqf6tK6zxiI2WxGp9MRO8fn80mMaLVayOfz6Pf7isAAUJSMbIdAIKCIVTqdVgyK4s1gMEAymcTp6amcVIx5kFlDUPFNhwQFMtYpUzTzeDw4OTmRYMcGLkbY+PlWq1Ug2pOTE1itVjknnE6nmCSbzQbdblexGT795mtKkYKMELajscp9uVyi0WgorkUmyXw+VySLldgECZvNZrGLvF7vrWOJx+MSgLrdrmIiPC4uf/zf/HyPx4PxeCxR8WaVOWNhjNrQGcZIocfj0flB3hPjX4ZhKPrXaDS0xM7nc4mH5Kq4XC45Dq6urhAIBPTnbHtzOp16TwkYJ+eHHJR0Oi22CeuwCfldr9cSW5bLJUajEQKBALLZLAzDUCzs+6vP6fJhZIwOIoqzbKrr9XrI5XISbIfDob7HarWCw+EQHJzcKQKO+b7a7XbU63UxgWKxGEqlktxvZKR0u13EYjGYTCaBiEejEXK5HHq9HobDISKRiOq1KSLwWvD7/TAMQ5Edm+36iYrb7Uaj0cBgMJBQ6nA4dD5SlKUjpdlsSnBhFftkMtH3YIscRb1WqwWHwyHnFQVrOmwAyBGzWl1bZ9ngRHYUm/Lef/99ZLNZwbOn0+ktVxnPj5suM8Y1CXdnvIriGACBu+PxuAQmgrfprOQxMPa1Wq0Uqb15zfL+OBqNBLmnCMf3ktHRRCKhKBz/Hv/dYnyR7iSC1G02m2DSdrtdzKtGo6Frdzvb2c52tvPBY3LY0XpwewltfMm2XeS287mYjc2KTeDzXY8d+C9/G82/m8Vk+eE13wAwW1nw/osMHvwfylh1ez/w56tOB65/9E1k/1EF0Xcm2Fg++OvM/cD0XuKljm3zu76Ewr/mQ/H3uVH8fW4U/mAU9u7yIx05iTfGwOYjMljb+cTmpYUcNolMJhPs7e2ppYZLEOt62+22nAAEAjM28corr6DdbqNSqSCTyYitEw6HVR/L5c/pdKJeryOVSondQNGDNv+bT7gZTeFiwOjH/v6+2CLA9SJ5dXWlJ72JREI1yNPpFLu7u4KR0rVANgqf8Pp8PnQ6HbkOGCkxmUyqW3c4HOIJcQnz+/2q7iXMlZ8LAI1GA7PZDOl0Gvl8Hu+++64WG4oTFDZCoZCWc5fLJUGNcN+bTInZbCbBxzAM7O7uolqt4vz8HJlMRpXjjF/dbN4BIBFss9mgVCrB7/fD4/FIRLkJ+z04OFALzE1w7e7uLur1uhY0uriGwyGSyaREOr7HfB9vRjkSiQT6/b7cVnStHBwc6GduNBpqr2k0GgiFQrBYLAgEAmon47kzn8/1PrKynU4fMpv4epDdVCwW8frrr+u1bTQagqQahoH79+9L6KPriWwlxjz4tU5PT/H48WOJoovFQoIaBRcyogDIsUFX1Xg8Rr/fRzKZVHU927x4XdyMvbF1ia/her2WIEReS7/fh8PhgMfjkftos9nofZ5MJmo4S6fTSKfTePHiBRqNhuKMs9lM0aZAIIC7d+/CYrHg+fPnagN69dVX0ev15IKYTCZysTFqdbPSnM1idGudnp7C4/HI7VQoFMR2IgSdfB2fzwcAih++9957EqXoonr99dcV9ePiT8cIo3A3441WqxVnZ2dotVr4uZ/7ObmG+DXG47FYRIwqkhMWCARwdXWFYrEomHkoFMLl5SVWqxVyuRySySTq9TrK5bLuURTD6EBZrVYol8sYDoeqGd/b20OtVkMmk9H7yzjnbDbD7u4u1us1ms2m/t58PpeA7Ha7USgU1PZE51yv10MikUCpVFJEjO2CLpcLzWYTu7u7mM1m4kHx67K1ju1S5XJZLDLeT25C7Cmg0dFDV9BwOMR8PofJZNJDg1arJYjyZrPBZDJBNptVC5XZbFabH6vfc7kcQqEQEokE3n//fTmOKpWKuFo3r1/+W7ed7WxnO9vZzna+OLOuN2HM5jD6o8/6UD56NhtEfu13YPs3Wzj+Rh5P38uhPvLoj98/yuDpezmY//0A7v0vn2BZKH7kl1udnMM8WmD5EXznytfssCQ+mmVjPryD4r/iuiUIrZxA9at2WAcfLNRYB4D1uPyRX3c7n9y8tJDj9/tRrVZvLdEmk0nLMqHFq9VKgFQ6c+LxOOx2Oy4uLrQAO51OhMNhTCYTtFotNbEQWsmnx+RtLBYLWK1W7O3tCfgbDAZlga9Wq7BarcjlcuIpkAFDoYGxJJPJpBpkVnoTgMkWFofDgXK5jNlsJlYO4zX8Gv1+X24e4JoX4nQ69cTZYrFoqW61WqrIDQQC6PV6gp+en5+rKYtP98mpYHyEyxOjG4R6ut1uPXWn84FLMeMlZEUQxMrFxe/349133xVT5GYVusViQaVSEc+ELB6LxaKWoul0iv39fVSrVQlly+US9Xodjx49Eiw5EokIvMyvywjHzbiP3+/HZHId8OTP5/V6kcvl8PTp01tNY9VqVVXHN9ubGC2bTqfI5/OqMx4Oh+K13HTDxGIxBINBvPHGG7h3757iHPP5HMlkUiIYWUKhUEgxILZceb1eRCIRRKNRdDoducA4Z2dneo2Hw6GOixGrcrmsKnI2g1GIsVqtmE6niqvddLDQucO6eQASOjqdjmDHtVpN59LNJqtgMIhKpSKIOEWWarUqxhHfV4pBdArxfzebTTFh3G63XhNCx8lYOT09xe7uLnw+HwaDgaI/FBmA6+ghWSZsH+J1SdcHuUWscCd/iA4wOjEojLlcLkXICGCnYwi4ZscEAgG91nRXUaicz+dot9sIhUISfegwIcCX7C/gOoLGWBKB0HQN9ft9tNttxajYNhYOh1Gv1yW+8r2mkNnr9QQe5s/NCnoCmc/PzyWA8rXnuTybzcQv63a7EpcplAwGA6RSKb33jLNRaB4MBhiNRoKwU0Dc3d0V4Nlms+nvEOicTCYl8jOiyHOXTCa2hl1dXSGRSKhenDFBv9+PTCaDarWK0Wike08ikZBLjM69VCqF4XCIo6MjPHjwAKvVSvdhAHJvUshKJpNycvF6oVtyuVyqkW+zfaq0ne1sZzvb2c4XbkyxyLUz7Lu/832uZ73CejRC/j98A3d+5Ztw/WcB1H49j9qv53H4v3qBO7/yTWy+9S42s4+wwnycMeGjXXMmMzqvhT9QKdiYgeR/dQXv5e3fjywjIPNPSljV6p/MMW7nh85LCznj8RherxfRaBTFYlGOCQowoVBIkSvCJ+mYqVaraLVaODw8BABxNUajkarBGS2hsOL3+1XxC0DLrMPhwGQygc1mU/0uF0Gn06kabLfbrcYpOlUoAhCozJpsLk2MfJHbQqYC3UY3n25HIhEAkPBxM8pEsYmiFQC9FjwmLm2EQYfDYb3WNwUvVvWmUilcXFzg7t27Wo7X67Waus7Pz2E2m+VwomhCaCsjVHzC7HA49HORuXF1daUn8Yy8UUyjcMNWIy7KXGQZLWLVd7lcxvHxseIKXLj4hJ8V1OSpEGBKgYxiH3AtUKXTaVxcXMDj8SAQCMhhw9iM1+tVsw5bbgaDAUqlEsxmMwaDAXZ3d+WwIn+HLpZ8Po+TkxOkUinFROhE4tN9Cm0EG1erVcXt7HY7arWaomOMDjHydTOitlwudQ4XCgUcHh4K9Eq4LeujLRaLoM+MGVHI4PXCCCEhvlzoKdLYbDaJJIxGtdttMWjoxBoOh3KSkCnidDp1XdfrdQmTk8lEriXG/viz7ezsqIqd1z/dOnSfTSYTHScB5bFYDB6PB8+fP9c5TccHz2uKpJvNRhE+ul4oarBxDrh2kx0dHam+noycer2uOFej0cB4PFaj2nw+x8nJCRKJBDqdjqrBee+ie43H1ul05Eak2MbzkowvOu+s1mvb7HA4RDabVTU7X+9KpQKz2YxSqYT1eo18Pg+fzyeBhyI6xVubzSZBdblc4vLyUhFOk8kkQDtjdpPJRK4lXo9kEi2XS0SjUUU6GX9dLBZIpVIolUpiBFH04HtvsVj0NdhER74MzzXek4LBoN7D9XoNu92OBw8e4Pz8XILmu+++i3T6dn7b5/NJcDs5ORG7hlE2Rsbo0PL5fIpjUgBbr9c630qlEoLBoO5pdEQyWsuHERTptrOd7WxnOz8468kE2X8xvH4SPwDi31og+39/sY1WbOfzMYaBda8PY774rI/kY439v/4WQn/jDYT+xhtY/QScweYZsFl+eHOVORRAP//hMsFmOETo77+D/H9xoo/M33iG5fnlJ36s2/nw+VitVXSPZDIZxSEYX7gJOAagRZWCTiqVErSWT6mtVis8Ho9qdAkqJV8iFovdanYiB4dPiz0ej5ZctkPRDr9YLMQJSaVSegJPuDBdMfV6XRwbLnN8Wp7NZiW4nJ2d3QImc7lnLTgjGcvlEhcXF0gkEnqySwcIHTPlclkOHLb3vP/++1gsFuLe8Ik3F95yuaz2L9YzM35zfn4uB4jJZBLMlyIQhY1KpYJ2u41AIKA4EZ0+5OrkcjmJM4yrsB3GZrOh1WrJ6RAIBG7VgNPJdHx8jHv37mFnZwej0QjHx8dyy3Q6HUXCFosFotEo9vb2cHFxIYeR1+tVQ9VkMhFQmu4UMnwWiwVmsxk8Ho/cUc1mU5G31WqlpbLf76Ner4uFw6gTmTV8Ly8vL3FwcIBOp4NGowGLxYKDgwNEIhGcnZ2peevs7EyNT2TekDnDc41OsUQigcVioeuEP7vVakUoFMKzZ8/w8OFDrNdrxONxDAYDVCoVLJfLW84Qr9crwYbOJfJrlsulxAWKmJFIRC1UTqcTs9kM7XYbwWBQbUqZTAaXl5cSHxjFq9VqMAwDJpNJx0R+CMVLig+Mf3W7XUSjUXi9XrTbbQkYw+EQ9+7dg9VqFQ+GcbNUKoWjoyMJqhQdCfCeTCao1WpIpVK4urrCYrFAIpGQmMBoTavVQqvVkpOKwqnT6dR9IxgMSshlDTaFVNa18/7gdDrRbrcRDoclPFOwY+yLgl+9XhcMme1qjBVNJpNbLVZ0B1Ik9Pv9cqbMZjOYzWYJZ3SYFAoF1cifnJzcajUjC4xMp1QqhWAwiIuLCwAQt4rOSavViqurK8Hjc7kcwuGwxE2Hw4FWq4VOp6O/w3ssXTB0JZHRRBbNer2WKM54Kl2JV1dXcjYSPu90OiUgMjpKbg0dRIRZHxwc3BKWeS9xuVwoFovwer2qag+FQmo1pKBOlxxfX5fLBZ/PJ2D8eDxGLpfT+e/xeFCpVPTwYDvb2c52tvMhs9kAv/0Eif/kt5D4T34Ltv/Xt7Bqtj7ro9rOdrAZDLFxWIG7eWxsH82f+Wkbc6MH2w9idDTxb02x+q6D/IOm+YfuYvND+Mvr8RirWv17H53Oj3i02/lR56WFHLPZjGg0in6/L9Dpzs4O4vG4WnWKxSISiQSi0aigxNPpFJlMBjabDc+fPxcbAbh2+VD4oegQi8UEPY3FYuh0OopsdDodOXj4pLvb7erJLKHAXOTp0AGgOvDBYIBoNCrWyGKxkGuHT+ObzabiDVarFcPhUC0piURCLB4ugHRq0G1gGIYAvjs7OzAMA4ZhoN1uS4ygUyEUCokD4nK5FAthM85yuRQjxu12K+JEsC7rzlnPzKfTjLodHx9rgXW5XEilUnqqX6/X5ZQwm83IZDI4OzsTE4JA00wmo2WIi1ihUAAANWERQEzh6fLyUnXidEsQPExILOMm5+fnCIfDCAaD2NnZwfn5uZpq1uu1lkRCe1utlvgd4/FYbimyN+hCYtTJZrMhGo0KdEu3Cxk73W73Vi09IxsABLA9OzuDx+NRNC2bzQocHQwGry8mk0kuJa/Xi0KhoFgHhRgKkKwMvymqsJ4egBZoRucSiQTK5bIauwjHHY1GatchS2kymSg202w2Ua/X1ZxGQYLXHDlFFDFZ685WLQoc5NYQZh2JRLT0spaajBKKR3Rl8FqpVqsIh8NqwMpkMnj69ClyuZzeyzfffFMC5u7uLq6urhTRZGNTqVRCNBrFZrOR4BKLxdDr9eSy2mw2CAQCGI1GEj54jT558gT379/HYDBAMBhEq9USz4YCCxvCeI9ar9cSZefzuY6p3W4jk8lIUFssFohEIjoHb0LWAUiUosj27NkzpNNpXF5eIplMotPpKM5IMe/w8BAvXrwQoJ3i03Q6hd1uVzyRMSlyYvj96Xbk+0xHTDgcxpMnT3Q+N5tNrFYrRZdGoxGazSYCgYAYVul0Gu12W21njHDyPmg2m5HP5yVw8n6QSCRQrVbltmPNfL/fF+NmPB7j7t27qNVquj8SVs22RKfTCa/Xi4uLC3HO/H6/YqdsweL9grGx+XyOarWq6B7/bXI6nTg9PRUsPBgM6kHDaDSSaL2d7WxnO9vZzna+WLNqd2BM5jB1rx3GP0sA7uVlAel/eAnzFMCNZJmxAhyNDWzvfrRzxvhws852Pkfz0kIO26dYZTsej1Gv1xVl8Xq92N3dRaFQEACWtdzNZhPtdhs7Oztot9vweDxaDkOhEO7evYvj42O1sdA9Qmgtm4oI2eVT18ViIXcM40kEdhIK7Pf7US6X9fSdCzF5L7FYTJ/LuAhrqYvFohwYrFfu9XoYDAZqJiIIlBwbLtOlUgnxeFyV12azGTs7OygWiwgEAvD5fPD7/Tg5OVHjE5/ms96ZvBXGGaxWq6CydB0R5smYG2G/qVRKizOdS6PRCMViEYZhIJFIqC1pMBiotpfQUYJdI5GIYhiz2UwAZLqGXC4Xnj59imw2i81mg1qthmAwCJfLJXFkNBqhXC4jFoupAcpkMqkVyePxwOVyoVqtotFoIJVKyZ1zM1JDR4TFYsHp6emtKme73S4nBBfY+XyOQCAgoSQWi+H8/ByxWAyGYaiSOxgMqgKdrT50E7ABjc4Fxu1MJpMAti9evEAoFNJrzmYfLpAUZZrNJg4ODvDixQs5Ahjp63a7qrJmrCwUCika1mq1cO/ePfT7fTGiCEWmwANci2+9Xu/Wa7K3t4e33377lgDKevWzszOJgGwAIhz42bNnCAaDiEQiKJVK2NvbE1+HUPLZbIZsNovBYCCRlc1dfH39fj9arRYePnwoyDYdD+QLDQYDhMNhuYlMJhPee+893LlzBzabTUJsPp9HtVqV44VOmJuAcp4/FLXIeGJj1t27dwUkvnnfoPhGplWlUtG12mg0EAwG1dJGkDBw7Yo6PT2VqM1rgKIrBYtutwun0ymRky1yvJ+wYp1un36/L0g5m5MeP34slwmrtNfrtY6TsSG2/zEuNZ/Pb7F7GGelyNZoNBRHpNNpZ2cHXq8Xs9kMrVZLtfYUrcj8SSQSCIfDKJVKmEwm4jhRhKegRq4TXTz1eh1OpxM2m03tcKy75/k/Go2wXq91Dx2NRuKoRaNRga352hmGgWKxCJvNBp/Ph2g0KoYXnXJs3yInihwhRkc7nY5cfz6fDycnJz/2P7Tb2c52trOd7Wzn0x1LLgOYv7vqmg2Yv4vF+FmZZbGE9P/pd5D9/wzhudrAc7XBzt+rIvpf/M4Pdc2F/t53PlLMWR9kYX5wF6YbD7vMd/ev/9vWyfypzUsLOVxOb7IZms0mLi8v8ejRI8xmMzEPGKEiGJhxDy6uXBZ2d3dhNpvxrW99C/l8HjabDUdHRwgGg3qyHggEFG9gfGA8Hos1k0ql5G64eXwE5XI5ZetKrVYTyDiRSChWVa/XtVwTnBuPx1Gv1zGfzwXCZTTIbrdr4WbsZDKZqGY6m82KM0HA62AwgMvlEhB5s9kgGo2i1WrB7/dr2fR4PHj//fflaMnlcjg/P9frnkgkFKMh2Hc+n2NnZ0cV1s1mU84e8lFarZaqnQeDgSC4AFQPPhwOJSywwWU4HEoEoPhycHAgJw9FHb5mABQ3qlaryGQyqlvnUs2n8nQkMc5QKBTgcDiQSCRuObjYGMXlzGKxoN1uY39/X81CBPmu12uBXkOhkGCldBsAkDBF4Q2AwNB0O4TDYYzHYwkbrEleLpfivczncwF5+f4T+Ez+C+vnI5GIFlKr1aqKYzo2lsul3D2z2QwXFxdydIVCIbWAsZJ5vV7fEqEajQY6nQ5yuRxGoxHa7TYePnyo/0Z3BOuZ6Qaim4YcGEbzPB4ParWagM+sb3c4HHKAMCLJljo6oQjvvVnNTQdUNBoVUHcymcgddXR0pEhis9lU9Ib8HMMw0Gw2VT3P6FsymUSxWJRLxu12C7TLyGCv1xM/hfXyFDfJXgEgtxAAuaPsdjuy2Sy+/e1vw+v1ypXF46fgyEavm/wXCkV8Xyl+UrBgHJDuD34unTasIZ9Op3rv2XzF5jq/34+zszNF227GvgAo4kaxxGw2y83Da7FcLuPOnTuqeOfPw/Pc5/MhEAjg7OxMr00wGJTo1e/3kc/n5XAh5L5Wq2GxWCCdTt+Kxfr9fjmFzs7OFKOlA65SqWC1Wkk0ozDkcDjgcrmQy+VQrVZ177BarXLCUUCmmMZrzuVySYhjdJCuLt7Pz8/PMZlMEA6H4XQ64ff7t46c7WxnO9vZzna+gLPp9LCxWQCTCcZ08ZFRop/Wmf6B12DuTRD89TcQ/PU3sDo+A9Y/3G6zns0Qfn/5oX9+9cs+XPyJCDp/7DHwtccwfu4VXP6JOC7+RAT9P/zqJ/kjbOcj5qWFHMMw4HQ6BV89Pz/H4eGhxAQ+HY5EInKU8CkvIwlkK5jNZiSTSfEJ2KZEhsJNS7vdbsfl5SUCgQAuLi4QjUblJLFareh2u4K/8v9fLpd48OCB4kPL5RLxeBxXV1eIx+Mwm82Yz+coFos4PDxEu91WnMQwDFVRj8djAYGHw6GiMG63W0+8DcNQZIA12DeXOAKVN5sNLi4uBFjlMtjpdHB4eIjvfOc7SCaTCAaDOD8/h9vtht1u19N8Lm3z+RyXl5dwOBwwDEMQXbIi6D45ODiQWEQXRyaTAXC9qJ+fnwu2yijIdDpFLBZTVIWLMKGtBCHztSS3hiIanUxnZ2cCsEajUSwWC0XcCDNmi5hhGBLTer3erZjU3t6eBKXZbIZYLKZ4XKlUQjabhcViUeSm1WrBZDLB6/WKRXR8fIzNZoPHjx/j/Pwc+XxeTA4u5WxIm0wmig0lk0lFW+i+4TkSDofFXXG73RiNRuIQ8cm/y+WSA2M4HIo1NJ1OBU4mpNtut+s94HsYj8fhdDpVz+1wOBQZunv3rpg85+fnCAQCipfwfAyHw4jH4zg6OpJIxvjUcrmEz+eTGwu4rrRmWxMZOGyVIhx7sVgomtNutyV6AhCgmW1XPCdZER0IBFAul+US6na7ihHyNY1GoxgMBhIrHA4Hut2ugMBcqj0ezy3nyXg8RiaT0bV7fHyMZrOpOF0qlYLL5UKj0ZAjb7PZCCLMynBeJ4wJrlYrOJ1OnJ2dYT6f486dO5hMJqjX62rkCofD4r2Mx2NYLBY4HA5cXV0hEokowkPuDXBbILJarRL9yJ2pVqvw+XwwDAONRkMRTcbECIWmk+fi4gJ7e3uCXTOmtF6v1fzE+85oNJJwPBgM4PP5MBqNJDLyvKWTjvcB3lfu3r2L9XqN/f19NJtNtFotvVebzUZfez6fIxqNilPEqCnZXIQLk2NF4W6xWOj6iUajaDQatyKlwLUgS1i6yWSSmNzpdFQVP5vN0Ol0UCwWsbe3J7GSbr7Hjx/rAQCFotVqJWA2I4J0Q25nO9vZzna2s50v1qz6fRijKTCZAhYzDIvlh3/ST8mYHA4M/9RXUX/Niqs/EoX53sHH+wKbDVz/7C3E3lrA1gVsXcD4AF2nv2dC8V/1oPBLXtWUd+6ZMf3DP//j/gjbeYl5aSGHDAsCJROJhJqMaPU3m836JZ/2djI92KC0XC5hGIb4BLTi+3w+OTooDIzHY8xmM+zu7qLb7WoZIwhztVqh0+nA5/PpqTifMLNGmE0lBMxyuSGwuVy+7rpPJpOq0KUAcrMC3GazIZPJ4Bvf+Ib4I8FgUAsLfz4uQ+RleDwefY98Pn8LHspIBFtv+JSeMQpyIuhmYksP7f+E4N4EGmezWTUW8e/s7u4CAAqFgtqo2MhC8SOZTOLs7AzNZhN37tzB5eUlMpkM+v0+nE6nXB98HyhEMHZE/k+1WlXzE19HAqT5sxPga7fbxbRglI5RKEaayJVhhGI0GsHpdMLn8yl2x5gJgFsuE4K4uaTfv39fAGfGZLxeryIywHVMplAowG63Ix6Pazllc9JgMIDH40G328VqtRIAmOKD3W7XecSaeJvNpveEgFhWsFOk4rHQNUEWC4UhCi50krBtZ7lcipdE0HW5XEYymcR4PEY4HMZgMECj0cBXvvIVgbffeecdFAoF7O/vYzweY29vD5PJBO12W68r30e3261Fl24PCq3hcBi1Wk18J6/Xq/hfMBhUzIr/LRAIiBPDiM8777wjt9RoNMK9e/ckkjx//lwRGrvdLn5Sq9XC/fv3EYvF5Bri69NsNuWAoVODIHHewxaLha71m9wZvmdkdFH8olidy+Xg9/sVYXQ4HHjy5ImcYuQ4kQnj9/txcXEhhwrbx25eRy6XS47AZDKJTCaje5/H40Gj0UC/34fH49F9kjXmPB9YFc7rNZVKKV7He2EgEECrdW2l5TXPmBwAFItFCcbhcBjz+VyuK7qgrq6uYLPZFLfa3d2VIOlwOCR6M55Ikebm/f38/Bzr9Rr3799Ht9uFx+PBZDKBx+PRPdnv96PZbEooJeeK92VC93u9nlyAjIu1Wi0Ui0V8+ctflkOR1ejD4RDBYBDPnz/XvZbgdH4+xXG6zm42Cm5nO9vZzna2s50vxph9PsAwsBmNgc0GJq/3sz6kT202jw7QuW8GDGBtAwp/OPbxv8ZyCfs/+xbi/+lvIf6f/hZctQ9uottYIBEHuObrOCvjH/XQt/Mx5qWFHNZ3B4NB8U4IsX327Bny+TzcbjcmkwmOj4+1GFFgIRuBlbKXl5d66svFarVaoVqtwmw2I51Oo9vtCmLa6/Xk2Ol0OrLFsyK42+0im80qAjabzRRronNmsVhouWBlNpeYRqOhxptarYZsNqvllIIOF8TlcgmHw4FOp6Mq4kgkgna7LScNeRPValW8Bv6MfHpPuC2rhd1uN9xutxavmwBRxk3oytnb29PSyAhMIBBAoVBQxCeRSIgVcnPRPD091c8TDodvAWETiQSazeat+ne6NygoNJtNRSuAa2i1y+XSUrxcLuF0OtWmQ1cA66lZm+12u/Xz8Odg5IrwX1Y2O51OGIYhFgyjNf1+H4ZhIJPJiJnTbrcVdUqlUoJRd7tdhMNhubmy2SwqlYpcGFyuKR6xeY0ixmg0UtvZxcUFHj16BIvFgkqlgldeeQWj0Qjdblc/PyuPb7JEyJLxeDywWq23oMbdbheDwUAuklqtpteen0+eTL/fR6FQQCqVkljGn5mtXzejaiaTSVE6LqaTyUTXUbvdVtSPjh2CeAlnnk6ncuekUimUy2Wd84zoEYo+Go1QKpUkbHU6HV178Xhcrxtrwjudjt7vzWYjBs7+/j5sNhtOT09hsViQy+XkIjs/P5eAzAa4TqcDv98v6DddK36/H3a7XRXV5+fnCIVCEmPZmkQh2mazyQHldDrhdrsxGAx0HxmPx4oWUugqFotqyaLwQuHM7/fj9PQUyWQS3W5XnDFyqRi5rFQqipBmMhm1kZVKJYl+vGfF43G1spXLZaRSKcUQ6bCiU4ttYT6fT8wx8plsNhvcbrfg8HQ1WiwWMc4ajYYiX7z3ZTIZvWfdbhf1eh02mw2RSESikcvlwmw2Uwvf+fk5gsEgvF4vTk5OFNlky14gEJBLqt1uw+v1Kk5FgXW9XsMwDEXyms2muESGYcDlciGRSOAb3/gGstmsHH8UiQm1ptORbkHGZN1utxqxQqGQhPjtbGc729nOdrbzxRkj6MfGbgOsFmC++NlqVVptbkGOTTfdNIaB9e/+Etr/ztfR/ne+DvPd/R/72xmra8eOsQQsE2Dz5vs/9tfczg+flxZy+ES11Wohm82i0+mg1+thsVgI6looFLBer5HJZJBMJtHr9VCpVNRws16v0Ww29QsyuRUAFKFwu936s2AwiMFgIK7DcrlEuVxWtMJms8nREQqFcHR0pIWNggmrq+keonBC7kS329UiYTabVT3Mhpt2u4179+5hNpspYrBcLlVly+rqRqOBcDgMk8mkp8yMXDCSRk7F8fHx9YtvMsHtdsMwDHQ6HQQCATx//hzJZFINQuRwcCEEoHhHLpeTEyWfz6NWq2G1WiEcDqNQKKgJiy4LglXpWkqlUoq2kI9CHorD4cD5+bncJRSK0uk0JpOJHEnkegAQh4KvM5f3YDCIWCymmmYA8Hg86HQ6aLfbWK/XODw8RK1Ww3A4VA203W5HvV5XzCQWiym2xeWXkR5yZhjXIj/nZjMaQcnD4RCj0QhXV1d6DSgWUUSiuMKn9OPxGPfv3xfzJZvNolarodFoIBqNotlsyoXA1iMu/hQ+zs7OtAxzeSYcuFgsIp1Oq3qb9eFscCOHp1ariUtD5wBwzV6hS8cwDJTLZUXFGEEbDAZYrVYolUrIZDLw+XzixDASVa/XkUgk1GLW6XRwdXWluNpyuZRjh00/jD+Gw2G1CxmGgcPDQwllJpNJldmj0Qj1eh3pdFpNY4zNmc1mnJ6eIp1OYzi8bhmgyFEsFsWQ4TnK95ACLpuHKD6wnYrvO4UruqoonpK/MhwOUa1WJfQx2jcYDBCJRPT67+7uSlChyJdKpdBqtfTaAJBww8YligjA9wRQCh48ToLZy+UyXC4XXC4XXn31VZyfn2Oz2ehcrVQqKBQK8Pv9iMfjuLy8xM7Oju7LPN95zdGxxfr0y8tLlEqlWyLHcrmE2WyWs6fT6eDx48eCskejUYm6s9lMohNh4GxJ432bzjQ65yhas6qc5xoA1Ot1lEolxGIxzGYzucoIfiafiaBq4Bq6zvfAZrMJhm+32yXiUYAnK4d8oHA4rOjicDi8xcIhdJ916NvZzna2s53tbOeLNetmG8Z0BsNmA+y2z/pwPtXZfOd9pP7lFL7zNXzna6T/1rH+bPV7XkPpX3FhlDYwShu4/BPxH1vMyfy3Q2T+6jeR+avfROL/8k1g88Hune18svOxHDmER5KbwSWeyxGftK5WK7RaLYkybIliDKDRaCAQCChitNlskEqlFE/igkp3DhfkbDaLyWSiZZXxrHK5LIdIo9EQ/JIuD1YU36z15nGazWb0ej2kUin4/X689957AvwSgjkej9HtdpFOp8X0ee+99xCNRhGNRnF+fi73ic1mw2q1wtnZGRKJhJggrVZLQGi2ofCJvclkQjAYRK1WE/yUHBLGVdgaw2gPn6R7vV5F0Sh+AdBT7cFgIHD0ZDLBo0ePbrkrVqsV2u22mqYA4L333oPL5cLrr7+OYrEIq9UKi8WCQCCAUqkEr9cLj8eDi4sLtXVFo1GUSiVxLFKpFEqlkqrGz87OcHh4KLcFxaBUKiVQNCGobAGjUNNoNMTPSCQSEjdYd16tVgWeZhQkGAzi5ORErA3WL3OJc7lcAnPPZrNbIFiz2ax4nsViEduk0WioEYtxFo/Ho1gQa9rZ+kOBkEIBF9FGoyHQK5k5LpcLtVpNoGJ+T8K9eU6zoWw2mymyVq/X5frgEs1Kcq/Xq5p5Lr08r242fRGeTX4TnR6MpW02G7FIGFey2+1wOp1yFlUqFUSjUcWROp0OPB6PBKbpdIperyfholKpyEXH7+X3+xWDuby8lOjAc/qmS4r3IgKUB4OB4obJZBLJZBIvXryQQ4rsF7Zd2e12iUM3W+gikYgcg4x10lWYz+cxHo8V5+r3+2rWajabyGQyakCik4r8I8KL6ZShaOXz+QRzZwsVnYwUICiGAZBQxWpuwqsZf6IIQsgx7928xnhekTnEGCNFJgq7POcYR2KLFN07jFAxdmu1WtFut5FOpyVK8TW1Wq0Yj8fI5XKYzWZqfrNarZhMJgCuXXZerxcWiwXRaFQir8Vigd1u178bdGQyOuZyuRCLxdBqtcQp4vXG6Fqj0UCj0ZCYCEAuQQCIRqN4+vQpQqGQjp2MNIKwt7Od7WxnO9vZzhdnTNEw8N3fnTY/Q9XjHPN/9xb8f/O34f+bv/090LPJjOrXHMCNl2NjARq/64dHryJvdm+5fG6OsdpcQ5T5sZ1PZT4WI8ftdqsOmXElRpQYm+GywSf3bBLhE3E6VOj04JPgt99+G9lsFtPpFLPZTEsJYzzhcBjFYhGRSERPdDudjqJZZPEwEsRWns1mg3q9LudFuVxGNBpFp9OR3Z9PX58/f65oEZcLt9stoGc0GlXkxe12y4EUjUa1HDSbTSyXSzx8+BAmk0mg1Pl8fqs5iJW6rMHm8sPa32KxqApvRgsodGw2G1xdXWmhzGazEsbIeqC7wOv1otvt4vXXX0er1cLV1ZUWpfl8rqhEpVKB0+lEr9fD7u6uXAXz+RyxWAx2ux1HR0ew2WyIxWISI1jL3ul0FJEjyJkLFKNsdOpwger3+xLV6HIie4XvIwDEYjFUKhVUKhU1aRFUTLGEkT1yNVqtFmKxmL4mcM3QGI1Gcu8kEgmBdFerlRwXNpsNTqdTgGAudsD1Ek0HC2G1rVZLT/8DgYB4JHQ7DIdDCS5WqxWxWEwL8mAwQCwWw2q1wng8RqlUwr1793Qd+Hw+OTMAyB3i9Xr1Gne7XdU0OxwOQYgpsFAMXa/XGA6HyGQysNlsiqEB1y1BDx8+hM1mw3g8VhU7RQXCpOkiYoySDpb9/X0JJSaT6ZbYQmErEAig0WjgwYMHWK1W4gzx7y4WC9RqNezt7Umo431kOp3K4WaxWHB1dQWXy4VAIIBsNisoMLlTxWJRgORgMIhKpYK7d+/CMAxcXl7qvEomk3K48R7C5qTz83MYhoHd3V3FIp8+far7RSAQgNvtltgJQIDrYDAocWM8HktAYrsaADG0eE5dXFzIKcj6+larhXA4jHK5rOaoy8tLAYFHo5FioPP5HL1eTzB2CpWbzQbdblfMI56ftVpN0dXhcIidnR0Ju+v1Gh6PB+12G4FAQOLpdDqV4AlA90KKQLwv7uzs6J7AqGK/38dsNpMQf7Ppi9cno2u8z1erVf07QzHOZrMpssW4JSHndO/YbDYkk0mUy2XdX1mzzvfm6OgIyWQSVqtV/17cbJCzWCwShrezne1sZzvb2c5nP4bVBnMwiNkf/DmM//hXMf3DPw9zOATD+gGOm8USG6cN62jgUz/On8ZZv/MMO/+sC9P30KKw9oHIu0vg7Ref3YH9DM9L/4bKp8u5XA7j8RjxeByj0UiV371eT4wTwikZsViv13Lj8Alro9HQcuh0OpHNZjGbzcSOOD8/RzKZlDOAywhhyIxmkDHBRZt14YSNer3eH4gWUOgwDENLGBehwWCAaDQq7gOf+OdyOZyenir2EI1GbzEtvF4vqtUqAoGAXDxc0mezmSz9m81GThLWX3NhuAlm9fl8cDqdMJlMYtrwNXU6nQAgSO96vb4ViWEEgguKyWTCkydPcOfOHcF2+dow8sYWHgKCWcWbSCTU8ETRajKZyEXBJY7g6XA4LJcQ4zLL5RK9Xg+TyQQOh0Nslnw+j1AohMvLS7lw6Foie2O5XOLq6grBYBA2mw35fB5nZ2cSAlhBD0BsIYoehNYCwGAwQDweRzKZxGg0UryKcaZWq6WGHNbTE8IdiUTQarW0KNJh0mq19HdGo5Eqk9k0Rk4Ia8wTiYTESYpLbPXi+3RwcICrqyuxcigqrVYr1aKn02m5KFarlYQPApcXi4XEUIpOBBUzRsbzjUIoIeYAFMch2yYajQoq22g0JEyxqWg8HqPdbiObzaqJbrFYKPLGrzGfz+F2u9Fut3Uem0wm+Hw+HB8fI5FIyEHC65H3HVZuk/PCCA2rvJfLpe4/hATTCUNBoVKpYDKZIJvN6tqgaMWIZywWQ7FYhGEYYv3QGURGGEUIRufoVopEImKrUEgZj8cShxqNhkRRuu/I8mJTFmc8HmPzXVsq7x+8x90Utv1+P4bDoVxbDodDfCYAimmymc/lcsFisUjY6Xa7CIVCsFgsePbsGQBICGT0i1HXUCiE9XqNi4sLnee5XA6dTgelUkkxyr29PdTrdcxmM/Gx/H4/TCYTyuUyrFarnGKBQEAxMqvVKqGa4gkdf7zWb14zbJJjM1+/31dkkM4suos6nY5ch4wzMuIWDofVSkiWGyNjLpdL95DtbGc729nOdrbzGY5hYP3zD9D55XuY+c1YOgzMvSZ0/sBdrH7h4Q+0Um1GIxiLFYzp1lmr2awROPk+x8wGCL54OTjx+u2nyP7zNmLfXiD27QXSf/M5nP/4d7BZzH/4J2/nE5+XFnKGwyGWyyXcbrcWMj4Jnc/nsNlsOD8/F1jU6/WKq2KxWMR64XLDp+ypVArD4VDAydVqhVQqBYfDIV7CzegUI1NkpLRaLcVVuDzHYjFMp1O0Wi3BSukc4mLbbrexs7Mj5gJrnBOJhCz7NpsNpVJJT/jX6zVWq5WqmfkLP+GfXJ5ZtUuWD1u7AMgRkk6ntewBECOHnJ5+vy/H0s2oFxd8/vxWqxXNZlMCGYHODodDNb2EKlMgIedotVphMploQWfMiAIKv+96vZariA4XLrXD4RAejwfz+RyvvfYaKpUKWq2WFkFGVqLRKFarFS4uLuB0OiUCkNdDyC4jIvF4HE+ePIHT6cRoNJLAR6fQYrHQ0/7ZbIZut6uIEIVGNiiRMTMcDiVO2e12VKtVOU74/pBjsru7q3N+MpmoZp1iDuMeFBn9fr/cREdHR3A4HAiHw1qayX8hx6PdbismRacSALks1uu1joHnCIVT8pKWyyWKxSLC4TBCoRA6nY5ERZPJhKurK0VUuJBSGKWjhoJBOp2We4PtZOFwWBBvQp6j0aicJeQtMQZIwDCX6NPTUzx69AjJZBLT6VR/RtBuKBTCeDxWZTVrpimkkctltVrVosSfnw4MArgp3tCpQWaOYRhayEOhkFxQFMdisdgtx1W1WhU/hdcHXU6DwUBiIZ0k/Pnn8zni8bjcfGS6EKI8mUwQCAQUx7oJDCb/azQa6b7Cnysej+scoVuKtfT8+SkWUlQl3JhMLHKvKDATQp3P5wUcDgQCipLynkFuULPZVKSUopHD4bh1vdCJxusQgKJ9hUIBZrNZTVpsj6KIy2P2+/061+v1ukSYWCym42u1WhJgKIbS3UdgOYUcRiA9Ho8eBPBaZUzs8vJS12Uul8NisZAb0mw2S0Dfzna2s53tbGc7n+2YnE4Ms44P/LNhyg7TDdYdABgBP7BcwRiMALMBczT6aRzm53s2G3j/n+8g9HQF0xwwzYH0/3cC440nL/0l1k+ew/7PvwX7P/8WVq32T/Bgt/PD5mNFq8gY8fl84paQMeB2u5FIJDAcDlEul2G323H37l0cHx/D4/GIh8GnsKwBrlQqcLvdCIfDivcQnrter1GtVhGNRhGPx7FarXB1dQWz2YxkMinhZjQayUVAyCWFFHJ2CNr0+XxqMyLHhEIEgbB0g/AJN/kd8Xhc7TrkBDG2AkCCEQA92WWzDRfFO3fuoNvt4uLiAg8ePJBIxdYotkKFw2HFH1iPPBwO0Wg0EIlEkM/n8eLFC8GZya4gx+T58+e3gJ/hcBidTkcMDAJLp9OpXAR0ahDYvFqtxBuhq6DT6aiSdzQaIZlMqjr58vISFotFgFk6s+hGWa1W2N/fx8nJiZZTgo0pKpAx8uLFCzx+/BjANZ+J4hXjXuQG8evM53M8fPgQ0+lUribGingecJluNptyKtxssmJ8h9BfCgN8zeg0YrNWt9uVc8Dtdks0oajCc2G1WmlJfPHihcQznkcejwfRaBSNRkPi5sXFhd6v3d1dwbtZW9/tdsWkWiwWiv6USiVBtG+6CaLRqNw9AHB0dKTzMh6PKxbVbrfh8Xiwt7cnQYr8nMVigVKphFQqJV7JbDZTdGg+n+POnTtot9uIx+MIh8M4Pj5WlbrX60U4HL7FrBmNRorR1Ot1xONxvYbr9VqMGbpvKGQwWsf3jFXc5GlNJhNd53QCAlC71Wg0UjsTX8Ob9x06Q8iUoVvO5/PpNeFrTffR6ekpXn31VZ3HbDsymUwIh8Po9Xr6Om63G7FYDOfn5xIyeJz9fl9V5aenp4hEImrrajabcDqdyOVycpIZhoF+vy+uzmw2QzgcRqvVQqfTEXeJFfe8j52dnWFnZ0fiMh1BvV5PjYBnZ2eK47FKnmIu2/JarRYODg50HpDZZTKZFEvltcLYKAUSCqR7e3vYbDa4uLhQYyGFcsbeJpOJ7juDwQDJZFL3aQrVVqtVPCxGL00mEx4/fozFYoFKpaJYKLlfN4XqVquFBw8eKGLFOOt2trOd7WxnO9v5bGf2C4fYfNjmagDj333vB/+7xQw47MBq8z1OzM/4rKdTuP/BN5H+a28g/dfegOkb39nCib+g89JCDp+C8snnbDbTkrBardBsNmEymWRdJ6/hwYMHOD09VY1tKBRSXTJrnSkeeDwe2O12PHv2DNFo9NbS9+LFC9jtdvh8PpRKJcFDWV8diUS0uLOiFoCEGEZgGA1ZLBaIRqNotVowm81abuieqVQqWl7IZKALYDAY6OnueDxGLBbT96lUKmK+GIYh4YWMFMY8DMMQ3JaiCmuMuaz3+32Ew2FFK7rdLvb39wUq9vl8GI/H4tCEQiH9rAB0HIycsIWG4gthtGygoaAxHA5hs9lwcXGhp9yTyUTgYsJLGf2gWEKA7tXVlaI35FN0Oh0tbcFgUD8v27kY9RkMBuj1eop7DQYDJBIJAFBMj/E4wnkTiQTW6zXOzs4QCoVQr9cFtQ4GgzCbzXp6T46Tw+FALBZTzCkej6NSqcBqtcLn8yGVSgkGzWpjulX6/T5arZYA2dlsFpeXl4JG0wE0Go10DlL0i0QiEpl4/AR03717F/V6XYIjI03kNXGRJr/DMAzVKFPcYHyP0cVsNguz2ayoCwUO1jLncjlcXFxo4eVC3+l0JOTRgcMIVr1el4gwHo/ltqJrjPFLOtM2m42u1fF4jNFohEajgd3dXbknBoOBopY+n09C7Gg0gtVqFbza4XDA6/WiWCzqvOOy7/F4MBwOkUgkJM6QA0XnF0HQFDkp3pB9xfglmV68rnn9EIYLQIwhu90urg3/HuNB4/FYrVqNRgM+n08QYV7XdrtdcchmswmbzYbBYAC/3y/HCwVsCot08/l8Prmwjo+Pkc/nJbKbzWbs7OwoisjXgOcdBZlAIIDJZIJerweHw6H/Rrchm5tYgc7rFLgWr+PxOK6urgSvJ0/N4XDIRUbIMWObbIUjywqAGEelUgm5XA61Wu1WA1y5XEYsFsNyuUQ8Hsdms0G5XIbNZsN6vVYL1XK5VKyTLsV6vQ7DMNSmSGBzq9WSwEseGOH1FD15jmxnO9vZzna2s53PcEw/IrDYMGBshYofnM1mK+B8weelhZyTkxN4PB4kEgnU63WsVivs7OxgPp+jXC5jd3dXT0q5FNIJQWYJoxKTyQSJREIMDTpqCEdmc9NwOITP57vV5sNFju6ZyWSi6vG9vT25MwjVpSNlvV7rl34ed6vVQigUUtyHjg9GMp48eQKfzyc+S7PZFESWDpFAIIBIJIJyuYzpdKrWJfIbvvKVr6BcLqNeryMcDkvMsNlsArryGBklczgcqgsnA6jT6WBvbw/f/OY3BcCNx+OKfUynU1xdXeHw8FARm5uOod3dXVSrVUF0+T4RUkqnEuM+oVAI6XQaq9VKQtxyuUSlUkEoFFJN8XA4vLUgsnGIyzTrrSkiuN1u7OzsiHsBQIsYF2aHwwG/349+v6+mMjIyCNjlecDoB7kli8UCh4eHODo60vejwEf3RCKRwPn5OQAgEomg0WigUqkgn88L5NvpdJDNZvHixQvFSnq9nng3BBQ3m02Uy2XkcjnE43E5JMiMcjqdEmqAa1gya+rZZMZWML4/drtd1dwul0vMnWw2q3gPl3A60fgaM6rI64gMJLvdjmg0iul0qvjQ7u6uWo6AaxZLLBbD6ekpZrOZGo1isRhCoRBevHghIYLOKZ5DFA0pOgwGA2QyGQGj6cChCEBGFV9rRmf4XrpcLjx9+hSvv/46KpWK2okCgQCq1arOfYpAVqv1FnCbLjBGtE5OTjCdTnHnzh243W4Bo8/OznRN0lXidrtxfHwsEZRigN1ul4vIarVKQLzZ9gRcu7Oq1arEtUqlglKphIODAzWFjUYjnJ+f63wAIMjveDzW9UD48OPHj9FqtQR8JsOIwthyuUQ2mxXnyO/3o1AooFKpYGdnR5EtihIUExlx5PclP4pCMkH2g8EAX/3qVzGfz9HpdG5xvRaLBYLBILrdLsxmMxqNBjKZjNg7FJrZKMXXZLFYYG9vT3B0CqHr9Rq9Xg/pdBp+vx9vv/02XC4XIpEIer0efD4fnjx5Imciodb8N4DRr/F4rEgZG/YoKgKQu5T3da/XK6j9ZDJBuVwWZHk729nOdraznQ8a8509mF491IclmfisD+mndizDBfARuoN1tPzB/7jZAKsVUGv+5A5sO9v5jOalhZxYLCYXBZ01FEb49JQLD6GlV1dXWngIIfZ4POKYANByTbcBYzvT6RQOh0P8CEJa2S6yXq8lcrB++OLiAvP5/FZ0a7VaKXbEeAcjPX6/X59bLpe1XLbbbTk9/H4/5vM53nnnHbW+hMNhPbkmjDebzYoVwuYePvF3OBxIp9OYTCbiwhAszAWDMZybwgRBtAAUFwgEAlgul2reYtUzYcUWiwUul0sOCkZIjo+PFRNgzMHr9arNBbhmsLRaLblKFosFDg4O1DpDeCmZL+PxWA4hLkmE2dIZYrfb5Xxh9KFer98CJw+HQ8TjcUV/CNMlz8fj8cjRQWCsxWJBs9mUmEB+0GazETOEMGkKW8D3XD0Un7isA1DLEwBBc/ka0p3icrng8Xi0tPP17na7ePvtt7G/v6866cFggPl8jnw+j263i1qtJtcC41XkdzAaQ7cb/7xarUrQ5FJJ5xYXUPJKKOrw3KpWq+KtEC7LmnWn04lisYhms4lUKqWoGH+2WCyG8XgsQZANaOTZ2Gw2RU4uLy/hdDoRi8UUnWTDERlVjAvyOqaQRWGU5/PBwQFOTk4AQI6t7xcDTCaTImDVahUHBwdqa2NskEJhKBTS8dC9Q8cYI5QUECl68fUKBAKo1+vw+/2wWCy4vLxEKpXCer1Gs9mUg7Df7wukS4cceTOMBfKecfO8SyaTEr94jlJ8YKsfrylG7ur1usTT8XiMg4MDtNttub/oWOt2u3LC0U3JuBJFun6/r6gZxTwAYpG9ePECe3t7EnmbzSbMZjMeP36Mi4sLXUc8Pt7H8/k8isWihMhgMKjXcblcqkGQsbxmsykRmq4/tqrx/mm1WuF0OrFYLNDv95FMJhUl5P2P8Sv+bAQ/s92QUHvGPsPhMKLRqIDYFJQZBeT7wFat7WxnO9vZznZuzdce4+qvOmH+P3f18fQv5dD6n379sz6yn8ox/c77MD5MyNkAlm8++4H/bPRH2Nis2CS2fJzt3B6Tw4HFL30Zi9//FX2sf/eXPuvD+ljz0kIOF8hyuYx79+4JiHqzEjiVSsFms+Gtt97CbDbDYrEQg4HxBnIUWCnNymbyWux2+60qYS6IZI6wPpdNPFarVb/4cyFbLBaK/XAB6Ha7AiczCkJXCpfX4XCITqejKvBgMChoLxcmi8VyC3BbLpe1nDEi5vP5tKwS/Gq329FqtRSDYbyHVewEtF5cXODx48dawAaDAQqFAsLhsKqQ2ThF4C95Dg6HA++++y4A6Kmz2WzG0dERAODBgwda8BjpYCSOr9HBwYGApHRbcEHjeWCxWNButxGNRvWaEcy62WwECr28vITD4cDR0ZGcEBTw6EogHLvdbsuNQyfI7u6umqDIaQkEAorssHWH7gHWoLfbbUXvxuOxGo4o7PT7fZhMJrU/Wa1WFAoFOcfo5mo0GrDb7RIi7ty5I7A0W28YmUmn0xIs2c7D2BWdR4SqkrNDoZIOAr6XvBbowgGuI2GMdjkcDpyenuLu3btyuBG2bBgGvF6vzp1ms4lIJKL4FGOEFC3MZrMETOBazGNlPfC9uAuvsWQyKZCx2+1WjJE14gQVHxwcKEJDESUYDKLT6SiuSBcHRUIKq4w13bt3T2wXvh5er1eQ4hcvXuDOnTsYDAZyLlEciEajcp7VajU8evQI7XYbz58/Rzwe17nN/5/wdfKyTCYTzGYzJpMJGo0GrFYrMpkMqtUqYrGYmEK8Jur1ugDHZL/U63XdCwjrrtVqCIVCOt7BYIB2u4179+6h1+sJ3s74JxlgdMGwdS2Tyagpymq1StSiE4XROzpU6JajI2i9XqtNjg7HYDAoQWswGCCbzaJarWI8HmN3d1e15JeXlzg4OFDc0263YzKZ6OvwPGRrFJlOrD2v1+sCMwPX3LDpdIpyuawo7WAwkIDP145Oy/l8rp+f0HG32y2Y9U23IP+9uQl8v9lIeHl5iVwuh0wmgxcvXug8rVQqur+121uQ33a2s53tbOf2mF49RPl/s0Q+dPvfiIf3CzD98SbM24cAn/hslksEnrRhnm++58zZAOb5BoH3u1jfeChl8nqxcTsBqwXGZAajVPtsDno7n8sxrDa0/9RrqH7djupXbfoo/6ILi1/6MmAyf9aH+FLz0kIOW2r45JXwYMMwZMmnI4JPwdlcxegIq3r9fj9KpRKcTieq1ars+YlEAg6HA+12W5wJCkVsvGLE4+7du1gsFlrw6Vag42c6napZ5uLiArlcTuIDF/SbMFoyKZbLpUDDV1dXiidx4fZ4PGog4gJPlgqdQhSPVquVWDl0fvAJeKPRuNWm4vf7BSpmUw/bhnZ2drBcLuH3+/H+++/D5XJhvV4LDEvuC107dDTRvcIoyXq9RjabFWOFHBwOl6zpdKpoF8GsiUQC/X4fhmGg1Wrh3r17KJVKGAwG2Nvbw4sXL/R+8Gfnk2ybzSY3i8lkEk+E7x1FAKfTifPzc7WatdttPHr0SLwWChnkmbDVhs6Iy8tLNZalUimdl/1+HzabDfF4HC6XS/BU1rw3m00kEgkBfBmNc7lcatiKRCI4Pj4WmJvOqkqlIvYS3zs6igKBAJ49e6aGIEKzucRymWSDFOMyjUZDjiIKCqVSSUux3W5HJpORs4eRxGQyqaYh/uwUi+haWCwWgu6S3USBkZwpLtjJZFKNVWwLI6vFMAwJkRROm82mWE1kDRGa7ff7cXJygk6ng/v378NkMqHX690St3htpVIptbFVKhVFrVKpFObzuQRYHrvJZEI0GkW/31dE7ftbkAaDAUKhkMRE1ql3u11xaOgyY7SIkUO73S4HntPpxOnpKR4/fqyIGu+Hu7u7eP/992EymZBKpeB2u1Gr1XSObTYb+Hw+RVPJi9psNjg6OkIkEhGbKRgM6prl57HyPh6P4+joCPl8Hu+99x5SqRQAyNXE471ZAU5uDZ1zJpNJzsler4ezszO43W5dq9lsVteKx+NBq9VSnK9er9+qMKeLiBXlvHbo1PF4PDAMQ9cPW9rotjQMA6lUCovFQjwqMoQoMFFEogOO7WDk4lBsp2PHarWK0WOxWLC/v69rE4AcpWSETSYTZDIZ1bTTYcX3aTvb2c52tvPjzfiPfRWdf/vr+jB96cFnfUg/1hT/soGdYOcD/yzmHuLZf3QPlnzuUz6qn/5ZPT2C++9/E77zMTxXU3gvJnD/g9/B+snzW7yX9WAAYzTBOuDBOuLH5rsPpLezHQCY/muvYpD/QebSxgxUv26H+d7eZ3BUH39eWsipVqsC7NK6zqfj7XYbfr8f1WoVfr8fwWBQLgEulgTmzmYzQVr5izPFgF6vp2ph1oJvNhs15XCRJUSY0Nvod+vk2FZElwFdMpPJRMwOii+GYWAwGEhc4sLQ6/XQ6/UkdLCVazqdavEAgPl8rpaZZDKpul4+rSeA2WazyZXgcDi0sPr9fh0XozvBYFAuHi5A5J3cBPvy+xeLRdWt0wETDoe1PLMt5u7du+JolEolZLNZuYf48/l8PjlJ6Lwgb4PLcigUkhvr8vJST8o7nY4cEKwl59NyRqDofBmNRgKWcokbj8fie9yMttAZcNNBwmify+XSa0Lhi5Bcvs6sCid7hG6Pm3++XC5hs9kksFSrVVgsFokahBszQjOfz+UgWq1WyOfzep2//72lK4ff4yb3hvXyN+u9CVbe39+X46darQKAFn66Nchr4XvIxiaXyyWwdCAQENCaSynrtO12u0QSn8+HUCik78VGrMFggFgshkajAZPJhHw+r+pqipSBQECMJpPJpD9zOBy4uLjAZrNBNpu9Bcrlee12u3VvyeVyYtw0m01FNfP5PFwul4QuvsbtdltCMt9nig6JRELCM2OWvPfweqTDhAKUyWRCJpMRJwUAnj9/rvOK9wI67Hq9HpLJpFxJtVoNnU4HkUgELpcLpVJJ5zxdI2azGYPBQOIBz32n0ylh0ev1olqtwjAMZDIZANdCaLfblUuHLCsA4iaxwY33PcYL2aBFl8poNNJ9lM1fw+EQ2WxWwhoAib2E/jIyyntDq9VCuVxWhIw13+VyWa8h3VGBQOBWwxmr4nm/o6uMEUm63uj4ZHsVBR66MyORiBxOfE82mw2i0ahg6YwyVqtVJBIJNbmRO+VwOHSPAKCmq3w+r+gmnVfb2c52trOdjzmGAdOj+zj6ta/g9/7l38Sv/MW/p4/lrw5w9GtfQfPPfTFjSMaHZnyu5+G9IvqvJT+lo/kZnN9+AtNvvg3jjXc+ENhrcruxcTlgal4/jDElYp/2EW7n8zw/Ijf78zYvLeS43W643W45SsLhsJZcPsklR2c6nSo+wvYem82mdpVCoaA4QzgchsvlQrvdFkNmZ2dHddHRaBTRaBTn5+dqN/L5fOj1enLq0BEDfA+caxgG6vU6Dg8P4XK54HA4UCwWtQwx/kMWDet++eSdLS6pVEp8D7vdjk6noyYkLv+VSkXcGy4L9Xod3W73FifkZtUznUeBQEDLdKPREO+EwhXFlX6/j+l0ikQiIaYGIbS9Xk+tP1arVbErOqQ6nY6cBXt7e3jrrbcQDocxGAywWq3EIeGxAlCjDlkSdEGsViuk02m9XwDg8XjUglSr1bBYLHDnzh3Y7Xacn5+rEYrugn6/j52dHblNTCaTfr5YLIZwOIzLy0uJFVzeubgHg0GdU+l0GtVqFaPRCPl8HpVKRcsjn6wD0NeZzWaIRqOKvjCWwWpxtjF9f/SILWdkPx0eHkp4ohBoMpng9/vx5ptv4hd/8RcFzjWZTLi4uBA/Zjgc4v79+7BarXjy5IlamSjKVCoVgbrZZMVlttvtIplMolqtKjbF+M18PkcikRDPxOPxyE1Bpozb7YbH48HFxQWy2SwWi4WuCZ/Pp1YswzDktKGz6zvf+Q5ee+01RZLI7zGbzVqe2+22WsIo8lBEJDCYsO9OpwOv14uHDx+qWYmROIvFgvF4LBA46+0ZxaNgelPk8Xq9im6xvY1iSiAQkDONHCfGd0KhkM45soOWyyUeP34MACgWixJtKRw3m00JgiaTCfF4HI1GQy6b4XAoUPJ4PJbYxsrrbrd7KxLG2FCxWEQoFBLjirEu8onIziG3h+4qm82m84ARR4oSFJ8ASCA3m804Pz9XvJBOSb73FJgpfOzt7cFqteL09FT3MrJwKNjS1dVsXgMFY7EYhsOh7nepVEr3/LfeektiFaHu5IWVy2WJqbPZDKVSSbEsxqPonqL7EwDeeustHB4eYr1eS7RlTXo4HMbV1ZVYWd1uF6+88gqq1SoWi8UtR2ipVBKv6CYLajvb2c52tvPyY0nEcfzrr+Frf+sJ/srv/sfYt9+Otvxb6TfwV373P8a/9Rf+Oap/4RdgevXwMzrS7fw0juGwY2OzYB26Rk2sG63P+Ii282mNJZmAOfqDTCST1wvLXh6df/vrmHu+GNGpHzYvLeQA0NNtOl+4mLhcLsEv+QSXUFMAckwQBnqzhpiRJz6hLRaLckvE43Gcnp7CbDbrKS9jV1zQAcgNk0gk5OhhDOvdd99FKpUSzDibzWI0GuHq6goWiwXpdBpWqxWr1UqRAUZQkskkhsMh5vM5arWa4gZcmsnwsdvtcv/QFdFutyUQ0OlB8QmAYKeM07BFZzqdykVBWC4X8NVqdasm2uFw4OzsDPF4HMC1iFUoFCSaUdihk4g12PP5XG4KLkeFQgEmkwmRSESRheVyqdiNx+NBKBTC2dkZUqmUapktFgtarZaWcp/Pp/iFzWbDl770JUU7CoUCrFYrQqEQjo+PYbFYtMhR3AsEAnJ3NJtNfc3VaoVwOIyzszOUy2XcvXtXr3ckEkEwGMSTJ0/g8XjUwMNFjuce3+ObbimeBwToRqNRLbRchBnbYytVKBQSZNVqtaLf7yOTyWA4HMIwDKTTaVxcXCAWi2E+n6sKnE4EilJ0a3Dx/P6abtbL7+/vo9Fo6L0rl8vY2dnR+06uSbFYFMuKrhiTySTId6fTgdlsVq09wcU8l81mM1qtFu7evYtGowGz2YxOp4NHjx7JBTQYDG4JoeRgkddCYY4CDyM4jFvxazI6xnsJ66DptAGA3d1dNBoNsakeP36sdrhms4n9/X3VUjMO2W639boyYkmRhIs/HUZms1lAbraxud1uuTVYa37v3j1cXl4CAFwul143coXYcsco1uXl5a1qb74PBOrW63UEg0GBtNvtthwnfr9fkTrCsSmoxmIxAXiPj4/VTEYQNQVktruZTCaJSmyP4zGRj7VcLiU29no9RRitViusVqsEaoLdya1iIxsF6X6/r+uDYh3dUoxG8r7sdrvFF6MYzEYqtlqdnp4iEAhIdHW5XDg/P8dqtdI91+FwCHLPfyPW6zWGwyHS6bQcfWRo8edhbXu1WkUwGFTbYKFQkLuSjBy+ptvZzna2s52XH5Pbjef/cQp/+ef/KXbtH30PDZmH+Pf+3f8Hov/XElb/6uuf0hFu52dlTP0xjOEEmxv8nO38dI7Z58P8X/85FP7sHir//TuwpFP6M8NuR/uPPsL5/zCFYc5A9+7HkkA+t/PSPwWfWvIpN8WBzWaDq6sr7O7uIpPJ6ElovV7HZrNBu91GJBKBx+NBsVjEnTt39HR4Npuh2WwqGsQaYj5VpuuiVCpJcOEv1RaLBZPJBNVqFfl8Xk+FuSB4PB7VIvOXebfbfYsXkkwmUSqVAFwzNLiAmkwm1V7P53PZ++myISflJiSV9biMVaXTaUynUxQKBYxGIzx69EhgV4KELy4uEA6Hbz2FHg6HiEajKJVKCAaDCAQCCIVCEr7a7TaGw6EabwgN9vv9MJlMsFgsODo6UoSFEatEIoFms4l+vw+XywWr1YqrqysEAgHBWPkeOxwOdDodcXj4nnS7XYTDYVWf0/HDpYlL6nw+lwuHohCro9nIxDp3skMo2FDoe/z4seJjdBsxWsSmMDo1KGxRkDGbzXJKsPGJrzsBtnQeUSA8OjpCKBSC1+uFx+MRk4bOHrvdLoA1hb18Pi/HyGw2k6hkNptxcXEhITMej6NWq8nJQPAqrydGYsgAIseJogHr4M1mM1wul1qPGC3hcdGpYLFYMBqNBAbnUs64C0XDTqcjmLjZbMZqtUI8Hldkr9friRlD0YqxSeB7LV+1Wg1ut1tuDrvdDr/fr9amwWAAn88Hs9mMcDiMZ8+eye1Axxmh44xbMSJEl5DL5ZKjgu11dNMxTkgA9nK5xOnpKVKpFEwm0632MUakqtWq3BYE4rK9ieLraDRSDGy5XCIejytGSAGz0WgIEkzA+GKxwGw2k9Dh8/lQq9Uk+BC0HIlE5H5hXIzOwvl8jul0ir29Pb3e/O+MsPG8JLSY4jGvBUaCeC+kaEhGTywWk9DJSBXjVbPZDMFgULHV0WgkkZnRPEbqCJD3+XxyArHZK5/Pq33L4/FIPOJ5zPs7OTk8x3O5HFqtForFos7bXC6HarWq46N7hscDQJ/PeCnPSQq3FKUvLi6QSqXUdLZcLuV8Y9zRZDLpfNvOdrazne283Jh9Prz46/fwl77yX32sz/ul4FOUf5cDhsXyEzqyT3ZGQ8dH/vmzt3fg/Y3nn9LRbOcDZ73GxuWAMZlhs/yAavLt/FRN7U8/RO3nrFi6gFkIKP6pPEzfTbp0/+RrH8jE+aAJHK+xPrv6SR7qJzYvLeTQtRIMBvWL+2KxkP1+sVigVCohmUxqiSDnYzAYYDKZSExgu002m5UDh0BPNkCxgWq5XKq6++zsDA8fPtQTajpq6O4gc4bw1Fgsdut70CFxdnaGBw8eoFAoKNJCESmTyajWmcvi/v6+rPzktVxcXMDtdsNisQhoWqlUJHTs7+9rGfN4POj1ekin06pCp+voO9/5Dvb39+VeYEVzq9XS8kemDAUdAAI8szmGTgoCbilApdNpRYUoavj9fj1p57HQtUFwMhkbhBRbrVY5EdhSRUcRm44o0AQCAcWDACjeNZ1Obz0BTyaT6PV6YmIsFgvE43EMh0OUy2W0220cHh5KvKEbi/BULumM8rGanWIMhSk22Ph8PgQCAYkCdrtdrJ1QKKTjJIPnpjtiPB7j8vIS9+7dkyug0+nA7/erMv7hw4d48803MZvNsLOzIw5Ms9nE4eGhwLO9Xg/1eh3ZbBYmkwm5XA5Pnz5VPKxQKCAajcqhwHhXq9WC1+tFNBpFt9uVeEBBJhqNagElc+SmCyWZTMIwDDk0KPQNBgNd52w+IszY5/NhOByqUYpLL0Uor9eL+XyO2WyGg4MDANcOlVqthlqtJodUsVhU81Uul0Oj0RBs12w2o16vIxQK6TpyuVwoFosSTgaDAcxms1hBvMZzuZzuM51OR+6P+/fvy62TyWTw9OlTxcaSySQymQz8fj+KxSLq9TrW6zU8Ho8Wf5PJhPPzc4ktrEKnCM167Gg0inK5LN4N3S287xFG7vP5JBBRjG42m2rDmk6nqNVqt9hM5OaUSiXs7e3p9SyXy0in0wITMxJEYeT4+Fj3Z7JjHA7HLVGPrjeKuMPhUNcQuTIej0fcIZPJhNFohGg0qihUs9nEdDrFK6+8gmKxiGKxCLfbLSFmtVqhWCzC4/GodQy4bkIjS4jnFGNyFId5/e7t7emBwYsXL+Dz+RCNRpHNZnFyciIIN8UwwzDgdDrFzgkGg/p3irB6crQYZex0OpjNZkin03LfpdNpDIdD1Go1OcS2s53tbGc7P3wu/vwj/OWf/6c/0uf+r/9Hfx/Vf/fnP+Ej+snM/X+/jvfPUx/4Z5OlFbFvAqvuFpb/mYxhwHC5YOqNrkUcjwuG1fZZH9V2fsIz990WahYeYHO4DwCY+V/udzn/6Rq+48EXxsH10r+h1ut1xGIx/bJOyGw8HteCQGAkq2YB6BdtAOLpsLmIX5NOBFrfCc90uVzwer04Pz+Xy2M8HisGwHppfk26SMiCabfbilRxuSdcdDgcqu683W6r+YVPpi8vL7Gzs6OGldVqpSgJl5xKpYL5fI79/X0UCgUcHBygWq1iZ2cHFxcXmM1myOfzsNvtOD09VdNRt9tFLpeDx+PRwk6HCgUCxg9Go5F+BrYEMdZ0kwFit9vVUhMKhfT3GX1YrVaKHnDxXy6XcjRwub8Z3SIUlQIZ4dJ2u11V0ozOkJHBWmDG7m66BHK5nCrJDcPQYhcOhyXw8RxiPXi73ZYYRkfVer1W2xkAiTeMgNhsNhSLRTF3+ESerB3GMugW4TnFc8HhcODevXtoNptoNBrIZDJyvpCRxJgg31O6ICi4dTodOTwoarKenGLg5eWlYmMU/igGWq1WjMdjJBIJmM1mFItFHTMB3sPhEMFgUADmm+8xHVvHx8c6Z9g8xPPF7XajXC7LkcHXltdnLpfT9VWpVJBMJgWzJXuELCFGUxgFIsuKYtFoNJKIxO9HZwoF3kqlgul0imQyKcGP7qpoNAq73Q6LxYJOpyMXDd0qvV5PLjk6i9haVyqVFEm6uLhAIpGQcJFMJsWyId+LTp9QKITVaoVWq6UI3U3n2Gg0wnA4RCqVkhMxGo3q/kCBiWLIaDRCq9VCKpXCbDbD3t6equRDoRC63a5A0y6XC8lkUgIH752TyQTT6RQ2mw2GYciJxONlSxmdcldXV2qS498n+Jk/x3w+Ry6XE6eK8U+KNTy36NYjq4qCZ6/Xg8vlQjgclkunUCgAuK5m59ebTqfwer1oNBrw+XxqHSOfiecI3UN0K/Ge6PP5cO/ePbzzzju4uLhAOp1GJBIBALnRGGXkdcF/DwhqJoeHbYZOpxO5XE6A/cFgIHGY59EWdryd7WxnOy835rv7OPj9Zz/y55uwxqM/8/QTPKKf3CyLJRz+H7t4XkpgsTLr4+i389j8tRh8f+e3P+tD/NmdzQbYbLAOerGxWmD0Btgs5p/1UW3nJzwfxB83FtdOd2P9wXByT2GDnX/a0Yf/H3wHmzff/0ke5ic6Ly3kOJ1OVCoV7OzsqEJ3Op2KE2P5rhWSTgev14tgMKgnmi6XS79sz2YzwVytVquao7gUGYaBy8tLgX35lHQ8Hmshj0QieOeddxRJodOC7gU2vwQCAQSDQfh8PvzGb/yGeCqDwUDQUlYoE+Dr8XgQi8XEQ6G9n9wfLlW5XE6vQzQaVSMMAC3VpVIJJpNJAFda+QmSZRU53T3j8VhMiMVioVpyft1CoQC73S6BhS1B/GAFL10k5+fnODg40AJVq9Xw6quvihPCNiYu7/v7+3oCTk4KW58Mw9ASuFwuVT1M6CthvFarVZDWQCAgblKj0cDOzg4ajYaauFKpFOr1uqq+ye/pdrsArtuaGGtjXf1sNlMVNeM7LpdL0YlqtYp0Oo2dnR0xZRjropOK0RSv1yumDmNdq9UKzWYT0WhUFeOpVArxeFxxMApVdrv9VpW8zWZTWxXPk/l8LohvOp3G5eUlIpGIoiHANRi2VqupvYkNTXSn0bGWSqXEWnG5XKp0rtVqioHQWUGXCYWWm8wlQmcpBFQqFdjtdn19Os3y+Txmsxk2m40cDWQN8WcLhUIS4AijpshYLpcl2rAynNdBv9+H3+9Ht9vF/v6+XGu1Wg0vXrzQe8hI43K5hN/vh9vtFseFcGG6ZviesjGLgpTT6US/38f+/j6+/e1vS+x9//33BS+nw2QwGCiKwxpzVrI3m01VgPM8ojDKRrFwOIxisahr2OVyyRFGgdThcKBWq4kPtlwucXh4CLfbLUBwq9WSC4YQZEYl+V7z/kXHHIUNv9+PWu0aLEluFx18DodDjB/yYq6urm7VkZMFFo/H1UCXTCYxnU5RrVZRr9eRyWQwGAyQTqex2Wxwfn4Ok8kEn88HABKFKR52u13Y7XZEIhHU63U5hci8AaAKc94neX2zTvztt9+Gy+XCnTt3bkXOGC99/vy5HDX8d2WxWMipk0gk8OzZMwyHQxweHqLVasmdxO/NKnhee41G40f4p3U729nOdn72ZhVy40/Gv/1ZH8anNqujU9z5c0cw/7G+Pvb+0luw/r9/dl6Dz+2sVjBmC2ycNsBuA4yfkpqi7XzoZP/uJazD6//fPAVclQ1wcgEASPyDkx/4+8YK8F3MsH7nmT6+KE4czksLOVwQWFNM+CyXnOVyiU6ng0wmo7YZwnO54BOOy8pXumMCgQAGg8GtyAldDRQq2EA0nU4FB+WiTYGB1bbkNwQCAZyfn+sp9P379+F0OhX1GgwGYn7Q5k8IK6tyeXxsk+p0OrL+M2pFh0Sz2YTb7Ua/35e4tbu7q6/HJ81saVqtVnIycBllnIVOm3K5LB4E3T5ms1nCAUHLFFjIXuHCThcKBRc2fdGBQF4P2SFnZ2dwu93IZDKClno8HsVnGFci6JXVx5lMBuFwWM4oRkFYucxK7tPTUyQSCdhsNty9exeVSkXLbr/fR6VSwcHBAZxOJ3w+n6JIrJAOBAKIRCJa8NbrtWrlCUcOhULicDx9+lTNThR0VquVILhsQCM8ezabweVyqQbdZrMhk8ng6OgI9XodOzs7ciFZrVa43W4sFgsEg0GcnZ1hNBrh61//ujgzAG7xTxaLBZxOJ8rlMnK5nBq4jo+Pkc/nJcgwUkiGEKukbzbA0XHicrngcrl0rtDJ5XK5EAwGxYMJBoO6Nim0ANcAX/Jfbrp36FCiQyWVSqk1KZlMyslkNpvFyZnP56p6psg7Go1gt9sVeXE4HGq9onhHUZFsI7pr2J5EnstwOJQzhmBn1lFXq1V4vV50u13E43G5zlhvTfGOS3+pVMLh4aEYLj6fT1wUCs50BVHA5blVKBRUp06HE+8htVpNrB6KSWQAUWjyer2oVCoAoPjPzagqo6z82nQm0VlYqVQEiweuxRoCsQneJiiY4gx/jul0Ktg573NkK9lsNgl8vL7IhWo0GgiHwwLJ9/t92O12OcN8Pp/u4zabTY4tutYcDofOGUKXKX6SLcbPG41GGI1G6HQ6iMfjcnk1m01kMhm5dej283q94hKxUp0tgrx3TadTLBYLxGIx2Gw2VKtVnaObzQZut1uCFuOG8/kc2Wz2x/13djvb2c52tvOSc8ddx+hPfvWzPoyXnvVohFW/r4+f5CK4+YVXMfuDP6cP84O7P7Hv9YUekxnreAiot4DV5tqVY/7paCnazofPslhC5h+XkPrNKbL/dQvh/9sbWE+nAIB1t4fUb05hb3/v78e/PYf5v3vrsznYT2heWsjx+/0IBAI4Pj4GANXVtlot+Hw+xWsIU3306JGadwqFgiqhDcMQIDgUCukXbovFglAoBOA61sSnqWazGQ8ePMDFxYW4GHSzpFIpQT7pjqAI4nQ6cXR0JIimxWJBuVyWa4cf/X4fd+7cwdXVlaCkXBq57BKeyWWPMFnCPykqcekhR4bxGoouhG9SSGAtOnkqbC/i03AycxinuXnc0+lUvI1Op4NsNqsn706nU6BfxlksFouWrWfPnkkMsNvtt4Qfv9+PXq+HyWQi0WE0GqFWq6kZKBKJIBwO4/nz5+JlsAI8mUzC6/WqSYfcHLJV6N5oNpuKR61WKzlI9vb2cHJyotYltuHQMREKhTCfzwWOZbsPK7A7nQ4Mw9C5GYvF4HQ68fz5czx8+FAsEbJvSqWS2BwUgSjM8T0iI4NRNDooNpsNSqUSDg4OcH5+fqtum3whRqTcbje63e6t87PVuq5CTKfT8Hq9qm1mo1AqlcJkMkGlUkEikZBriiBiq9WKdDqtNh86VQjAbrfbciUx0rZYLPDw4UMJZPwcAsLZNsRIGh1iiUQCnU4HjUbj1nlusVhQqVT0HtKpxOtiuVwim81qWR+NRnKTrddrBAIBtFot1UuTO0RhcrPZKFrDRiy2ilFQ8fv94gClUimsViu0221Fscbjsbgs0+lUizrZLLPZDOFwWBFPwnPNZjPK5bLcYbPZDHfu3JGAV61WAVyLmYwnEiJM7hevVcLIKXBTKKDTxOv1wmaz4ezsTJE6tlstl0vs7e3hO9/5DsLhMFKplMT0xWIhgTORSKDf78tdRqGaDh5ytXg/okjrdrv1ORToCZ+nW4mOLAov/X4fwWBQESZWtJvNZjmVeC8bDAbodruCCAeDQQHCGRGjqGq1WuWaAyD3p81mQywWQzAYRLPZxGAwEBB/NBoJ4s7YZyAQQLPZxGKx0GsymUzg9/sVpaNoyugXWT43W+UoMG9nO9vZznY+ndm111H92tY9cWsMA+vf8xpKv8eN+utWfRT+UATNP/f1rdvkI8bYbIDecAs7/hmZ5fklzL/xFlbvv7j13zeLOcy/8RaSf/sZdn/tDLu/dgbHm2cwB4P6ML6AUfqXFnLW67WeLkejUfFlAEjkiMViAK7dO8fHx+j3+0in0zCZTPqlu1AoYG9vD5VKRayCFy9eaBHiQkO+C5d+i8WCYrGIfD6vpY1RK7pZer0elsulFmha9tvtthb958+fy1FDgaFarcLv98Pr9d56Ik7w8nA4vBUfurkws/WG1dXVahXVahVf+tKX5Hh58eKFam3JYmCzF+GyjNo0Gg0sl0vk83lUKhWJAFarVVwUn88n0C1FLPJ4qtWqgMV2ux3RaBTz+VyLMAUvVmHfdPuQMeP1eiVe0Ilw//59vQ/kWFDYSyaTekL/7Nkz2O12hMNhORD4vW02m+JyTqcTJycncLlceP3113F2dp2pptjG1qtIJCJHVjwel1OAQhuBswSecvFiGw9bp1hNTYYOF2vDMNBoNPDw4UPUarVbjVur1UoiI19zRmLoInO5XOKqFItF3Lt3Tz+Dy+VSTOOm+BeLxcSdoQOM7yPbw3heRiIRxQrZTkQWC4W/VCol9ky321VbEK8Jk8l0CwR7dHQkzlUikVCEhs4nMl8GgwFyuZzcD8PhUG4INoctFgskEglEIhH0+31FWcgmmUwmAhAzFsWqaLvdjrffflvvMZ1lN8UDRmzYmsYYJB1JrAwHIDE0lUrJhULnFsW7YDCoRjIu6hQlXS4XfD6fYLjNZlMCI8UdVrxTgAGuhTeKAmSvUDSlW8RiseD09FTXFcU+RjbfeustjMdjHBwcwDAMnW90+ZRKJXz5y1/WeRkMBiWUARDgnJwmvkeGYUg0Z9uT3W6XSMcY4U3w9ttvvw2bzYZ8Po9ut4tOp4NutytWzsHBgVqeKAJGIhHdj7xer4TndruNzWaDBw8eyAVYLBaxXq/VekZXVTQaRa1WE58qFAoJaE7BzeVyCfhMIZqR3IuLC7no2P7G957XEVvBeA27XC7UajXs7+/ruuX9/GbF/Ha2s53tbGc7n9WYY1GUftGJzfcVeq3swDRswHz/4LM5sM/5GB43MF9gtY1Ib+e7s+p0sKxUAYcdxf/xfVz8yqE+en/iNbVcfVHmY8GOCcW0WCywWq0YjUaKa5DTMZvNxL0xmUwoFAoIBAKCk5LdEI1GZbVPJpNoNBoSEiiiNJtNMVxCoRCi0SiePHmCeDyOeDyOer2up7zj8Rh+v1+1zYPBQOBPOoEYHVmv12i322rYYrsUq9AZAxiNRohEInC73bi8vEQmkxG4lbEPLoSMl7Ht6OjoSLDRw8NDCT8mkwnNZlOCzmKx0HJhNpsRiUTQbDYVY6JgwMWOSwpdP5FIBI1GA6FQCIPBQE//CcZl5Iw142QD0X1AFwgjccPhUFEXNh6RL5JKpXB5eYl2+9qXxsgLnUx7e3sC5r777rtYrVbweDxiEjHeFAqFxOZgXIaNRNFoFJVKBa1WS9+fC9zNZq2bANTFYqFYUTabFfh1OBxq8Wc9N7k6/H50KdCZs1gscHl5ifv378sh1u/3dbzBYPCWIETIM3ANbWZchgJCLBbT+0CINyM+fN3o2qDbaDAYyJEUi8UwGAxQqVSQyWS0uM9mM4k4rVZLEZF4PC52SjgcVl0zI0eBQADdbldMJy7cdF8RdE2+FEWPbDaLxWIhVgvb0cgmYlwRuBb/2BBnt9sxHo9Rr9d1jbpcLjF6CANmjM5kMokpxfOCzhrySyaTiQQ2uuXMZjNSqRTK5TKWy6VE1F6vh729PQC4FQ1imxOdf4xwslWO0OXpdIp0Oi1uTKVSwXK5xMXFBfL5vMQIirB0Dfl8PrGRACAQCEiApIBst9sV+3z99dcxHF4Hexn75PUWjUbFRCoWi3KWML5J3pXFYtH1QZbRZDJRvJECJlvqblahk8d1dHSEBw8eKGrFD7KuxuOxANqMINK1R26P1WpFtVpFPB5HNBpV7JSAdgpbBFN3u1243W54vV64XC5cXl6qrY38GrbvjUYjNe+x9YsONQpJdF+NRiP0ej25fyiI0oEFXEPVnU4nms2m7jf8d2k4HMottp3tbGc72/l05q1RHgd/a/DD/+LP0HR+796H/tnGAnS+FP4Uj+bzP5Z0EsZoio3Nio3dBnPA/1kf0nY+R2NJJlD8I2ksXbf/e2/fhN4f/dJnckw/6ry0kBMMBlGv12GxWGTLJ8CYT2bJpeACHYvFZGmPRCJaVLz/f/b+PMiyxMzuw87b933fc8/qqt7QAAhghsMgOSRHNE1JJEMzQUo0peBIDko05XBMhEVZCluSrYUKybJsBs1QWJYYHskj0aRIjakRLXJMagjAQA+Arq4lKysrM9++7/vuP7LPQeagARTQ6K4G8L6IjBl0VmXefO/eW/mde87veDzYbrdot9tYrVaw2+16mk1eDFkUhUIB6XRagofb7cbV1ZVaeVhfS7ZFMplEvV7XckU+x3A41ALPhbXX62EwGKgKlyyZTqeD6+trHB0dKQoxGo3kxgFuhC26bMhzYTUxn14TOtvpdNS+xBpjxh1msxlCoRA6nQ4Gg4Gqim8DV9m8wyYvVulySWKsigJHq9XCYrFALpeTm2W1WomDw4YfijjhcBjT6VQxJz65J8uI8atyuYxIJCImEdkel5eXCIVCGAwG8Hq9iMfjqgJngxUFimQyKehpPB7HZDJBoVBQVIrRjtFoJFHmyZMn2NvbU4yMIhi5GPF4XO1P1WpV7A+v1wuHwyGALV0nBoNB4h/FHIorbrdbLhm+bvy52bJDflM4HEa329USzIgaBaHZbKbziT8L24Nuc5TK5TIymYyifkajEZFIBM1mE/l8HqlUCgDkEqAby+Fw6OfZ29sT94Mcp2KxiJ/92Z+ViEY2SjAY1NeiIGs0GhVJubq6khvtNnzXbrdLxOHn6P6im4jX49OnT+H3+yUkxGIxLe6BQADtdlvRLwAolUrI5XJa0Hu9nq4Bl8slx9LR0ZHO46urK7mPGD+kW4/wWrpzGNF6/vw5Hjx4IMGInCHyqRi3o7i12Wx033O73RKHPR4Pnjx5AoPBgEgkotapUqmETCaDYrEIt9utGBN5Mk6nE51OB8FgEE6nU+1PFHkYUVutVnJH8TWkc4qfo0uQsT2n0ymAOyNC5POUy2Wd18vlEslkUi1NdP3wHnp9fY3j42OJb3T13Xa6vfbaayiVSvD5fIq88r42Ho/VKNfr9ZBMJuF2u/H+++8jEAjoPAAg0YyiP2Om6/Uaw+FQrDC+hrlcTg1xDofjDig9EAhIQH348CFSqZQih4z2kYUF3PBvRqORWswoBDG+SsYQhevd7GY3u9nN9x7TYIbfaL/xkb5Ga+7G9ps/Pq0xn8RMYi+9ru0GwNZhA7ZbbGtNbB4/+8g18MY378Hwuddh+MyDH9ER7uZVzuIogaX3wz83TBthikU/2QP6CPPSdwbGTAaDAcbjsWqOJ5MJ9vb2tJTWajUsl0u0222s12tFQPgklmwLAi+5aNB5QlcNm2HofOEv6Fzs+ISXQspqtcJ0OhWfgTXfjJAQ8mswGGC1WhEMBtFoNLBcLsXyYbsPBZlEIoFyuYzVaoV0Oo2LiwvZ9NlKRNAqW3MoCLGymCyVWCwGAKpvZ9Xu3t4ejEYj8vm8HB58Gk7YKXk1p6enGA6HGAwGWkxYB85adDZacZG1WCxytZjNZolpk8lEvCA+zQ4EAthutxJLEomEnmozXkQxgAvdfD5HIpHQMRGUS+EvkUig3+8jm82KA7PZbPD06VM5jsxmM8LhMMrlMtxutxw7vV4PzWYTn/vc59But8VkuV01bzKZVFvNVqharSZeEBdwvobZbBZnZ2dykhEeOxgMxDYZDAZyK6zXawSDQVQqFS3NLpcLbrdb4h7jahTDuHTTfRGLxXRO0bnCJZuxwGq1ikgkooYj4NtLLp1qfN0pAJTLZTgcDiQSCRQKBYRCIQyHQ0SjUQkVDx8+FHsmGAzK6bDd3tTw0X01Go0kXjmdTl2rAOSKA6BGotVqhVarpaU6EAiIk1Wr1cRT4iLN5fzw8BCdTkdNdBQ7ubjTvcfYUT6fx3q9lojaaDSw2WxgMBiQSCQUUbvdxsV7EWNEdGgNBgNEIhEJuLfblPgzrddr5HI5XWe8x1CoYzsf4e9ms1m8H5fLhXA4LAGCwh6FDjKY6ISr1+uIx+OCJvM+B0BA4VarJWA1v4bP59P5aDAYsL+/D4vFgkajgUqlgrfeegv1eh3BYBDBYBDtdhvHx8eo1+sSkykcUXChAE9n3osXL7BYLBCPx3UeUECz2Wx631utloDCjARSXOP1ctv1xXOV9yzC77PZrBqkvF6vYNsUWOn06fV6EtgGg4Fa2Rj5dDgcind1Oh3E43GYTCY0m03EYjHBqHO5nFrq6M4i12o8HkvcIch8N7vZzW528/1n/eQc3/rvXvtIX+Nbf+v+j+hofnIm/PDHq0nnlU+jhfXFFTbD4U0V+UeYzc99Bmf/ohfnf96GZ/9zFwxm8/f/S7v59I7RhOrPOr7rp9cOYPTFvU/ueD7ivLSQY7FY4Ha79cu61+vFYDCQZZ+V04lEQtXb/X4fm81GbBO6QSg4UIRhdIWtKWTQFItFJJNJLaiNRgMABEvNZrOIRCJa8lkVS14EADF3xuOx4L8UhLjQEprKqACFl0KhIFdKOp0WY6Fer8Pn891pwaEDiIIR66P5dL1QKCAajWoRm81meipNMaxarUo84MLd6XQkJJ2fnyORSACAatcpvnCpt9vtWlb5+t92AgDQskhxZjQaIZ/PYzqdIpVK3eHqdLtdJBIJeL1exbQymQzee+89LJdLnJycCBZKIcZkMqmGOp/Py7nBxXU0GiEYDAoIS0Apa63prCDDhBXu/X5fkR2KJKyj57JO9gVfOwpPXq8XVqsV1WoVqVRKT/rJVSHjhbwNCnx0OsXjcbVuuVwuxONxcUgIFs5kMnJJEeq62Wx0DXEhJocIgF4fAqkZCVsulwiFQliv18jn83jrrbd0rvAcYZyHPxujXOQI8b0gaLtUKmG5XMLlcqmJiMt8JpNRLTzFKl5DbKPr9/t3oiqpVErXInkrvIYY+ev1ehIUbDYbisWi+EqsLF+tVjg8PMRgMMCjR4/g8XgkqDBOeDuGxlYpRtBmHxDprVar3Eq9Xk/RqOFwqDYvusHInVosFliv14oUWiwWRTANBgNSqZSq23n/orOPcGi73Y5SqaT3m+DkSqUCo9EokYcOQUafeF+lE4vwdkLQr6+v4fF40Gg0xOjp92+eKvl8PonVfE9Xq5WEpGw2i6urK1XGExxNF1y9XlfUlW4mQuuj0SguLi5w//59NZ6Rd8UGqlKppH8D/H4/QqEQxuMxAEgkrNVqcjtdX1/jtddew2azwWw20/nK95YQbQrCiURCwGuKcfy3IhQK6f5H9+dsNkOz2cRisUA0GoXFYhGXim4rnuvdbhej0Ujunm63K1cP2Up071GA2s1udrOb3bzc7P3V5/h3H/1jP9TfPZsmkf0btR/xEf34j+3hNUzfRcsxLoHAbzz78E/+lM5HdeDcnlHGBlg23/8P7ubHY7YbmCff4/MbwDz98Xm/X1rIoRhAPg6XTT4RpgOn3+8jHo/fiYHQ4bHZbFQhzifO2+1WIg1bSdiCkslksNlscHl5iUgkosgNAcMABNvlEsFGG7Jiut2uBIbj42PFd3q9nrgnXIo7nQ4qlQpisRiWyyUODw8F+GX7EheCfD6PQCAgaGulUlFNczAYVFsWn8673W61FlFIIoPhNviUYF5WItNtxCppLpzdbhcul0vOFwJiGVWi2+D2wkmmCBk7/LM+nw8nJyeYTqeo1+uoVCpwOByo1WrIZrPi0TB+MJvNcHBwgHK5DIvFolhGKBRCvV6Hx+NRTCSXy6Hf76uNqNFoaPELBoNa7NkIxoptg8EgZxHdHj6fT1Bfo9GIYDCo9z8UCuH58+dqxeJCC9ws+BQzCDi+Xb9NRxVBud/61rfkErHb7XKLse2GwsF0OpVjhwJkrVbDdDpFPB7Xkm+xWFCv15FKpbScOp1OiWSsSX769CkqlQpee+3maRbPC7fbjWKxiO12qzad4XCI/f198YFisZgEL8ZZKGxEo9E7DhHCrm/XkvN7TSYT9Pt9tNttcZUymQzOzs4QiUQAQIJAvV7HcDhUnXoul8N2u8Xh4SEuLi7U5tbr9TAajRTdoUBEcYANR3y9w+GwBFJGdegU4fe6fc3QTQLciLwUisjromBAHg65WKyr93g8SCaTiogyykWBksficDh0bvN1oCDG7wvcCBjk2kSjUbRaLeTzecF7CZ6+DYEmBDkcDqNUKmGxWMh5aDQa8dprr8Fms6FcLsuRxBggX+tcLodSqYTJZCKRcX9/X8LLYDBAu91GOp1WgxfFztPTU8xmMzGC0uk06vW67kmMoPFeYLPZYLfbEQgEUK/XsVqt5Ehku9X9+/dRLpfR7XYlwF5eXuLzn/+83ETD4VAC821nDwUZni/kGlE8ul1BT+cihXHgRqRut9tyU90WHE9PT9V2ReddOBxGPp/Xn6fQRRFsN7vZzW5283KzbjaR++fy+Hfe/8M/0N/7H/un+Mq/8ruwfn75MR3Zd47BZsPmd78NU/jTzZhZt9rI/Dd1mMd3/7u9uUX44RLrbvfOfzd85gE2v/ttbH732zC+vXM4fZTx/hdfRejLN+5l75kZ281Hc/js5hXPdovk36l+109bxoDl7777CR7QR5uXFnIGg4GEAABqmIlEIuh0Omg0GjCZTAC+DcFlDKndbstmzyf7hA5zGWbUim1KbrdbjpBIJCLnBONY/GU7m82i+8EN7HacyOPxaAFwuVxwOBwol8sYj8eKvthsNkEx7Xa7WnW4mLA16faTby7f0WgUg8FAC04gEECxWJRzIJPJYDqdYjweSxRYr9fodrtyVCQSCT0FJjyVjBAubdPpVEBZijF0HJFVk0wm4fV60Wq1MBgMxD4xm81qhGGMhQ4l1jMDUKTldgzttrDC2mmfz3fn72WzWYkIbDNjywwh1+SThEIhRVjIkOn1etjb2xMMlbBTNtqwyp5P3+kKMxqNMJlMOufG4zHW6zUODg4EtD07O0M2m1UUkC09dDSxAYpxCpPJBJ/Ph3K5jGQyicFgIAbS9fW1WtTYFERuCpvaKAr1ej0sFgu5euLxOJ49e4ZAIKB4EqNSfJ0Zp/P5fBLjgBvnDiNVHo9HkSNCgG/Xc9PZVKlUsLe3p9gLry+PxyNBiuwZxs4YH7TZbHIKdTodgZE3m43guR6PB9Vq9U49PQWTWq2G4+Nj9Ho9ZLNZMZhWqxUCgYBgvYQZ82dhcxCFUjrJGIWz2+24uLjQewcA7XZbrCuv1wuXyyVnFh2Cw+FQ8Foeo9frRalU0jVEYY8xHIvFgnK5rAr7Vqul84sRQsKreTzT6RSr1QqpVEriE50zi8UCR0dHiuJNp1NcXl4iHA4jl8upIpvCTbFYVJSNTXp0N7bbbezv7+ueMp1O5UpJJpMCvRsMBjx//lzXHCNHFHsJg+a1RYcT2Ue8/lgh73K5JGRarVakUikUi0WJXeRo0QHDBjl+LTLICoUCYrEYhsOh3C683/D8ZpwqEAigVCqJ++Tz+eTuMRqNek/o7BmNRuKmTadTwZSDwaA4PxSMvF4vKpWKODu9Xg+9Xg8WiwV7e3tynprNZgndu9nNbnazm5efzXCIg1/p4d95/w9j831WjdnWiqt5FJd/8R6s//0nuEB98U20/pl3UP59TtT/xMmdthqDxXrn44cZo91+5+OjVoSvz18g8/8qYu+vN/UR/5svYPs7X7/7B3/XGyj9gg/l3+dE+fc5UfzH/DB8/qNxi37aJ/Kr38TJX14g9Z8+Ajbr7/8XdvOpnm2lDv/5h7huNkDiH/zo3FyfxLy0kDOZTDCbzdDtdtUUwl+YyQHhgknYKsUFm82Go6MjNJtNdDodiQv83OHhITweD2q1b9sp2SaUTqfRbDYVdaAbo9frIZVKoV6vqwaYbh+KBBaLRW00rINmJTNbcdrttmIBhIpS+CkWi2p4AiAhiyBYo9EIp9OpJ/mswKWzgVwaAGpUoogyn8/RbDaRTqcV9+CCSPjn0dGRBDA6oLjUUBioVqs4PT1FvV7HcrmU64OMEP5cdF7UajX4/X6BW8mWefHiBQAIztputxGPx7XUEJbLqAfr5yORiADJFxcXYqswIsSaZvJH6IygQ6vT6eD09BSj0UjRGYp2Xq8Xh4eHaDQaEkEYFaETgk09DocDhUJBbg4+sfd6vej3+1gul+LYEGZKNgbFxuVyib29PYl5BNb6/X4AEPuH1cZ8bRnboYgUjUYFeaZTBbgRNHw+nwS+1WqlKBDByRQBGL+i+EAYdK1WkxOEQsZ6vUan08F6vUY4HIbb7UahUIDL5UIikVDNvc/nQyQSQb/fV5SGziQyfejicDqdqFQqsNls6Ha7ODk5QaVSUbMbnRu3IzJ0ldCFx9dts9lIOHW5XIItEyxL50q1WhUziLwh8nQYb7JYLFgul3IB0uXFGBV5Jk+ePJFLieLUcrlUFIjx0G63i0ajoWNixC+VSilK5Pf74fF48OjRI6RSKTV7TadT1Go1eDweRKNR5PN5Oeg8Ho/OWYpshUJB7/Vms5Goyza7drutuKDD4VAbGN1tt2N1rVZL7VfHx8e4uLgQk4uONLJk6DAkuJ1RMr/fL9GL9xz+XF6vV9dyoVBALpeD3W7HkydPsNlskEql5EBkc5jVapULjEIZIdDFYhGvv/66qswjkYiar6bTqa5nMtQo5NtsNmSzWTx//lxxJ7Yk3j63otGoBP1gMKioKRlDzWZT92+KY5VKBfF4XCLPfD7XOUlum9vtVhPdbnazm93AaMLq938Wq5//LLZfeutVH82nelb5Ivb+9HP8G1/5o/g/vPc/QWER/o4/8x+d/zz+8l/9J/HVL/lh/vu//Ykdm9HpxODAiUnyRlyZRg3o/OJnbj739n0Uf+Vzdz5+UFfL6vd/FtO/Hcfsv43po/9PfwGmB6cf6bhX+SLWT59/+6PeuPN5czqF4h/0YGP59n/bWIDCH/LAnEp+pO/90zyb2Qzbr7+P9Qe/1+3mx3s2kwk8//XXEXq8hruw1Ufu/937sQOtv7SQY7fbtbAD33YL0MbPRSCTyejP8cl3IBBAv9/XIk9xwmAwqAa22WwiGo1iNBppAYrH4+JDMCpw//59FAoFuUUIYQYgAYCtWIxE0O7PxYf1sqz6JluCSxJBqL1eTzXcHo9HT7PZ0DMYDBQh49NoWvTpHqnVaoKGUvzggl6v1wFAUQkunHw6zSfUhNBycd5ut5jNZojFYnC5XIoyOJ1OPZVn5IYxLzofPB4P8vk8ttstptMp9vf35QAhC4PuErqmWIfOWA8AuUcajYYcHoeHh5jP53ofyGehMMLa4fF4jFarBYfDgcPDQ5TLZQyHQ0Xp7Ha7RMFisQifz4dYLKYn/1arFc1mU0tjIBBAq9WCy+WCyWS6I/KxZYptQ6PRSF+TdeisjGeNdavVutOyRG4S66IZw2KsjlEhOmOAGwcbI4hW683THDY/8TygA6xQKGA2m6k1aTwei+NDlhJfezZE1Wo1Lb+ELxMgTIA1XTtWqxV+vx9GoxHValV8IUYg6bJbrVYYDAZYLBbY39/HYDBQNIzOK0K8eT/w+/3iAFGc4vJ9eXmp9jnWzDOaFgqFkM/ncXh4iOFwiMlkoiU+nU7rZ+BryZ+70Wjg9PRUr+/V1RVCoZCcahSD/H6/Xo92u62v43Q61QrX7XYRj8flinM6nTg/P8eDBw8wHA5RKBTUGkXOCt+PUCiEdruNcDgsLpPP55MThsfQ7Xbx+PFjhEIhOBwOiUN0gwAQm2c8HiMQCMjV1u125SpiBIs12nwNKe4Res77McXOcrkMp9OJ4+NjuW7q9brOG1bBFwoF+P1+nJycwGw2o9/vo9fr6Rh5XVFAXq1WcuTR2UWgtcVikcuJoslsNtO1EQwGMZ1O0Ww2MZlM5KBk7T1dgHt7e6jVakgkEgiHwzg7O1N8MxgMSjCnuJzJZFQ/TuGZTj86AzebDZrNJg4ODhQL5c9FDhGdRvV6HaFQSA7E3exmNz+9Y4pFUfpXfwaFf+0LsP3rVTj+9Qp6/9oEpX/1Z1D5lZ/5yG6Ln9TZzGY4+ed+G3u/9BC//u/9XvyHf/kX73zE/2wH8f/oy9hMvhe04kc/hmQM/cO7K9DGBBjfeg3FX/Bja8adj+Iv+GF86+Ugzquf/yy6/8sR3NY5XJaFPsK/nMfz/40DxjfvfRw/0s0YDB++2RmB/hczH9/33c1uftxms4bzb/z/EPjPv6KPzbeevOqj+oHnpYWc1WqF4+NjWCwWFItFLYd0VVgsFiQSCbTbbTW18IkyG18AiFNhsViQTqcFm1yv1xiPx8hkMvrlngszf7kOhUKoVCqIRqOYzWZ6Ak12Bpec6+trZDIZ2eun06kcG/1+H16vF263G5VKBel0GkajEZ1OR00p2WwWk8lEURSz2YzVaqWnzna7XS1HXBbJauh2u6pX3m63Arba7XZUKhWs12tF0siIYOyk2+1iOp1qybLb7XKUEByaydzciPP5vBxBJpNJLU8A1HR0enqKarWqBWuxWCCbzUpIAKD4Ax0Dy+USmUxGcOLpdKrGrWazKeeR0WjU4khB6cmTJ3IXkK9CLkUsFkOr1YLX61V0hM6c0WiESCQiVxEbZehYIZyXrBWTyaRqe0ZAKNbQXUEmC9uhTCYThsOhYinkXqxWKwFvGeux2Wzw+/2C2rKmmqyiSCSCwWCguA6jW2RGRSIRrFYrgcDZIBUKhXB1daVmNEKtGRU0mUxa2svlMhaLhX4mk8mk47ndVMX43G1nWrVaxfHxsVxhZMgwIpLNZjEajfD8+XNdtzabDa1WCycnJ2g0Gmg0GnJU3QYd365kdjgc4r1QDCkWizg8PEStVtNrx2Y6t9sNj8cj3o/VasVgMJCg+sYbb6DZbKLVat0RvIxGI2q1Gg4ODtTY5HK54Pf7EY1GUavVUKlUVBndbrd1jY5GIxwcHMDhcCASieDFixeoVCr6uRaLBXq9niC+5FBNp1OJt7cZSWT5tNttnJycSJxoNBqYz+dIJpNwOp0oFosCuD948AD1eh3b7VYQcAAStCuVCnw+n96/xWKBarUKt9uNTCaje8l8Ppd7hI4kniMEbhMAT64Nm+oo8DLyyu81mUx0PO12G6PRCOPxWOywSCQiAYj/3Wg06j3yeDyYTqew2+0wm816X7fbLZbL5R3HGgXPQqEAAHL5ULRiuxsB5R6PR/fN7XYroZtNWOSPsZa9VCohHo9LqLRYLMhms3jvvfcEMzYYDPB6vXj69Kn+23a7Rb/fRyqVwnA4hMvlUoS32WyKDbWb3ezmp2wMBpiOD1D6iz+D0l8NYf8PXeHw91/BaLi570ddI+z/oStk/sg1Sn/9Pnp/+kuA0fSKD/rTO75f/Spi/+cv3/lYN5uv5Fg210XEvr6E4YOUjGUIhN4foviHA9h8SJJqYwWKfzgA837ue35d0/0TTH6lh6T3w50bp8k6Kv+mAcYP/j37JMf7pPOJf8/d7GY3H++8tJDjcDhwdnYm2z7hx7VaDePxWIwbl8uFVCqF2WwGh8OB8XiMdruNyWSCw8NDRTnIKGEEiE0vBPc6HA5cXV2pAYkAYNYIc0lkZIPV4WRbPH36FEajEcnkjZWw0+lIRHC73Vpg6G4YDoc4PDyU86dUKqlimFXrjLfQiUSRgaBVfl3GVuhUYlyLLiL+3aOjIxQKBRgMBhQKBWQyGZycnKh9xWg0Kg5BxsV8Pkev18PBwQEKhQKGwyHOzs5wcHCAg4MD/ax0m4RCIdUGkwVDfgojRgSXEjpLka7RaChqQIaLw+FAo9HA8fGxKrPr9Try+TwePHiA6+trvPXWW2r8YiRsPB4jEonICZVOp7FcLvH48WNBrskmGY1GyGazAG7icl6vF1/+8pf11B+AFkUu+kajUU/cyU3J5/NyDvA1ZzSH4Fm/349WqyX3FT8P3Di8KOywbYxtZHROzGYzZDIZzGYzuUR6vR5WqxUSiYRcNoQ6U3CKRqOYTqeoVquq9N5ut8jn8zg6OsJ2u0WtVhMnptVqKQK4XC61RNtsNtTrdbl4uFTXajUJJ6VSCW63W7XK1WpVx8aFn21Ajx49UqwmHA5LiLsN6WWEymg0otlsolqtwufz4a233sJsNkOlUsFsNkMymZSTZDqdSshkjInxvlarhXQ6jUajIdGMPBb+3C6XC5eXl7BYLHeYJY1GQxBens+NRgOhUAherxcmkwnlclmunHg8rkrt+XyOcrmMQCCAUCiE6XQqbszteCAFXN5/KBDXajVFfCjS5HI5NJtNuQStVivK5TLC4bCEbQprdBox6sVzkY1lvEfRsZTJZASoJk9sNpvB6XTC7Xartnu73apqnNFHvhb5fB5WqxVOp1NV5Lwn3G7Lo/hGkd7j8cBgMMDlcqnGfTAYiI1DnhWvdb/fj3A4jEqlImZUrVaD0+m8AxHm9WS1WjEajRAOh+H3+2E2mzEcDhULZRMgHwK0Wi2BwiORCGq1Gtbrtd4HOvcuLy/h8/nEtqKrjwLSbYGMjYq89gOBACKRCJqvaNHYzW5282pn+k98Huv/6wL7v3CFrL/3Xf+c0bDFfqiDyJ+9Rv9Pff6TO8Dd/NCzXa1g/Y2vI/GVGSLfXCH9a5cwDmd3Ikm/czYWYGv+3kJd+Q+GEXJ8b3dR3DPU7wif5BiGn6zraTe72c3HPy8t5DAOEI1Gsd1uBYDlgsJGkVqthvl8rkUpEokIdMlmEZfLJXYN63bpwGBEhMBjCgFGo1FPoykE0eXACALjXYysEKJL4CX5HFyQyaOJx+PY29tDp9MR0Jisjslkotpvj8cDo9GouBfFDf4sPCbGVfhEmfEkgjPZUsPoDQAkEgk8evRIDoVQKCS3kcViwWAwEDCZyzrjPel0WnGr2WyGxWKBUCiE5XKJr33ta/D7/YjH4wAg9wEjW6vVSsBPHtuTJ0/kvuB753K5cHp6qqhZrVaDw+FANBqF2+1WbIVxCb52Pp9P/BOCYPP5PCKRiJxRjMARaky3DVkxhLmSqTSZTLDZbHB0dISrq6s7QGsuuRaLRfEhVqsDEDiaxxMKheS+mUwmKJfLeOedd3B9fa02J4qHy+USgUBA3JZOpyMHiM1mU5U7W6lMJhOm06l4JORutFotTCYTpNNpOJ1OiXUUpthudlsAHI1GePHihdxEm80GVqtVLKhgMCiILqM6BPuy3YlRE3KPCKFmO1m5XBbzirXhFGYSiQS22+2danlGv9jAViwW5YoxGo0SQ9frNWw2GwwGw524TjabRbvdxmAwgNvtViSGleJWq/WOuEiu1WazUUyTMSBCslkdTRgyobh0M1E8vl0NPxqN0Gq1EA6H5TQjtJyxQ4/Hg1arhWw2i36/LwGA0GOKQHQKlctlVdb7/X61SR0fH+s6pXOJn6fQNxqNFI2iuMVYFWHajGnZ7XY5VAiIJg+L96TZbIZarYZQKIRwOKzrZ71e65wrlUqCZDO2x0YqxsGCwaBcZhR36NTiOUroc6VS0ftJzgzfT7J46CJMp9MYj8dyp/HYGCWs1+sIh8NyJ7L9kPcHiu9870wmE9rtto6VDx7IKbv9b8VwOMR2u5WriT8XAdSM5u5mN7v56RqD2YzSHzTAYvrBwKbmf6aBwZ/64i5q9WMypt/8Buy//jWsqj+CynODAaafb3//72ncIP8vvf7Rv9+HzLrRROjxGobVrcNaAaFHa6ybrY/le+5mN7t5dfPSQg4dIhRkotEo/H6/nljzibLD4UCn00G328XBwcGdpXU6naLRaOCtt97C+++/L34GGS+MIc3ncz0lZjVsp9OB3W5HuVwWM4cOg/F4rOOz2WxyCDFiMBwO7yyQXABpoafLhiLHeDzG4eGhnD+Hh4e4vr5WbTmbXOi+Wa1W2G63aLVacDqdePDggQQVWvNns5nAuaw0Hw6HenJMRg6dQ+VyGf1+XxXtBwcHgrWyRYZOHXI7yLiJxWIoFArYbDb44he/CJPJhOvra8xmM7kz2BzE5hsuwdVqFX6/H36/H16vF+FwGHt7e5jNZnj06BEmk4kq1BkPq1QqSCaTyOfz+NKXvoT33nsPiURCglixWFRdOOvm6Vwhh2cymSCbzWK73aoZKJlMCkgciUQQiUTUdMP6bFZrM6bDJqTJZKJYEmN65HU0Gg1ViTcaDSwWC/1cgUBA5+Zms1F8jiIkRTq6lp49eyb3EN8fQpan0yn6/b7gxgcHB2I30TlBMY4LI9/TxWKBTCaDJ0+eSKjhstztdsWoWa1WYqX4/X61izUaDUXObDabwLfkrzBm1+/35Q6iW4iOGgqrHo8H9XpdTB1Wmq/Xa72XZOPQ+TUcDtXsxvvC1dWVzi3Gc/r9vhwydHAxhhQIBCQ6LhYLuSTIs2FFOh0+zWYTbrdbUTy6fkKhEGKxGJ48eaKIz9nZGex2Ow4ODtSaBUA18Pz+vI5TqRQikQgePXqESCQikZoAeAp3FD74tO3k5ATlchk+n08umOFwiPV6jUqlAr/fL2E6Go2i2WzCZrNhNBohnU5jNptJcDUYDDg4OFAMjq8jBTSK0hcXF/B6vYI5UxR/9uwZttstIpGIIOxmsxmVSgUHBwdoNBq4vr7GO++8I+E4k8mIF0OhhT+fwWCQu4fCGV1iZKDVajX0+30YjUbs7++j3++LgcY/y5+HTjzGpQj2pnuO1wodSnRhmc1mRcF4LvLeQ34SRWBeo2w6IyOIjjwKik6nU44nzyuwwO9mN7t5dWN0OlH4i78L9+4Xf+C/67dP4fvlIgy7yucfuzGMp7B8D5atZQAYJrMfyfcyLX4kX+Y7Zjufw/k3v4bsf/wevJcbeC83yP6fvgXnf/M1bD9ACuxmN7v5yZmXFnIajQba7TaSyaSgu0+ePNGS5nK5tLAwynJ1dSXQKdtZuBSQ1cKl2Gaz6Rd0Psnudrviv0SjUdWYs+a33W6j2+3i+PgYANQiRBYDALl2yLdgHGa5XMJkMsFisYiHw3gBgZqM6LBNhYuL3+/XU/lcLodut3uHA8IGqnw+j0AggMlkIscAvy/ZFX6/H7VaTU+MCZll4xYhntfX14Ku8u9z8eXCXa/X5WZZLBZyZ9DBw1jIfD5Hq9VSS1a73UYul5M7hQsbG74Gg4GWauBmAaOoxfafzWYj4Wp/f1+LE5drPv2mC6dUKokFZDKZ4HQ6Fe1oNBpa1AKBwJ02MjolgsGgzgNGiQg4tVgsAj7TXZBIJFAul/U+9ft9JJNJub7Iy6AzjK4Ap9OpxY+NbF6vVwwNuhRYd51MJjGfz3FxcSF3Bq+X8XgscYWMIB7raDSCy+VCOp1GrVbTAn9ycoJqtSoGzv7+PgComtnv9+Py8lLxRB4jgbt0sJjNZi252+1Wzg7gRthkY9ZyuUQsFoPD4cBgMMB0OkU0GoXFYsHDhw9Vtz6dTlGv15FMJlVpzwXdZrPBYrHA4XAoytPtdrG/vw+DwYB2u63WMP7d2448uk8Gg4EEB+CG52QwGO44tlijHQgExG4ia4p8It4TyNa6DQ4mB8jr9apWPpVKwev1olqtyq3BBrhAICAgOGuveX7z+6xWKySTSVitVp3nFBJZ004BjswjCsEU5fh68p7GexEr3ym0WSwWxbvoABuPxxKVyN+ii2Wz2SjiyFa1Wq2GTCYjsdJkMqnhjg5BAIKaM8LF15gCodlsllBIcZ3itdPpFAQ+FouJc1QulwEAqVQKJpNJUdlEIqF7Za/XkzOK14nf71fsi8IYq8x57yB8nuIrxRlezwaDAYPBQMIpHUDVahWbzUbONfKEdrOb3fx0jDEWwcHPf5uF84OO2bjB1Z/w7lw5P2azKpWR/ttlmKbf+TnTFEj/rRJW5cr3/Brb7fd/z9cbI9L/yaMf9jC//2y32IzH8P3qV+H71a/egKS3P9y5vJvd7ObTPS8t5HBB5dPjRqOBvb09Ld+DwUB1uhaLBfF4HBaLRTXk5MpwWRuNRmLJ9Ho9RSr4dJxP330+H7bbrYSK2Wwmrsh2u4XD4dAizQai5XKJBw8eKOZTqVRwdHQkOz4dHOT8sKGF1cKVSkWOjWKxCK/XC6fTKRhsqVQSILherwsG6/V61e5ExgWXDD6lJuOCS5DVakUul0OpVEIkEsHZ2RkACCpM9w0X6729PTQaDbWG3ebf+Hw+VWZ7PB44nU5FFtgcw//NWA0reVmBfnl5iUwmIwGDTot2u61li46O20+sGVEgr4YxpPF4DAByFL148UIiVSAQUDTIbDaj1+vJueRwOFCtVgUZDgaDisawAYhtM4yKUHhIpVJiiLAu/vr6GgcHB4rt2Gw2iRqRSATD4RD1eh1er1eLXyqVwmQyUcSGbBuKSXRl0f1xdHQkFwEX9NvAXjqwJpOJ+DGdTkdCRD6fR6fTweHhIex2O/L5POx2O+x2OyKRiJhFmUwGqVQKxeLN00JyXRh34vvA+maXy6VK9vF4jO12i2w2K+cbHWwmk0n184RGT6dT+Hy+O61ajUZDzXCMwszncyyXSySTSTXUbbdbpNNpDIdDtYcxHsVlerlcSmAlY4YiIv+vy+VS+xl5JbPZDOl0GqPRCNvtVtGdwWAAn8+HeDyu/38wGChOw4YyQrB5nLdZMIxa5XI5cZGKxSIikQharZbg6YvFAm+88QZ6vZ74YAT70v3G94XunYcPH+Lk5AThcFjQ4efPnyv2w6a6y8tLnW+ZTAb1eh3lcllNZT6fD6PRCO12W1wwVp7v7+8r+sivFw6HMRwOYTKZcHV1BbfbjU6ng2g0iuPjYzXZ8dgtFouuKTbJET5P8XY4HGI0GiliSwB4t9uVuEQXHSHY/X5fQliv10M0GsVwOMRwOBQk2ufz3blGCE8mcPrg4ADD4VBRQDYHErjcbrcRiUQQi8XkyKvVahJDc7mczgeK62SZeTweJBIJ1Go1/dvD+OtudrObn45ZpIMf+Wt43/7+EZvdfPpmdZVH9v+Zh62DOx/Z//Iaq+vC9/7L2y1C/8fvH8UtdALYrn+wyN5udrOb3XzYvLSQY7Va8dprr6HdbovVwEgQcLNM7u3tYbVaoVqtKiaSTCZRKBSQSqVQKpX0FPbBgweKWhEc2263MZvNFOtxu91Yr9e4urrS0/tcLqcGFUJDGQ8KBAJqr9lsNndqhdmIFAwG4ff7Ua/XsV6v4Xa7EQwGVUNOMCgbpegEstlsiEQieP/99wWD5c9IvgadJZvNBo8fPxZ7haDm0WiEYDCIXq8nRw/jVB6PR4vNaDRCLBbD9fW1+EEWiwWBQADPnz/HwcGB4iuMZlEQYEzFYrGgWq2q/YcNLG+88YZYHGzu6fV6uLy8hNfrxcnJCd59911kMhmxSwg7ttlscuVst1tFICjm0BGzXC7lWuJ7cRsOTWgpgcV88j4YDO7U13PJpvBGVlA4HFZlOYURtulQaByPx8jlcmIhORwOWCwWLdoEC7Opyu/3a1lNp9PodDpyHFHUo1hFNo7BYECj0cB0OpWoQJaL3W7H9fW1YjiEMddqNQl0FM+SyaQ4IPxvo9EIqVRK9cgGgwHJZFIV35PJRPGvdDqtNjmyoPr9vtriut2u2oeSySRyuRzeffddRRf5vkwmE/GO6LA6ODjAP/pH/wipVErXI7k+bIpjFIXQcbpkyIEaDofIZrPi8sRiMfT7fSwWC/FH2CL23nvvwe/3S2yic2e5XMp1QQcdo3kU/jabDeLxOGq1miKdg8EAsVhMAo3b7QYAnSfk3FA0AKDGq+l0KocSGUkul0vQ3GAwiEKhIEBuMBjEeDxGtVpFLBbT96IwUa/XkclksN1ucX5+LpEqGo2qmY6gXjbFER5MkYnnO0Vwl8sldx/FDsYveU8aj8fiCNntdjXvGY1GiS9W601Nh8FgwGKxwGKxkAjDWnq63+bzOdrt9h13ncPhQDKZ1P2IzWC8jlKplJg+jJTdFlpvR/soLPKaYwyWIiSjUoyzhcNh1Go13UsJyqbo++zZMzidTmSzWYlGFFgJ3KZj9PLyEpvNRvymVCqlNsDd7GY3Px1T+PMffcl2WRdY/MLnfgRHs5tPelalMqJ/+ct3Pr6fE4dje1bB47PvXvP9tBjH/v92js0HDzl3s5vd7OajzEsLOZPJBM1mU09kc7mcLO9msxkHBweKUrGJZjAYyE1AEYOuGAJM4/G4/ixjVrcrvymgEF5JuGev15O7Yjwew+fzIRgM6klztVoFcANUZR0663j5hJfAWrISbrs/6PJgVIo/K0GuXH7ovuDC2Wg0JNCwkeby8hJWqxX7+/vI5/PIZDJaqJ4/fy4ANAWOSCSCy8tL3L9/X3Ez8oSWyyUajQYMBoMWEwpZfJLO5Zk8EDa7DIdDfX+KVqzwjUQiKJVKMBqNyGQyqFarevLe6XQQDAaxWCy0QHORp7gTCoVQLBZhNptRLpeRSCQUP6EYFY1GBUEmC8br9cLn8+Hx48cCwRKCertJxmAwaBFtNBowm81YLBYIBoNwOBxot9vodDqKjzgcDrkoKG4xcsbFjbEmQq/JYpnNZmLv1Go1JBIJRS96vd4dEcZsNsNgMAjCTYCs1WpVRC8UCiESiUhQGQwGmEwm6Ha72Nvbk+gB3IgI5Ht0Oh25qdgWtF6vdV6RPUJRkxE4ungI+qUARrfNYDCQa6nRaACAYlcAVLHe7XbRarUQCATQ7XZVw02mVTabhc1mUyyLIG5WPLvdbrRaLTXVMd5ImHOr1YLP51OzGl0pbLfidUaINuNtvJcQOMzlnvHE2WymaE69XleEkpwuCrN8veie6/V6avICIKGuXq/j+PhYVe4Efq/Xa4krwWAQ7XZbwjEFyMVigW63KwgzBWf+HBSlf8/v+T0YDoc6j/h+326aY9zT7/fDarWi2WwiEAiI4wRAx3GbV0SXmN/vV2vTZDJR9Gg6ncLlcokdxfs3wcV0DvFrU5Ski4XXJO9j/BzvWScnJ1gul2od83q9itNRrOXxUPyn04bXEkX49XqNarUqVhLvb3T/0dlZLBZhsViwWCx0TwiFQnIEsXbe5/MprktBeDqdwmKxKMq7i1btZje7+UHHbZ2j8nPm7/8Hd/NjO+ZUEu1//kv6ML71GlbVGu7/78uY/ltJNMZutCYufbxohnH6b/SwfnL+qg99N7vZzU/I/EDRqtFopOpfPrnlIkwuAZtg+DSa/z+f8na7XaRSKXQ6HTW9vP3226rspSWfkStW6cbjccWoZrMZ7t27h06noyWONdlksjgcDj3td7vd6Pf7mM1mOna6flgBPJvNtFAQ0rrZbDAajeD1eu/ELuhAoLAyHo/VyJNOpwW8pS2fbJp8Po9EIqG41Wq1wunpKZrNpmp/KSQEg0Hk83mEw2FYLBaUSiWs12vFKFhlvt1uVa1L+CfdS3TR0EF1eHgIt9uNSuXmyQIdDev1WgIba+DJDqGLwmazoVwu62n/3t6eGmImkwkKhQJOTk5gMBi0bPGJOXDjSuBiS7Bvt9sVayccDgtgTbHEarXKEUNhiT8ThRjCculK4DlAVlOlUkGj0YDL5YLdbpdzoFKpqGGKXCKySYAbJhQX9EajIcHg4OAA9XpdMR5GlDabjdxkw+EQmUxGoNlSqYR+v49cLie2yGazwf3791EsFtHv95FIJCRgsEKZAgQdNrfjfuPxGPv7+zCbzZhOp2KjAJCgR6cQI5HRaBTdblcuNpfLBZ/PJ1cVXT1symIEjw6HQqGAwWCAt99+Gy6XCxcXFxIMeZ243W6Uy2WJL3zd6RJbr9cYDAY4PDxEJBLBe++9J7cNBTP++evra0GzCZDmOUFxIh6Po1wu63oaDAaIRqOCo7N+m4BxxoQoFHFJp9jbbDZhMpkEiWasEIAE6fl8jlQqpWuk2WzCbrfrNaXYQ7GAkadUKoXhcIiHDx/ijTfekCgai8XuvJYUPHmehUIh9Ho97O/v4/nz5wAgZ9BsNsPFxQXcbrcEwVAoBI/Ho3MylUrJYUTRmbE1AKo4p5hBmHY4HNbPTqYWo3q8Znm/Zkz08vJSX5NQcjqZKPS0221FOvl++Hw+ibPX19cYj8dIJBL6uSiM8cNsNiOdTqs9kPeucrmM1Wolh5zJZFK8l0Lb7Rr1QCAglhEdheTy7O3tyQW0m93sZje72Q3HnMug8Kf2MIkb9JH/owEY376PVakM89/7bQT+6CV8/9MrfWR/6QnWF1ev+tB3s5vd/ATND+TIIT+BzhGKDJ1ORxBZj8eD+XyO4XAIABJ2gsEgnE6nFnx+jk9V2QzCJ8b9fl/RqMPDQzUf8TiazaZqjzOZjOCaNpsNHo8HlUpFnBC2qYTDYbRaLYlJXObT6TTa7TYCgYDcKKwvZzRlf38fRqMR5XJZwFCKT2xxoqum1WohlUrp7y8WC1Xx9vt9NBoN+Hw+uWdYq8soCgGvfG0JETWZTBLUbotXw+EQsVhMfIp0Oo3r62s1ebHNijELwkk7nQ6sVitisZgabpbLpZZu/nk6DCKRiN7Pfr+vhjEu/AAkJnCh5ULKmATfB8aFbtcMM5qzXq8RDofFAqKLinwTCifdbhebzUaRD0Jh2Q60WCxwcnKiJ+tsOONyfXh4iFarhfV6jXQ6jcViIX6I2WzWz+50OsUMaTQaOm8ZuSEQmwJUPB5HpVJRLI9CU7PZxOHhIYbDIS4vLxUVopPF5/MhEokAuBFjstksOp0ODAaDOB0UX7rdrpxAdGY5HA6Bx/P5vK43j8cDn8+HarWK/f19FAoFRdHa7faduBFFUDKcWHVNEC+X/i9/+cuIxWLiujBmZDabVcdNyDIX73K5DIfDgVAohNVqhX6/r1ryQCCAi4sL3SvYJEfHUTablRON4hIFTrZpETrMKAxdYXzdarWa4OZWq1VOv3a7jfv37yuyRMH6NgR7Op3i+fPnOD4+lpjE5jG73S6XGV1Is9lMYga/Fqvs2SrG654NcATr1mo1OJ1OiSSMhPG+QXcahQdyi8iOms/nKBQKiEQiutYoLo1GIyyXSzF7Wq2WwMA8x6fTKUKhkOKbAHDv3j3dz+hipDBOFhcFaMYZGYnsdDqCNTudTgn7FPB4fyP/aLPZiIFGxx+b/NgqxbgaX3d+H8LF2+223KF0Xnq9XsTjcf09ClWBQEAV62zZIwcrEAi87D+Ru9nNbn5C5kem3e404B/bMZ0eYfRPfUEfxt/RXtj/XBLr32nWNAD1L/m+/b836+/82M1udrObH+G8tJDDKm46Kux2O3q9nhqoOp2O4MEGg0H8Dj6V5WJJEYY12QBQr9dhsVgEVO33+7K58xd0tjSRkUNocCqVQqPRkKOGi0E6nUahUEA8HlelLxdtn8+nZc/tdqPZbIrlQ0Cqw+FQu1EqlcL5+TnMZrOOmU+JuahaLBb4fD7U63UcHh7i8ePHEpBWq5XiVaFQCHa7XUsKBQ5WJl9eXgqOm8/nsbe3p4XSarUq2hAOh8Wd8fv9uLi4QCwWExCWTJ1Op4NQKITxeKxlBoDq4hmbIHOC0Fc+dWfMo9PpIB6PK/pGdwzjbzabDdVqFT6fTw1Xq9VKi6DRaMTe3h7Oz8/hdrsVk+p2u7BarWIH3W7sWq1WEgXYgEXHBJdNMlJevHiB09NTxTUYpaK4SKhsLBaTS4pRJZfLhVarBa/XK87TfD4XIJb8GJ63brcb6XQaDx8+FJeH9eqr1QpnZ2fw+XyYTqdIJBJi8dhsNnQ6HYTDYQSDQdTrdezt7QmkzHgLo4XD4VBujNstRjabTYIMANWSZ7NZXF9fY7Va4cGDB/D7/Wg0GhLdptMparUaPv/5z8NoNOLq6kq12GazGcvlEvv7+zg/P0e/30c4HIbH40G5XJbDgbGTw8NDWCwWdLtdOXAYG6MoAECiCR0iFHAo9CQSCTGRgBshl0s8hShyexhbY0U1HSUUdiiy0UFEXtBqtUKlUkEikcB4PMZ8Plf0iIwgQoEJWO90OqjVaojH44hGozCZTLDZbHj48KGEZ8Yz/X6/ok28diiwUFS02+3iWJE7Mx6PFd0cjUaIx+NyODKu5na7BRcPhUJybJXLZXGmvF4vHA6HxMxWq4VkMonhcIhmswm/368oF69FHiuByGR08WuRnxOLxTAcDtHv98XWoQuMx00xjnFUs9kspxoh1hQ6eR5SkKNYNR6PEYvF9HrNZjO5gTKZDNrttu6vFNnpGiO4noBzuhNtNpviWpFIBLVaTcIO/62gm40Oy81mg3v37uHi4gLBYBDz+XxXP76b3fyUzeG/u/rIX6MxduPwPzj7ERzNbj7paf0LX0L+j0fRvWfSR+nPvYHlH9oxj3azm918uualhRxGMuLxuJ5uc8k2mUzIZDJiPPAXdHJT2C5F1wU5FnSeECTKJ6N8Us1frPnLPquq6XhZrVYolUrweDx33CPdblewzPF4rDYZj8ejJ/nn5+eqVQZu4lZcABl1ikQiakficsdqbv6crMSmG4ftS6FQSM4l1jGv12s0Gg3kcjnM53NYLBY8fvwY+/v7imkdHBzg+vpakGQyiVipzGaidrstgCxdSpPJ5E60inGRer2uhZNOBD7lp0OIFciMB3EZoiuDAlGn08Hx8bHEkPl8jn6/r8WfCxUAFItF7O3tqZ2qWq2qop0RCb7nbK+iQMMoCJ+cc+Gi4ysajUrYqlarODo6wmQyUfTNarXC5XLh7OxMMGCPx6O403K5VESt1Wpp4ev3+zof+KSf8YqDgwOxPOiaIHcGgKqOGT+kY4rnIf8sryO73Y6rqysty2R6+Hw+cUjoHqK4QUGnXq8jkUjoNSCwm5X0BMIeHx/D7Xarhh2A/gzhwIPBAO12Ww1TdrsdbrdbdeOz2UznGK+vWCymeOTe3h6azSacTqeiUXz9KHaQc7JerxV34lJP2C0jjORf0X1B5lGtVsO9e/dQKBTg9/tRLpcRj8clzNxuYKNrivEnLvy8pugCIz+Ix8joIB0khBQT/kv3CCvU6abhz0BGjsvlQqlUEleG4iU/1+l0BI4mQJuCRD6fh9/vVxMU/0y5XMb+/r5iYYwXApDDZTKZIJfLScBzu90wGAwS4Pi1GGOk049ClsvlktvLZDIhFAqh3W7rXsaa+/l8LvHXarWK+0MnF+8ruVwOi8UCk8lE/16Qb8TWMEbCKN4yzhmPx3VvACDnF0Vjvu8Wi0XnF6HP0WhUzr3bIg7/LeCDA5/PJ5YQ/80Zj8dqX2PEcze72c1Pzxj7Hx1Eu90asN6B0n/sxvTaMRZ+A7a/A2+0tgGDnAWm0EdvNNvNbnazmx/VvLSQwxYhAkEDgQCePn2K09NTtFotta2wSrzVamlxGAwG4tvQfcCvaTKZ4HK5sFwu0ev1tORzeacDhTBLu90uRgadEGQsWCwWsWq4JJHPQKAtIxtvvPGGaokJZeZSR94DGTlkdGw2G+RyObmBWGHMp84UUa6vryVAscmKi3U2m1VtbrVaRSKRgNPplCsgFArB5XLh8ePHYrxQcOKyRi7HdrtVRbTf70etVhMc1e/3KxISDocVEWMTEuMujFCtVisBrCuViiIjFF4oojACw4gTo0FcoAeDAY6Pj+Uq6na7WhKdTifC4bDOBTJh6CZh9Aq4iW4dHR1hPp/j2bNncj7RGeF2u1EsFlUDT6GGcQ7yQyjO2Ww2PXlnQxKXNwJR5/O5XGCEoNJpZrVaBe4Nh8Mwm81wOByqKvd4PFpsKRQQwkxuDtu2GC2hM2s0GsktAkA8EZ5b0+lUzjcus5PJBABQqVQQDAZhNBpRq9UQiUQE2makjRBYCm3Pnj0TVJbRHq/Xi81mg6dPn+L1119Hv99HNBpFsVjEW2+9hV6vp6rpVquFWq2G/f19TKdTuN1uCSbhcFgRPTo3GC3y+/2CkdP5wcgW2UGMQmWzWQBQ+xf/GyN58/kckUgExWJR4vBtVwVFq1gshnw+j9dffx31eh2r1UqtWxSCM5mMok8UtgKBgJwkVqsVo9FIDBsKhYzqUKxOJBJotVpotVp3oL8mk0kiEZ1Px8fHcnmR2UMAOiOXBLnn83nE43H4fD60Wi0sFguEw2HVxJMnw9er2+0ik8no3OG54nA4kMlkcHl5iV6vh3g8rqYnihl0XtJxNxqN8NprrymWxtc1k8nIVdPtdlV3TnF2u93q+/v9fjks+b0Iiaf7LR6Po16vy0UXCATQarWQTqflPKS7jv928IEAAdoEVyeTSbz33nuCqfP1pDjMexpFWt4PeH0wLkkwOJ1Bu9nNbn46Zl2pofQ39jFf/3Cw4tHCBsd/4v8RZrR280nN+CiAte3DPzeJG4CgHwDg/QcXsAzuft64BBL/ffXjPcDd7GY3u7k1Ly3k8IlpLBYTcyCdTuPy8hKJRAKBQACNRkMOBNZKd7tdnJ6ewuVy4fr6Ws4PCieE+5Jvwae0bEFJJpMC//Z6PbldyOdhMxPBtAT0smWHzT1c1EejERwOB8rlsuIRXPxox+/3+xJ3/H6/IiqMwxAeSujxeDzGcDhEqVSC2+3GO++8ozjLZrNRxS9dE6wPDwaDuLi4EEOCnBGTyYRYLKZlpdPpYLPZIBAISBBhE1Sn01HDEp/SU0BgTbLBYEA8HkcoFMLz589hsVgQj8fx5MkTuFwu1UoTCHt4eKjIFSNlXq8XpVJJVfGMsqRSKYlejL08ffoU/X4fkUhEjBXye+ho8ng8qmvmz8lljT9HtVqF3+8HcBMfGg6HcDqdWK/XGI/HAmLfdlOR/WO321XNfX5+Dq/Xi1AohOVyqepkwoQ7nY5Au2zBcbvdyOfzcLvdGI/HglFTXOGCHwgE0Gw2xXNhA1ilUpFbgULXxcUF3njjDZ1zFO/4OTJ8KDaw6WgwGCCRSKBYLCrSSGfSyckJnj59img0imw2i2azifV6rears7MzOdbYQEUWCqG2sVhMrKHVaiXIOMVUnvt0yFB4NZlMMBgM6Ha7sNlsAlrz2huNRnLl8D1lVI+tSIeHh/jyl7+MN998U04pxitrtRrMZjPu37+PTqeD8XgsYajb7cLr9Qp2azAYJHxQUDk4OEC5XEYsFkOpVJLjiwJDMBhEJBJBPp/Xch+LxfDo0SOMRiNBz5fLpRqPXC6XxA8eEzkqdPgQtszzhWIrhStG7OjSoauH0TW3243Ly0sxkygqADcxVE6z2cT+/r6+DwHdgUAA19fXiMfjd64ltrA9ePDgzn2dbh22tlEYozhJHhTr2VktfvvfBUKuKeTlcjm5sdiM1m63kUwmsVqtJLoGg0HEYjEBxbPZLKbTqaJczWZT9+ZIJCKnEZlrbJibTqdyNVksFkGWt9strq6ukEqlkEwmdR5QtOU9JxAIYDAYyI3pcDgkkN5+zXezm9385M92PkfsP/4yXjxJ/lB/v/frSTj+1td+xEe1m491DAYYPvMA06Dppf74utVG6v9xBv/5Rh/Zv17F6vL64z3O3exmN7u5NS8t5MTjccxmMzx+/FhP9ynIbDYbiRF0QwA3vAsKEsViEfF4XDwF/kJuMBiwv7+Per0u5sp2u0UsFkO73cZsNlOzi91ul0OHdnqybMgVAYBoNCrLPCNXzWYTsVgMBoMB+XxergaCS1nJ6/P5FJkCbtpYyNGYzWYSJBKJhGJXXGitViuSySTy+TwsFougyTabTfXQ/L5cuA8ODmA0GiUw8Ml9JpORs4jV0VxaWf99eHioRaxUKuELX/gCGo2G4ikmkwnhcBir1UrLCEUcAlDpnuDPSoGI7iLWrU8mE1gsFi1x8/lc7ilGkqLRqF674XCIzWaDTCajpinGqfr9PpbLpcC6FO5cLpfcXB6PR4sjIyKsHKbgwKWVSyGjOmyH4kJKfgddBWazWW4Rh8MhwaLf7wvcbDKZ7oC7e70eksnkHSA1I3YUOIAbUCvb3BaLhZwiZrNZbjSfz4cXL16Ix9JutwVedTqdWh4pYo3HY1itVkGBh8Oh3CGBQEDxHwqbdBBQ9AoGgxIUyAdhVIZwWzpmYrEYKpWKGCtOpxOtVksiC5uQQqEQGo2GxNBYLIbz83MJLRR22AjEKA/dJ3Sw2Ww28WMoVjgcDtTrdbmLKA4RjH12dobDw0P9WQLGGfskk4Z8HZ4zdL3wWp3P52g2m4jH4xKQisUicrkcAMhlxBp0cpp4rfBndLlcuLy8lIONwjEjTHTR8TqLRqOKgpXLZV3PBCUTiMyYVqlUUryM4sNwOMTx8TFarZZcMV6vV7FU3pv4WrBNj8IbgfPlchnRaFQOIofDgX6/j81mI4ckmVxsBfN4PIo81ut1rNdreL1eNaDVajWMx2M1xI1GI8WxOp0OFouFIq7dbheNRkOiU6vVQiwWk6hE5hX/DvlmDodDgPnBYIDVanWHeXTv3j2sVivxxXjPIGeIMarhcIhoNIpIJCLxa71eqwmNDKLd7GY3P31z8DeWGC+t2GwNL/XnN1sDXjTDSP69nYvvx23Wv/czKPwRH0bZl3uvAWDd7sDza1/Vx66Raje72c0nPS8t5IxGIxwcHEig4FNz4OZJK5+mEkKay+UEnH3x4gWsVquiBslkEna7HXa7XVBfCjN0Q/T7fQQCAdTrdbUFMcKyXC5Rr9cRDodVP8zvx2WPvAiv14vlcqkmKy62jLb4/X79Yg9AyxqfRjscDrV1sUEnmUyqvYVxH8bIuMQEg0HBb4fDIQwGAyKRCFqtllw+hUJBsSNCUimOTadTlMtlHB4eiutxdnYGu92OVCqFxWKBy8tLRYH29vbwD//hP0Q2m9WizNgT21u63a4WO6fTKU4KHRuz2QwHBwcCLpdKJdy7d0/V8gSimkwmRSzW6zU6nQ5yuRyazaZcBUdHRxiNRoK0lkolxa0Iab0dhyITpdPpiAPU7/fFAmJ7k9lsFuiXIh7rhxeLBVKplFgxi8VCS2atVlMkx+PxCMLM6Fuv10MkEhFn6fb5RsGo3W7r56PIwBY3th0RaM3lPRqNotFooNFoIJ1Oo1qtSnxqt9uIRqMSfOx2u6IjFMsYk6nVanKL8c8Mh0Msl0txXZrNpjgwFEwzmYwcQ81mE9PpFNFoFJPJRPGhXq+HdDqtc5kxRMYpyT2iEEeXET8XiUQkFFIMYIvWbDZTBToAtcuZTCY4HA6Bxtnutr+/j/feew/JZFLsmMlkgs985jPodrti+fR6PfGIxuMx9vb20O125RobjUaK6eRyOQms5MUAUO07rz+KD2TYUJimIEhoMwVXgoWr1ap4Wh6PRxXlFDAYmSSLplKpKLoUj8flFqTri9dspVKRY458Jzao8X1PpVJYLpcIBoPodrtigk0mE7W2PXv2DADg8XgUfzIYDMhmsxgMBmqfYsMfr1E6mBipJRg8GAxiOByKn8X4LBk72+0W9XpdogtdOhRSbDab4riEXM/nc8VaKQrRtcY2NcZgCTHm/YRwe77OL168wHw+F+soGo1KmGq32+h2u7h//z56vZ4ifPl8XlwzRmYdDoecd7vZzW5++sb0m9+A4xcHqP1XOUxXlu/5ZxtjN8b/YRr7fzaP9eNnn9AR7uZHMUaPB71DG/B9NJzw+yusX+Q/mYPazW52s5uXmJcWcvhUm0s/IyF+vx+DwUC2fjo0KpWKgMQUQxjb6Pf74iYAEOsFgJqSGP+ghZ+AVovFglgspjjP8+fPkcvl9ESf9c10VRBySacGm3tY3UuGSCwWU1tKNBrV0+hWqyUwMGNhdLHY7XZks1nBQBlBYS263W6Xc8Xn8+H58+dqpOET5mTyxrrL181isaiFikKF0WiE0WiE3+9HtVrFeDxGKpWCwWBAsViEyWRCp9OR6ELmidlsRrfblTOCx9xqtQDciAUUF8LhMBaLhep4bTYbEokEvva1ryEQCMgVYLfbsbe3p2pio9GIWCwmaCpFsNssl9vNPhRY2BbGp+4+nw9ms1kNW3a7HbPZTIITBYjpdIpnz57h9ddfVysR3TRHR0fiFxHYzHYatmJxuaObgo1mBPry3OYx82skEglUKhWdX0+ePFG0h+e33+9HqVRSdAuABCpCsOPxON59912kUims12s0m807bglW3K/XayQSiTswW0b5KLIQ1ByPxyWKVSoVvXZ8DwAoqmU2m3F5eYlcLofZbIZEIgGfz4ezszO4XC5d4waDQeImBQaz2azrlpEjVq3TzUXB83aNNu8Li8XiTs08nS6MXFH4i8fjEiLJ17q+vsabb76p16jZbGI2m+kYzGazGuNarZYiNovFAsViEQDw4MEDNBoNOeNmsxlOTk5QLpdhMBjkLGQbHo+ZMTLyjuhCTCQS2Gw2aDabyOVyePjwoZrgKNjOZjOxwwBgb28P8/kc7XZbXLF+v692ul6vJ1cW32cKySaTCW63WwIVv47T6US9XkcoFFJLlcvlUoOg3W5Ho9EQ64cuIcYe6bDyer24vLyUi4aCrcvlkgNsPp/jxYsX2NvbQ6PRgNvtlojI+7bH49H5UCqVUK1W4fF4EI1G9Z4Tes34KQHm8/kc2+0WtVoN/X5fItxsNkOtVoPH44HZbBZEmsIjxUi6+XjekiXGhwScwWCAdDotkXO9XsulRQj448ePkclk5AzczW5289M3624Xkb/yFYz+WgqVX9vDi2b4zucfn2VQ+bU9OP+yH/b/9mtYDwbf5Svt5tM66zcOMEl8bxUn8s0V3E87uwrx3exmN5+qeWkhx+v1KjIF3DSIcKkLhUJipvAXbC6pR0dHEmra7bbiAoy8EKzZ7XbR6/VwdHSkZYpCgclkQr/fV8yBi0K320U8HketVhMnYzQa3an+5RNxNm0tFgt86UtfwvX1tcQp2v5p06eDZzKZKAKwXq+1oLMWmQsRIbSJREIQ5ul0ivF4jOVyqWp0v98vngafPne7XZhMJsWnzGaz4Lt0wHApXy6XODo6QrPZxHA4RDabVQMSmR+j0ehOhIvVzZvNBgaDQYKB2WyWM2Oz2aBarYpr4XK5kM/nFRthFIqxlel0ivl8jnQ6LUAvnQC33TUA5O5wu90IhUICTjNmxPcQgLgY5LXw6T5FuXa7jVQqJccHF20uZRcXF4hEIrDZbLi+vlaMgtBrCjPAt1t+uMjSOcMlHYAWOHI2WBtOZghdBozTsMaZUFW6bJbLpWJZxWIRx8fHaLfbaDabyGQymM1mWCwWGI/HEtKSyaSug3a7DY/HI4cEodF0+nzuc59DrVYTOJmRJYqO5NLQncbzlRFHXo+9Xg+ZTEaiJCMudGxQ6OJ7RMcQ283owmEkhe1Uw+EQ7XYbe3t7EiDo+qDzAoCuk9v3GTo42u22IMnkc1HIsFgs+nkIxCU8mmJts9mEwWBAKpVCJBLBo0ePEAgE5OyKRCKKCrKdiTwb3oPoyOF1z6Wf0TC2TG02G4RCIcGbWcsO3LgXF4sF7t27h36/r6gU3zee84w1lstlOBwOXWPtdlsxUwrPdMnQUUSgdr/fR6lUwsHBAVwul9wzjMPxfdxsNnjx4gWi0aggwwB0b7otMFMYur6+htvt1nX5zjvvoNvtol6vw263iwdGwTQUCuH8/Byr1QrRaFRwad6nZrMZAoGAKubpsqIIx7Y9Rm23262cXPP5XMexXC6RSCTEZCOIm61+BoNBr/ntCnKj0YhcLofxeKx4JH/Oo6Ojl/1ncje72c1P6Pj/2lcQ+StfwcH/eoj1X/Dp4/7/Lo/IX/kKbH/n66/6EHfzMU3o0Rqur1wA1QZMXi+MH0STd7Ob3ezmVc9LCzmVSkUcFIoahK4yImW1WtHpdJDJZORs4FP2er2OaDSKN954A7VaTUtgt9uFxWJBMpm8w2fo9/s4PDyUY4IgWVZK0zJPdsXe3h62262iG5PJBL1eT0+Gm80mwuGwhJ3lcolwOCw3Qb/fx3q9xv7+vjgv6/VaiwQ5CoQIBwIBVfn2+30tf4FAAPF4HO12W9EQche47LNZZzweIxKJaJEwGAyo1WpwuVxIJpOYz+col8tyDgSDQYGP1+s1HA6HllE6PshvYeyEP+9gMEAoFEIikcDl5SXW6zXC4bB4GlyeDQaDomqVSgUGgwGhUAjBYBDlchmpVAq1Wk1Pwm/HmyiGRaNRxVy2263iJnQUkF3E18RsNquxp1gsIpFIyI1D9g2bzBjpogjC84rCjsvlEij7NsCVX59CAo+fgl2j0cDh4aEWQPKX6NYCblxThUJB0RWbzSbRhI4HslEYX2HLG50QJpNJTWSxWAzPnz8XVJVLLwWKUCikuBZr6CkutVotGI1GnJyc4Gtf+5piM7FYDK1WS8IeWT6EiJvNZsXQyB0qFApyipRKJbhcLoRCISwWC13/1WoVwWBQTV1HR0dakg0GA6rVKkKhkI51OBxKzCLYut/v65okP4nujGKxiHQ6LVDybDZDv98Xz4ntQzxXnj59ikgkIpbU7fhXqVRSdCyRSOia43vrdDpVq06RkfX22+0Ww+EQJpMJp6enulbpFLq+vsZwOITNZpNTjzFRugkZyTk9PUWpVEKlUoHf75eI0m635XAZjUaqcmerHJvQCKf2er3I5/NyQ5I/xZipxWIR44hxwtVqJRfkbDaTW4bi4Ha7xfvvvw+n04l0Oi0+kN1uRzKZhMFgUEQvn89jb28Pz549U5STjWZ0uTCmxnN0NBop1uhyuXBxcSEoO19f8poYz2PtucVikUPRaDSKxcRYHFlnhUIB4/EY4XBYrkfC4ine8z7C85c8JP7bBAB+vx9GoxHX19dqqLvdOLiDHe9mN7vhrC6vsXl4po9V7dN1fzCFghj+0hcx/KUvYvszb73qw/mJmPbrJlz/i/dw/S+/jut/+XXU/8xbMMWir/qwdrOb3ezm5YWcbDarCIbRaFTdLTkQ5ITQsk8AKgHENttNn1+pVEIoFFKFNqNFbExi/IMLN6HHfCrMBZ0Mg9tPW3u9nmDFt90X5MLQBfLs2TOkUinY7XaxKFKplGISLpcLACR8dDodxR5yudwdngtbkwg0NplMcjkQgsvadQJ82e7Cp+QmkwnxeFwCE1uAGLPgE38KIKwW/8Y3voFMJgOv14v1eq0lnBwdij588s/3hlW9lUpFUNWDgwN0u13Faeh+IguJ7AguWXyf6PQgn8Xn82E4HKLb7cLtduP09BTValUA5NVqhXg8Lu4FhTjyPSKRiN7HyWSiSmg6t1qtlmI+hDVTmAFuljK6vsjToCOH7VN0ArF9KBAISAAhA4bnNn8+1pAbjUa1kDkcjjtxlul0isViIVfIer3WsREuHAgEsFgsMJ1OFRviz1KpVLC3t6daZrpIyOchEyeVSmE0Gun1dzqdMJlM6PV6Eu3IVXI4HHodA4GAxD5ySzqdjjhAhDvP53P4fD5dZ4z3bbdbuRwAKPp4W5DkMTJaRvGTwihdW5vNBsViUU1joVAIw+EQsVgM8/kctVoNR0dHeP78uaJrvCYo/Ho8HlxcXKDRaMgZQxcel/V2uy2eESNSbGnidTAcDnUPI4yZzU8WiwW5XA7X19eYTqdy2kSjUd237t27h+FwqHgORQ26fSiu0HnkcrkwGo0QCAQwnU5xdXWFUCgkDg5bxOhmokB8W9DsdDo6HjZNUbyiwEg3IJ0vfD/55/m/LRaLYNmMgtGlRcclY4ntdlvOFkZcu92uYM0Oh0PCktfrlaATCoXkNiSY/vDwELVaDev1WsfFuGMgEJALipBtXoObzQZmsxmbzQaRSERir91ul/upXq9ju91K5OV1RiFrs9noHjMajeDxePQ+EeTMmvnbguZudrOb3Xwqx2BA9898CWf/5jGCfy6P4J/Lo/IrK1z+u1+Cye971Uf3qR7zeQmOxstXxc/CBtT+2OHOmbOb3ezmlc9LCzmMHGSzWUVdttutGpji8bjqkinkzOdzPf3nU1RCVylejEYjPUmnY8Dr9Qooy+9jtVoxm83Q7Xaxv7+PQCCAq6srgU0bjQaSySSm06lYMjabTTEPPvXmk2s2qOzt7YnXQteC3+8XXJeV5IPBQFBjui2CwaCe4LMBqNvtIhqNotVq6Un+crlEOp3Ger1GtVpFOp1WRIcA2ttP0ZfLJUajEV577TV0u90738dgMGC1WuHs7AwHBwdqY/F4PAK/BoNBnJ2dybXDp/gWiwXtdhtvv/02Hj9+rMptwn4Zu8rlchKFNpuNnm6zhWc2m+H4+Biz2QwvXrwAgDtAXuCGyTKdTgWPjkajcmyRR1KpVHB8fIzxeIzxeCyHRzQaVfyKx0+OChkviURCcSkKRGxIoijBume6xdbrNTwejxqlKMJRZOL5F4/Htdi63W589atfxf7+vhgy2+0WoVBIYhXrtCm6AVDdMzkggUBAbCUu5WzUIStof38fz58/l3hIEYTsIAACOTMeQ94Tr6N4PK5leDAYyJXByFgsFlPTEK9jcokYg7PZbHJb8ZpgvMrv98Pj8eDq6koNSeQUUWCg043iIZvA7Ha7vh8F03q9jm63i2QyKXElk8mIi8MlnoBqOjaAG0DtycmJAL3kP/l8PrWJEeBMhw9dMXRykA/Dr82IJlug+v0+KpUK7t27p7gVhejBYAC73Y7z83O5AhmNo4vIbrdLFO12uxK/2OK1Wq3g9XoxHA4l0LLBz2q1IhgMSgCi06TT6ajinP+dHLDpdConSrPZxGc/+1ndr8gdqtfrWK1WyGaziksRAB2LxfD48WMJRoSVNxoNjEYjhMNhiSW8LxgMBvh8PjXP8XqmSErhhO97JpOB1WpFvV7XOUpHYqfTQTAYFOuJguRqtZK4BECuuNlshsFgALPZjFarBZfLhW63i3feeQfRaFRxLuDbnKnJZCIxlMByfn+eW0+fPsXR0ZFqzHezm93s5lM7RhNa/8IX4fuflXD/QUH/eS/YwekXr/H03z59hQf36Z91q43of/5NuItbmMcv93dmEQMMudTHe2C72c1udvN95qWFHP4Czaf1XLAIw+RC3Wg00Ov1FHsyGAxwuVyoVqsSAgKBgJw6AASnJX+FbSxcFvhEl0sZo08+nw/dbhcAFCEir4SsDj4BpgBC2K3BYFBzCpdTuicWi4Uajm4vYuPxWBESiiOMNpBJYjKZUCqVcHx8jOl0CrvdjuFwiNlsBqvVilwuJ3iv2+1Gu90W74IxBMYTKpUK0um0IMRcPgCo3YfRDsbcGOVhCxjdEwQ4L5fLO04bOlUYHwqFQgKoms1muN1uVCoVfY3z83MEg8E7zU18Uh4IBCRg8DVhTISLptfrxbvvvgufz4eDgwNsNhs5lG5HtehKSiQSWuIuLi7k+iEbh++R3+/XcTHest1uYTQaMRqN1KjDuBcjWhTJKDwBNzGRYDAIr9eLYrEoaCxdXjx3KODwvaM7hWIRgb9kcezt7cnlMRgM1MbFyMxoNJJ4E4lE1FrU6/W0BB8fH2uBZeManQTkGVHk8Xg8cocxakXwLEUFOmV4riaTSTQaDV1b8Xgcm80GLpdL10e320UgELizuNP5QweDx+ORq4RuLl4HAJDL5eSyYGwvHA6jVquhXq8jEAggHA4rykNxgwKI2WzGvXv3UK1WJbzejhPxvkGHGPk1TqcT5XIZTqdTEGHGP91uNxaLhY6HojRdQxSGHQ4HqtWqnCEUhsn+YVSSHKr5fK77EO91bMozGAxq+hqPx7p+/X4/3G43qtWq2Dl0tJBn5Ha7BZunO4VtZ4RIX19fC4zNn4X3Kd7TKci4XC48evQIb7zxho7NbDajWCzC6/XKvUQRjAIrY2AAxLq5Db7udru63thgB0DwaDrWKJIPBgPkcjnxvegU6vf7aqyq1+tIJBJyPbJBjU4dgpAZk2LEjH+eEPr5fC4uFKH1bM3rdDqwWCwSd3azm93s5tM4Jq8bkV8qwGZafejnvckhNj/3mU/4qH68ZjObIfCffQXpX68j+VszJH9rBmv/e/+d6u+PfDIHt5vd7GY332VeWsjhU38uJMDNEhyNRlEoFHB9fY1sNqt2JdYGx2IxjMdjNSvFYjEt2oxi8Qk1K7wzmYyae2ibn06ncgMRqDkej1UPzKgAly3GMBhbYeNPPp+XkyidTisuYjabEQ6H0Ww2sdls4PV6FUsyGo2CpBYKBUFZ6aJYLpf6OSaTiVwKgUBASwRFJtavj8djLcaERm82G8U+4vE4ut0urq6uVJFLeK3RaFSrEeHOsVgMg8FAMSA2DXFBr9frAjJTGGDNLuG25EdQ7KGIQJfN1dUVUqkUnE6nXDCMq/D9ZoNQv9+H2+1GvV7HwcEBhsMhEomEIM/8ORl7oJhycXGBwWCAaDSKeDwuYPR2u8Xh4SEePnwIr9crJk0kElEMj9Xe6/Va52Gz2ZQrhpweRq+8Xq/AqXSF0NnC85NCj8/nwze+8Q14PB74/X4JT5VKRcs3Yx/tdhvxePyOQMSllpEcRhHZLkZWCAUJnq9ms1n/na8HBcTbzVp0apjNZl0rrVZLDjPCiPf29iTcuN1uuYTIEiIgm46QRqOB7XaLXC4Hi8WCQqEgYYtLOwUcChmsmWZ0jFEvOpC63a5e58PDQ3S7XeRyObRaLbWeAYDRaJR4QAbObDaTU4/xpHw+r5a78Xgs5widduv1Gt1uF6+99pqECI/Ho2vT7Xar/Y3tXicnJ+LSsGWOn7darWi1WoqPOZ1ObDYbtFotgZPpSAEgYZZgX4rRFDv8fr/usd/4xjcQiUTEVSKYt9PpwOFwSNgipJiiab/fV2vcm2++ifF4LAHa6XSqMn25XEr0ZotYs9lEKBRCp9PB4eEhLi4uFBej4EHODq+lWq2m2CtbpBhvXa/XsNvtyOVyaudihAuAIrKMsblcLrkyc7mchKlSqSRYOJ07dFQxflUoFBCLxRQ7zefzgjDz+qBjif8u8b4EQEI1I5/8e4zEORwOCV672c1udvOpHMP3/lU+4++h/br9EzqYH+9Zn7+A6Te/AdNvfgPWwfeOW5mnLx/H2s3HMwabDeZ4DI1/6WfQ+PPf/jCdHL7qQ9vNbj6ReWkhp16vC1RL4CrZKXQc8Ok9G1dWqxWq1apamAwGg9wxfPI5GAy0TDidTgFuuSRst1vs7e1pgQWgppter4fxeAyj0ahlkoLJdDrF6empHA8UEAjV9Pl86PV6aoFhlIj1wlw0g8GgYKx8im+z2RAOhzGZTHB+fq4n3Iz0+Hw+OT+cTqeiNFdXV3JpMDJAQCljKRSErq6u4Pf7cf/+fXzta1+D1+uFyWSCwWCQQ4ARCi50jDcA0BP6er2uJ/KECJPRMpvN4PP5FOfgcs/4AXkdVqtV0ScCWumu8Pv9sFgsigzVajXVnRO4zDaoQqGg+nKr1Sp3SDAYRKPRQKfTQTab1XvLJXU4HAoiTIGGjBhG2vx+P8xmMy4uLhAIBAS9JpOGjWaMEdFhRdGC8bJAIKAIULlc1pLNGBjjO2xzyuVyci48ffpUguBqtVKjF4+bcOonT57ovLBYLOh2u3eazUqlkmJ8wA1fyu/34/LyEvv7+wICt9ttNBoNtWn5fD4JZK1WC/P5XOcVrzMAOnfD4TDG4zEmk4mcJOTusLLabrcLuv3666/rPW82m3K0HBwcoNfriXlFwbLVaqHT6eDk5ASj0QhGo1HnF51aRqMRXq8XT548QSAQQC6XU1sSW5oI/h4Oh3J2EbJbKBTUysRjI3zZ7/fLVeNyufD48WNEo1Gk02n0ej0UCgWJChTzotGozh2+t2x7ozuDzo10Og3gJkY3mUyQyWTk2KrX64p4UWzm/YUurvF4jGw2qwYlMoImkwl8Pp9a8ujCux2Rslqt+v6Xl5cSzOfzOb761a/i8PBQrigKG6yDJ0eMsVg2gzFCF41G8fTpU1itVomdbF2rVCowGo06D50fMAIePXqEXC4noYUxUv5di8Wi+x4FuWAwiNPTU+TzebnArq+v1cpFJx3vdR6PR04hn8+HWq0mNyOjt3SO8XXq9Xq6lngOkYc2mUzEHaLYQ8HrtkhJZtpudrOb3exmN5zI33jyqg/hp36WP/s6rv75Q8yDwDzw7Y/8n4jBdLprnNzNT/68tJBDlwXBwa+99hpKpRJarZZ4L6x6puDCxiMCgAlxzeVyArlGo1EMBgMtuwT+zudzRKNRuN1uXFxcqFr79tNhWuv5vQnO5dLFeMN8PhfA9nYDEKNhBIXy6e9wONSTebJhVqsVisUiDg4OVK9OhgWjTIPBQG4Qtg+xGp1P6UOhkGIrjKCUSiX4fD7FZLxer8C7bPNitIRCSiAQkMOp0+koNkHhgvwXOkW4fFLUoeOCsRxGNIrFooQzOptYy04gKDlDhPva7XbxN1iVTnYMWSGtVktCH9vJ+D0mkwlOTk7E+mH8hgs0m6X4cwOQg4CAWvJ5Dg8PBXfO5/NYrVbo9Xrw+/067/g63wbkkodEwXC1WsHv96NWqyGdTosVQ5HhdtvX/v4+VquVILN8T/lnMpmMwNjdbhf379/HaDRSxCcQCCjOMxgMcHR0pNeTsOxYLCYRgc6r2+fZcrlUJCkajSreQ2ZSOBxW0xxwI4Y+efIEPp9P15Tb7ZaQUCwWxWeJRqNwOBx4+PAhwuGwhDtWXDOeR3HJ6XSqArrT6ajS2eVyoVwuK7JHl9J8Pkc8Hhevqd/vw2KxwOfzSZAyGo1Ip9MCWC8WC5RKJYTDYUUtg8EgAEiAajQaEi8ZLWs0GjAYDHA6ndjb28OLFy9gMBhU714qlZBIJCQG0EFDVhPjfPfu3UOtVsNsNtN1ZjQa5XJh/I9iZiRyY8GmYGw0GlEqlXTdMeqUzWbRaDTw4sULgcIdDgey2axcNzzHGo2GnEir1Qqr1QonJydiDbHKu1KpwGKxSJS+DahnZIviPJlebIjj+3m7Pc3v9yvexHseRXM2TY3HYzXY1et1ifQvXrzAYrHA0dERJpMJKpWKWqso0N12wAyHQ4lG7XYb4XAY8/lcUTEAupfSkUkhidcNo5C8f47HYwlFdGYtl0u5e4LBoJq11uu1RMPd7GY3u/lUznbzPT+92Rpg+N5/5NMzBgMmf/wL6P3pL6H3p7+ExS98DnhF9+Dwb9Vg+LC02haIfGuFzeglgTq7+VjGFAqi/cD2oZ/bmoHeZ8Kv7NzZzW4+qXlpIYeQSgI8Hz16JDcFWQkUC2jhZ9Usn9ry6TjroWOxmFwAtyt+6dAZjUZqgGFtL+M52WwW7XZbgk+j0cBwOITX69Vy9/z5c7jdbpjNZjSbTf0dn88Hk8mEYDCo2EI4HEaxWBSYmRygwWCAbrerJqjnz58jFAqp8YmCEH/ZZ+NNr9dTbS75NGx/oRh2cHCAFy9eiNlwenqq6ul4PA673Y58Pg+r1arYD9uiEomEeCEAsFwuUS6XtdixkYuxrVQqhVarpdan5XIpAcTn82EwGMhNQfcInUHdbhfHx8d3Gono8CBwN5lMqsaZzJxkMonZbKbXodfrKeLk8XgEkOVTc6/Xi/Pzc3GV6OCgiNPtdhGJRHS8BPj6fD7EYjG95larFYPBAMFgULGzcrms14U12uv1GpeXl3A4HOLz8Ik9W9QI0HY4HGqnohuH1catVgvFYhH37t2D1WoVvPjo6AjD4RD9fl/vA9t/GO3i6wPcxDxsNpsiUXa7XWDdd999F6FQCPV6Hfv7++KFAJAYd3R0hG63K1BzJpPR9eZyueB2u8VMMRgMcLvd8Hq9iju53W7UajW5kiqViiDmqVRKS/3V1ZVeF7vdrlap0WikaNnz58/F/iFomst2PB6HzWbDcDiUEMh4HgAxZ2q1GjKZDK6vrxGJROBwOMS4obuFgPFwOAyDwYBKpaL31+Fw4Ld+67fw4MEDCawOh0P3M7bQNZtN8ZoIzSUDi69bv98XDJzCDR1TrF2/vLyE1+uVUEDBijwd1onzXhqPx1EoFBCJRASxpjOEwiFZTeVyWW4yMoN47+H1SAYZq7r5OcbneJ+2WCyCCdPhxHPJYrFgOp3qOqdTjBwfxqQYcSPjhzE6v9+Per2Odrst3havQzZdkbvDe77FYtGxEFBdq9XkCHr+/DkymYxYYLcdgD6fTyB0nkNsxfJ6vXC73Xj8+PGdxj1yjiiM0qmWzWYxHA7x4sULZLNZVKtVMY52s5vd7ObTOtvlCuel2Hf9/NVv7iHyf/v6J3hEP9wYnU4Mf+kLaD8wYbhnwHDPgPrnrZj9kc/DcIur+UnN+uIKuf+6CldpC8PNsw7Ym1vE3l3C/utfx3b14Uyi3Xx8Y86kYTo9gun0CKuTDBb+7/5ne4dGGN967RM7tt3s5lXMSws5mUwGjUZDMFlGoGhfZ2POcrmEy+VSVAqAllg+WSfvoFar6cmqz+eDy+VSxCYcDqNer38Hk4UiR6lUQiqVusPNoCjQ7/cRDofF73G5XIhEIigWi7LYM7YRDocVBWKlOcWGeDwuaCiZM2S/0FFAMYExgOl0im63i0wmo6fgrVYLo9FIbg8KPaPRCMfHx4qz0HnEFhs6mGKxGPL5vFwjLpdLi85gMJArye/33wG/csFhbCiRSMBisQjsSjFgOp0iFovh6upKVd25XA5XV1ewWCzY29sTQ4RP9C0Wixpr6Njhz03HBKMOi8VCYkM0Gr3TjFSr1RCNRhWJo5tkPp+j2+0KJkv2is/nQ6VS0TkVDofR6/XUOhUKhRT1o+OKDi06q9gsReh2vV6XyMfz+jZ4lTwkRjTouuDSSTcUWTOEJtNdxfeRSyury1kDvdlsxJG5vLwULJbtU7VaDWazGZFIBNFoVM4wClyMgRiNxjsCBCNkdrsd19fXGA6HOD4+1rKeyWRQrVZ1TTWbTTWHWa1WNWoRCstaabZ+0U1CeDdjiRTwKpWKaqfpEOLSTxGEwgLdXePxWIBvOrMIyKWThPXddIcxgYTnTgABAABJREFUKsTIEQU7AnHL5TK8Xq+YNjwP6HCh04wgYQozvV7vznlMISSXy+Hs7AxWq1UNWHRusCHJZDLJNcdWLzq/vF4vms0mxuOxokNs9wIgh0qz2US9XpebEIDiY7wn8mcLh8OwWq06V/r9Pux2u2KbfO0JEicfiHFKXjMEJW+3W7RaLTx48ACBQACVSuVO++BgMBB/ig4YngtGo1GML95Xk8kk+v0+Op0OEomEjqXT6eieQtFwu93KeUc2VLlchtVq1f2s0+lgu92qCp5uUF6/JpMJrVYL/X4f6XRazi86Iyl0EVBO5hrb7wDA6/Wq/XA3u9nNbj6tsxmPce/f6uHxVfI7Pvf8f9xD7t/77R8L0cGQSaJ3/DvWEgPQ/IwZRp/3lRzT+uIKwf/7V5D++yOk/uEU8V87g/U3vg5sd3ycT3pMxwco/lNZXP9iFNe/GEXpD3yf2LMR2Jp2jpzd/Ajni29+6lxeLy3kUAwha+Y26Nbv96PX66HVaiEWi8Hr9SqqxKfbfIrKCAoXay6hBLDW63UthIyDuFwuDIdDJJNJDAYDxbmGw+GdBcVoNCpeUSgUsFgsEI1G9fQ9HA4rRuD/oOp3NBqpspYRgtFoBLvdjlgshm63i06ng1QqhdlshtPTU1xeXqrxhyDl38m04DJ2dXWlJ8A2mw2hUAiFQgGp1E1tIbkRFxcXd5YQLmZsg+F7QMgpuS9cPG5HCij2PHr0CAAEbp7NZhLbyIjgEkRRigvS7Rpwxh34VJ7CRK/X0/+22WxyvTidTrFHNpuNFiGyVXg+DAYDOYsIoQWgyFc0GlXTz3A4RCQSUbxvf39foGLW0jN2wapjspoIyybjhH+nXC4jmbz5xader8NsNqNUKsHr9SKZTKJarcJqtSKTyeh9mEwmcrNQ1KFzYjweq/GK7UBs1+L5ZrPZcHV1pQYmunr29/d1Lc3ncxQKBUwmE9hsNjgcDjmOyDAZj8dyc9DRxthRLpeTuEWRhk1WFLYIUH7w4AEKhQJCoZCWcUaiwuGwRJput4u9vT0JkZPJBNPpFCaTCblcTn+GDqVUKqXjvry8xGw2g8fjkYuHThzCZtnKxqrw9XqN4+NjVCoVndc8D9naFYlEFK8BoNprigf7+/uYTqe6Hmu12p2GLMYuKX76fD5MJhOduxSSeN1SAKbwROFlNBrBYDDAZrPB6XRiPB4jGo3i+voap6enioJOJhM4nU7U63UEg0GkUinF/1577TU8f/5cDC9WxTPqaTQa0ev1sL+/r/YtgnsJM57NZmqR4mtAsZewaDKcGB1joxa5M3Qo8rq4urpCu93G8fExHA4HyuUyRqMRDg4OAEDxOB6n1+vFdDpFLpcTbH29XuP6+hrJZFLvo9FoRLFYxBtvvCGx69mzZxIC+b4sFguYzWbFK8np2d/f132d7jo6tOiuvA06vh3b4j2W0HU2MfKaBqD7IxsDd7Ob3ezm0zzr55d47V8pYvPLdpT/+j4m/3YKm1+24+AvPcL2J8BV2PsDJ6/2AL76EMZ/8E2sP2jK3c0nP4uUH6sfAFnnud4Cjy4+vgPazU/FGF0utH/5S6j/hZ9B6fe5UfsLN/+/OZ161YcG4AcQcgDoqTJdM5/73OcwHA5RKpUkJBDMyae4rBG3WCxIJBJYr9d60u10OgXV9Xg8SKVSsNlsAg273W54PB5xFBhp8Xg8uL6+BnBTY0xoJxcYtsIQBHx0dASPx4NaraYmkna7rRgSYagUgm5Hu0ajkaIsXBqj0aiWQjpr2A7jdrtx7949vP/++xIS+POQA0FHCONJRqNRSxtFEsamrFYrCoWCgLGMJc3nc6zXa3i9XrlvxuOxGCCZTAYA5J6hGMImHADi5PDJOJ9Wsy2MT+q51C8WC1xfX8PpdOLevXt6fS0WC8rlssDJFNXy+TzS6bTiRR6PB/v7+5jP52g0GnLusCmGPzdZOQSbkt9Bh47VakW73RYPh1GS9XqtKmwKdlxUt9stvF6vqqNZM00GEKMvn//858WyoVh4fX2tp/omk0mvnd/vF0eJwgTP53g8jmg0KgHt6dOnigYlEgkA0ELL95XLtc1mw5tvvokXL15gOByqxY1tZXTdnJ6eStij24HQYgqaPAfW6zWy2SxGo5GEz9VqhUajgWg0KufCcrlUdfdkMkEkElFsidEnujm4AJ+dnSEUCiEYDKpuvVar4cGDB3j+/LmcMqwTr1QqsFqtEuwonDgcDpyfnwvOW61W1Q4WDAbVZsQIz/aDJ2I83tuirtfrRa1Wk9BFePhgMEAgEMBmcwMM4OsJQMLedDrFZDJBNptVaxqv7/F4jGKxiHQ6LX5NOByG0+nU/YmQ5/39fVSrVbhcLvGmJpOJIpZsEZtMJigUCnjttdcUcbstvvB78zWhKJFIJARtp7OR8TCyqggspxDC2CP5Paenp3oP6MTi+2YymVRpfnl5icFggHA4LEYOeT0AFE8cj8fY29vD+fk5BoOBWv14PLwH8j1llHE8Huvfg0ajofdvb2/vzutJJ9tsNoPJZEIymdS9nw2DjEKuVitst1s9eGD0L5fLyQVE+HalUpF4xnObkGVer7vZzW5282medb2B9cUVov+XL8Pyd9/F+uIKmw+YlT/u4/8fzl/1Iezmx2gMayBwNvqJEDF38+rGFAqi+SffxCRhwMJ3w15aeoGFDyj+Ug7m/dyrPsSXF3Imk4nYNCaTCalUCs+ePZMNPhaLoVaraekmaLPRaMgmz7Yoq9WqOASfIg8GA0VNGElhRIRixHa7lauCEQp+br1eq06Z7gvyLQgcJgCZToL5fK4l3mAwKBJDWGmlUtHyzeUln8+rQppcnNvxnn6/j16vh89+9rNaWGw2G2Kxm/wywbSMfzDOQbAqFyqz2YzlcolUKiWRg8fLr0NoMJ9e01USj8e13LGmeTAYSFRarVZwuVxwuVwoFotYLpd48OABBoMBQqEQLi8vYbPZxMjg+xkMBtHtduXcoBOLoFlyQxgbSSQSWqbD4TAKhQKq1eod9gufuFM0YnyKghLr7l0uF9566y2USiU5u+iKYkU6uUd8TVerlaJadFAwCsgWNbPZjGq1qifyrPBmew3dThSJIpEIttstQqEQHj58KBEtGAwq0uf3+/Hs2TM0m02JIAcHB7i8vEQ8HlcNOuNbdHHx/KeoEw6H77jRuIQzFkLhhowiAHJEUPCkuDaZTNBqteB0OsVPYt24wWBAIBBQa1O9Xpf7zefzyS3FyMz19bV4LQSNA5A4C9ws9gQmp1IpBINBfOtb31KlOvlMq9UKgUBAcTZyoygm8jxnBI2iIgVLQoV5DvB/OxwOdLtd/Qw8TjK+AOh7Mj7J+N5isUCz2UQwGEQgEJDQxuO22+3weDxYLpdYLpe4vLyUM85oNArCzpgTOTG3xWOTyYRYLCYBkW1OzWYTVqtV7hJGHa1WK0KhkI6FLVS899hsNvR6PWQyGQkWvO/yHCXQmtec0+mUmEwBaTqdolqtqhGLgglw46ijiMo2s/V6rXa3g4MDCZN0//A+za9Fzg5fQ4KYDQaDoom8pilqe71e3bs6nQ78fr/uYaybdzqdqFarMBqNEgj5vRh9JBdntVrh6dOncLlcSKfTegBAh4/ZbJaLkP8e7WY3u9nNbj7m2W6B7wZlfgWMnN38eIxhDRhWdz9SvznG9uvvv+pD280nPAazGfhdb6D7z37p5uPPfAnGj9I8GgtjkvjwKNXKCYzvRX/4r/0jmpcWcq6vr+VY4dNOOl/4yzFrtqfTKfr9Pt544w1cXV3BaDTC4/FgOByqgrlarWI8His2EY1GUS6X1bpEmC1jBIRvklWxWq3U1sSvXygU9JSaDUNcuGjL93g8quXlU+7xeCxHCwHHjFlQwGEVei6XQ6VS0YLOv0vhiDb/b33rW/D5fPD7/eLfOBwOsWkSicSdJ8KFQkFAz81mg+fPnwvMyuW31Wqp/pwMGS5rBJsy4nW7lavdbsPtdivKUiqV9PSeXIlyuYxqtYp4PK7jqNfrilaxgYnwYUbKGHuYTqfweDy4urpCOp3WQshq6MFgAAACRwcCAUF+A4HAndpwRvPIB3K5XGi32ygWi8jlcvB6vYrF2Gw2FItF9Pt97O3tKSJjMplgNBpRKBQERSUn5vOf/zy63S5sNpsq0TebDYLBIJrNJsxmM7LZLIrFouDEZPXU63W953a7XY1EbFoi8yMajcLpdOLs7AwulwsWi0WsIzawrddrCXcWi0XXyGw2Q7PZVCRqNpuJp1StVlVdzXONMT3GpSgWADe16W63G8PhULXjXLaz2aziJna7XQIdRQcu6oypMV4WDAb1fbxeL2w2m+rW6b4BIPZTtVrFaDSSS4jX9Xa7Fbg6m82KJWWxWBAIBMSpoTBrMpngdDrFdeG1TxitzWaTIOFwOO6IDZlMBsvlEt/61rcQjUZ1n5nNZlgul5hOp+ICUZg7OzuDzWaTy8/r9eo9KpVKOD09Ra1WQyqVkvvF6/WKM8Priw4xs9msex9jTJPJROIYW6voBuE1MBgMFGVlq5jNZlM0K5lMotVqIZFIqB58tVqphY1RLVazD4dD+P1+AFDjFwU4j8cDABJQt9stqtWqon3b7RYmk0lRVTJ4hsOhjqvT6UiooTvL5XJhMpnITVitVgFAIGqTyYRmswmLxSIR63YToNlsxmKxQCgUgtFoRKPR0GvKKnW6kuhC4jnTbDbh8/l0n5xMJop05vN5XSvBYFAPBRjZ4vmxm93sZje7+Xhn/fwS6f/vBM7Kd/Jnyn/yCOb4dwc67+YnfyzdKUy/w2BjGQB7fy2PzL//tTsfhq+892oOcjevdKr/i9+F4h/0YJQx3HxkDaj9s2/B9EFz7E/ivLSQk0wm4XK58OTJEwAQ8yGdTisSM5lMZGMnQ+PNN99Er9dDs9mU88VoNCIcDquWOB6Pa1EhCJc14rPZDKFQSMBWr9eLYrEIl8uFQCCA7XarX/pdLhfOz8/h9/vlmqnX63KKMHrB7+XxeNBqtTCbzZBIJOBwONBoNBAMBiVq8Ol9s9nUUsen+wQNs3UqkUgofkJRgSKX0+kUSyISiaDT6ej7dbtdvP3221gul+h0OmLktNttrFYrPY2m2FStVpFKpbTs8ZhYsxwKhQBAAtZisUA6ncb5+bnYQwQVk2NBVhHrvlmfzcWWbTx0dLBViPwJijB00xiNRmQyGYlBjJSxNrzb7aoBinwbMlz4GnBxZxzMarViNBphMpnoWBiXMpvNePLkicCudJqQY0LAM5dACmAUUJrNJlarlaJu8/kcHo8HNptNNdaMzpD9xAgQK6kpYLDemvGlyWSCzWaDRCIhp4HZbBYAnNGSXq+HVCqFTqeDYDCI6XSq9qxAIIB6vY5YLCZGFF1CbIDidcPXutPpwOv1olAo4PT0VPXkFODIGgGAx48fw+fzIZ1Oq1WOokSxWEQsFhM0e7lc6hzje0TXwm2wL9kwPp8Pq9UKs9lMTieKOBQIG40GJpMJvvCFL2AwGKBYLMLtdgOAfg4u9Gaz+U4ULZvNSrjg61+r1WAwGJBMJtHtdiU8pNNp1Go1VKtVeDweic90mDHORZGarjYKz2RMkdlEQY4iLoVnXq98LdgOx2uFsR+j0ShBOBwO6z0lNJ7ia7VahcViQSqVkrjCSFStVlP1OWvUQ6GQODA8F9goSFGYrioCntkqx3jZbZgwYcC8H/Ia22w2EvMDgQCKxaKa0KxWK8rlsvhRs9kMXq8X/X5f1y2FFALXg8GgwOf8PsCNA5G8rPV6rWp6xjDtdrsYPRTd2G5GIZrvK69d/kxut1vne6VSAQBdq3xddrOb3exmNx/zbLcw/KNvIfKr34TvxV1rztIDjD736mMMu3l1s3nvKVL/nz7wgc5nngDpv13GqlTGdrW687Gbn74xfP4NrOz4DmVjHgRmb2VfyTF9EvPSQg5/sR4Oh4oERSIRtZlw6eACwCe8FosF/X4fh4eHio9QYGAdbb1eR7fbFXA1kUhIbCH/gzXjlUoFkUhEDVJ0H7D2mAs0q4Y7nY4iGGT1kAVBRxB/UWcFOI+NLBK2T81mM0URPB4PzGazmA2MdNG5QWGEsFhyMigo8Mn4bDbD4eGhYiaMfJGBQa4PxSrGREajkWI8/X5fsQQujYwocBler9fweDw4Pz9HPB5XZIxcHavVCpfLpddjs9nA7Xbrv8/ncwkBdEMdHx9jMplguVyi2+1iu90iHA4LllooFODz+fS/b8No+/2+RDzWTLMmm5BduhqCwaCaeChyLZdLlEolOU3Y4sPa7EQiocXPbrdLQGSshdBuAOJoMLZhNBolKDL+NxgM5Baii4HnQLPZVNMR4b1sUHv77bclThWLRTV0EbTqdDoFRQagZT6VSuHq6kriU6/XkzvK7/erYpktXIxJ+f1+VYhT7JzP52i1Wmpnuu0wqFQqSCaTElnb7bbcRhTiGOGLfKBoM5poNBrlxGBMiAIsnXCMCLImmm4Oxrv4HhEkzphOvV5XxHF/fx/FYhEA5FBhRM1iseD6+lpOETJYCFm/3ar08OFDQdt5HO12G3t7e7BarZjP54jFYmg0Guj1evB4POh0OmK7EOgdCoUQiUTw6NEjRCIRuQQpAFPgILuJTVPL5RLj8Vig3k6no+jlyckJLi4uxIzha0zwOl+TXq+HSCQiVxc5Li6XC16vF6VSSYD4TqcjZ9NqtZJrii6yp0+fSjCtVCo4PT1FKBQSgJ0xM95HvF4vzs/P1XZGAW+1Wsm5yCp0ije8H9IZdXV1BavVinQ6LZ4Rm9fozOH9joIl73m8tm6ffxT5CVW22+1qTWRrGJ02vI7pxCR8utfrwe/3I5/PIxaL6ZoxGo2KGe5mN7vZzW4+mdnMZnAXZq/6MHbzaRqjCauf/yyqv9cHfJB0MS6BTaP1vf/ebn5qZrjnxPb7JDCNdjsMP0gT6WIJ43dJ1xvWgGXy6kXDH0jIue0UIaul1WqJ01Aul1XZzUU6HA7DZrPh/Pxc8FcAYljcBp5SCGB1cTqd1gJvNBoxHo/VJMWn7wDUusKFlg1YXEjppLHZbHjx4oWWMv6S73K5kM/nkUgkMBgMFD+JxWKCHM/ncy3dVqtVrVxkAbGd5zZk1e/3C0683W513EajEaVSCT6fD6enp+j1enj//ffFoHC73YIvEx7NmuR+v3/nNbFYLGJdzGYzzGYzxGIxlEolBINB1Ot1iQMUJ6xWK+r1OlqtFo6Pj+FyudDr9eB2u9WiQ9eO0+nEfD4XP4bCCOM+5BRFo1H4/X7Fzp49e6aWI7obHA6HRAQ6X9is0263BUam6BAKhdBsNhX9odOFsOzT01Ocn98A8LxeL2KxmJwV7XZbQFOTySQnxWAwwHQ6RTabFTyZAgRZLBSZCHe9zdWZTqdiF1EQ2mw2gl4z0sGflUIA40lk+/C8oyMsGAwq6pFKpVAulxXLYYX4aDSS64C8kel0KucAX5tMJoOrqys5NOhiIHuK18t2u1WNOwW3drut+nO+LowA3X7v7HY7qtWqeEHD4RAej0dxOl6D3W5XbhFWlhMwTrEnEAgobtVqtdDtdpHL5eSiKxaLggUzJjabzbRsZ7NZ1ZcbjUbYbDak02mMRiPk83nxY+7fvy+3DqOToVBIvJ3bggevGcaOLBYLzs7OBJymOESX32w2Q7VaxcnJiX6ufr+PwWAgkPl4PIbL5cJsNoPBYFB1OUG+tyNp9XpdvB+2dFEs5/UyGo3g9/uxXq91rKlUSuc9Y2h0zZhMJsWsKKJSdLbb7cjn84qHsTXudkOaz+fTPWmz2cDlcuHw8BCDwUBRpH6/j1AohEqlIgAxWwkpvoXDYVxfX0ssSyQSuv+TF3S7ZpzXEhul3G63Hiy8ePFCjku+J8C3mW48rxg95f2y3+9LnDSZTIrXzWYz+Hw+2O12iWa8B+9mN7vZzW4+3jEdH2D9e9/BcM/+qg9lN5+iWf2+t1H9GRuW7m//t4UPaP3SmzD5fa/uwHbzqRnP5QiG76GrGD7/Bnp/7G0M/4nPwPCZBy/1NdcXV8j8RhfGD0nYx95dwPgPvvlDHu2Pbl5ayLHb7aql9vl8qFarEgXIkGDLC0GmXLr4ZJ7CByM7qVRKcSkuavl8HovFAslkUgBRAonJO6ALhU/1fT4fRqORFoJqtSrhKZPJ4OLipn7O7XaLzcJYF2M59Xod8XhcIkIkEsHl5aWeJrMKl0/6ASimxQpgn8+nBdxsNkvYWa/XarUBoFruYDCISqWiKAAdKD6fDwaDQcvYcrlEsVgUX4Kcou12K2dPp9NBPB6HzWZTxTfFEwpVoVDozmtHF5PNZtPrQigzADkuyNg5OTlBoVCAxWIRAJcxina7LWGBbUJsNyLM2mKxIJvNqnrb6/UKnEqAMYUyLpvL5VIOHcai+J6TrcJIBCM8dO5EIhGdc+VyGfP5HHt7e2L5+Hw+xX8Yo+HiyPYocj0YX6NoR7GRjphoNKon/uRquN1uzGYzpFIpNScFAgGkUilcXl7KLUUWCJdth8OBq6srxaTS6TT6/b74KRQ5AUhooEPnNqOHFdh0WbA2G4CcVWTKMMrV7XYRiUQUE6LQRgGBjCMCeQGgVCrJtcKh481ut2tRNplMat8Cvu3yazQagmSTgeJwOBRDCwQCaj2iIAvcNNbx/bNarXjx4gWcTidWH9hqDQYDgsEgHj16pLpsVnYzfsZ6dsZD+/2+OFN0mjAOSMeg1WrFe++9h0wmI3Fqu90iFouhUqlIMOV9YzgcKkpIccxisei1MZlMEl4Jso7FYigUCmrRo1tsMpmgXC7D4/HgwYMHigKx3YpsLMai6KThvZU8JTY7Ucym0M57N8HNPA8IJKYYxMYpnt9sxKJDkmJzOp0W+JzMHd4TeC9kBI313xThGSUtlUpIJpNyK/HeE4lEYDAYdE9wu91YrVZIp9Nq1KOwSEg0422r1UqAct5T+T3b7baENnJ7drOb3exmNx/vmA/2UPqjcVR+zo7B/t31xDwB3L9deEVHtptXOZuf+wxqX/hwF8U4ZQBiP7n8k928/Gx/+zHM0+/87/bmFluTAcU/4EH/yIjuqRGlP+SD4fNvwGCzffvDYv3Qr7t57ykyv9G7EYlu4bvs7xUAo+nj+WF+gHlpIadUKimeMp1OBda8zeKgXX0+n8Pr9cqdQm4L7fWs7b4Nmex2u4pAsaWFvBmyaKxWq1ghdCqEQiEtR+VyGQaDQQ4TLrfhcFhPjV0ulzgMdAVtNhucnJzIpcKllm4el8ulBpr9/X3xf+j+YTwgFAoJfHxxcYFkMik4MJkSRqMRrVYLyWRS9c2MdNC9QdgteRSsjgagiFWv11P8JBQKwe12S/jh6xuJRFCr1dR6NBqN4HA4UK/X9QScogidBU6nE/V6HavVSgsVoaEXFxd48OCBKr0plvX7fQSDQcViGOmga4ILEp1N6XRaYsVsNpOAwXYw8lKCwSCsVqscE+FwWFEHcoroAspms+j3+5hMJgJSTyYTiYlkttBBFAgEcH19rWaidrut15qOE75fgUBAUY50Oo3Hjx8r1sMWpNtMHIvFojYdv9+PZrOJxWKBBw8eoNPp4Pz8HLlcTuLbaDRSc9RqtRIrh/ybZrOJ7XarKnJGfujwYBU03Q88t9nWs1qtkEwmMZvNtITfjmvRcTUYDJBMJu+wkehacLvdKBaLAmE/evRIrwkAxRl5flMQ4ntHKC0dEPyzJpMJjUZDLV50bFA4AaD7CZkmFAIJzKVryu12y6XD6/Hi4gJvvvkmVqsVWq2WHICMg/IeRfg4o0p0eJBxxOayaDSK58+f65ymk4nRnlAohGKxKGcMI5YUWPr9PlqtFgKBgK5Bxik3mw1Wq5XiWwQE367M7na7SCaTasCq1+uIRqPweDyCezPSxYazSqWCTqeDvb09cXEIh6eA+c477+C9996DwWDA3t4enj9/DovFoqr4w8NDXF1diYVD2DRb7FgTf1sc48/carVUoU5BqN1uq5mOjrL1eo1IJIJut4vVaoVEIqH2udvnts/ng8ViwXK5RC6XQ6vV0jk8mUzEYTIajbr+KMZQxOG/E2azGaFQSMIcY4503lHE3c1udrOb3Xx8YzCbkf/FJFbfpWBm5QTGn8l8sge1m0/FzEMWbL/Hvlz8x6OA4cObhXbz0zWp//IC3quN4lCOxhaxv/YeYDDciV1trEDhFzwo/Mpn9dH4s5/9rlDkzbeeYO8/u4St9+3/dv3LRxj80udh/AAl86rmpYUch8OhpZpPUBl9MpvNejo7Ho8xHA4RjUbvAEPJu5nNZqoLjkajiEQiAqhOJhMAN04XLukUM1gFzmWXzVODwUDxDcYjKJKsVivFDLjgNZtNLJdL3Lt3TxEFOhbsdjtSqRQuLi60+HQ6HeTzeZycnMBut8NsNsu+z2Werg8e+3K5VJSnUCggmUyqhep2PIHtW0ajUc6W26/fYDBAIpFAv9/X03Cv1yvnAYHOZNyQu8Ll2Wg0IhqNolQqIZFI3GkYA27iSLe5N6woZuPSbDYTALXRaCAajep14kLPSNFkMoHJZBJclD8XK7W5zG63W9TrdcGBWRseiUSQz+cFMD44OEClUkGpVBJf6LYDiOchnVyMlrBKmVEj1tuzAYvnAmG7dNFEIhGMx2PV2y8WC2y3WzUycWGdz+eKitCZYjAY0Gq1BO6ly8vpdCpiZLFY5GihiEMBsFqtIpPJCJpLMcPtdqPdbmMwGIjXQ/GIXBFyo5xOp5hVhLoCNy1EtVpNogJrsQEI5k0XG1vpFouFzkM2R1mtVkSjUXQ6HWy3WwSDQcXu/H4/SqWSIkZ0lPGaoOBCNgudEbx+2EpGEHOv10MikdBCTlGRjq9UKnXn/Gq1WrrHsMaarVUWiwXtdltuFjKqHA4HgsEgWq2WBONQKITtdotCoYBwOIxaraavTaccYejHx8fI5/PIZrOo1WpivCyXS+zv76NcLmOxWCCXy0l8okjE+1aj0cDe3p6A3xSn2cLVbDbhdrths9nEd+J1Q/4MXY3kZCWTSZjNZtRqNUwmE319itv8u61WS0ysQqEAg8EgZxLbthqNBiKRCMLhML7+9a/D6/UKSs7mwsViodhVJBJBu93G0dERzs/PdQ5QVKSATkGMx0Q34nQ6xfX1tWKL19fXiEQiCAQCin7yXGdr3eXlJd544w3V1RPeDnzb6eZ0OnVeEKycyWRgNpvRaDQwn8+xv7+Per2u64Ywa/6bsZvd7GY3u/nuY85lMPnjX4DR/sNHorbfZxffGnfL+m6+c8IPFzfV9bv5qZ91vYHgN7swfMBKj7w7wOYDbeE7xoAbFeSDj1nEgPofO4Lxg/bW3znz0yTmgVv/wQj0j4zo/+NvvlIh8aWFHJPJhLOzM/zcz/0c6vW6bOyr1UpODy7BXq8X19fXii6R68In7Le5CbSxs4GKv0CzFpwLC5tJisWiXCysyLXZbKoVZ/yKQkqz2UQmk0Gz2RQ8lS0wFBHo0GAzzN7eHubzOQqFgtp6xuMx+v2+moOAb0eHRqOReBhsGBqNRmg0GsjlclokWDdOF8JoNMLR0REGgwEuLi6w3W7x4MFNbo+QU0Y5yL+hc4YwW0ZjuCSen5/D6XQiHo+jVCrBYrEgmUzi0aNHcj7RLRMKhVSxy8p3Rme22y2i0agqvvl5n88nPhKXKr/fL8cFGT2j0QjdbhdvvfUWGo2G6usJSG21WkilUohEIliv12i1WoplUEgwm82KMZnNZtVDF4tFZLNZlMtlub263a4WdrrGAKhenbwQ8mgoHsZiMVxdXQGA3Aq34byz2UyV03QyMPrFeAndGKxhd7vdaunp9/t44403VPlONs1isbjjKut0OoJIn5ycKGJI0YItYQAElHU4HDCZTPD7/XA4HHjvvfdw7949rNdrXF5eiltDBwPda/P5XK4h/owUVOgwu7q6knBJAafZbGJvb08OCS7G3W4XP/uzPysxiJyV8XiMTCYjAY8A6H6/r9ag7XYLj8eDWCwmFo7X6wUAXb+8d1Ds4XV/m5M1nU5xdHSkqOZ8Ptd5s16vUavVsL+/D6PRqDgNzy26PBgpcjgcGA6HyOVycn6ZTCbMZjOEw2EJGE6nE1dXV9hsNmLJ2Gw2ZDIZlMtlMZdarZbild1uF5lMRjXYFBXJMKIQyGY/iqgAJJI1Gg058BhXJEvH7XajXq/D6XTC4/Hg6dOnErUikQiur68Fmfd4PHK4VSoVNepVq1V4vV4BgPf29iQC8n5Zr9cVe6VLErhpe6JbkaB5upvOzs5wenqqiCSF+eFwKO5Nv99HqVSC2+1GPB7HbDYTy4dOnWq1ivl8joODA52rFosFL168QDqd1gMDxv8Y9SLk/uDgAPV6XecvWTrhcFhCj8/nw3a7RT6fV4RwN7vZzW52851jtNtR+MUM2g9MaP3JzwAATJEINj/3GVT/Vz8D88Hed12OdrOb7zeu33gI8/jDP2eaAc7H1U/2gHbzqZ5ZyoP179CTbb/5EJ789xf7pjEDDE7Hd/x3UySC2hc/XKQe5F5aSvlY5qW/O+uT3333Xf2CDUCOCC5Wk8kEiURC/JXDw0MAQLvdlsU+k8moipi/oLMlCYCenLOFyev1KnLECILD4ZDLgI1IvV4PFotFi5vL5UIqlZIAwraoQqGgZYyCD10GXBboeOGTckYVer0exuOxWmj4tJ7OHJvNhmKxqKptxqQI8GQlrt/vh8FgwPPnz7G/vy8wcq/Xw3a7xWAwULSCnB7Cpil4kJvDKBoAPXnu9XpiiABQPXO/30cul8NgMMBwOITP59MT51QqBYvFgm63i+FwqHgKl1vyMSjghUIhvd4UpwijTiaTasmy2+2K3t1eAAkRpQhCIYiCHAUR1nXzaT1jXQaDQW1SwWBQXBJWCjM6xsXf7XbD4/GoYctsNqNYLOL+/fta6HicdBKMx2Pk83k8ePAA/X5fTiCCVcvlMo6OjsTgWK/XsFgsag1LJBJaPFkZz2gghZTbsGAKIberkvlzUCDr9XrY39/HdrsVhPnFixfY39/XNQBAfCZeO3a7XU4Uunx6vZ6gyv1+X7BpAsEZgbNYLKo+pxjH43W5XBJ+KMyxBarT6SCdTguE3Ov1xL+iq2k6ncqdRbeIz3cDr+PXJ6eJzVtsHaOISdgwOVKhUEjgazJ02u22zqvFYoFnz57h6OgI4XAYjUZDghgAiXDRaFQxnc1mg8ePH8NmsyEYDModw3gZHT68rigi0QHHa/l2jI/OOYLV6fihKMTrjNd0IpFQjIxMI4fDIcGi0WjAZrPJyWi1WjEajSQGUoSkqysQCOg9pXuFziRGUQuFgnhA1WpVzjveh9vttoR1Vriz2YvNgmwHe/bsmaJiFFz4bwgB3jwP+F5cX1+LofX6668rztZoNCS0MXpHsDEfNNCpRR4RxanVaoVKpYJ4PK77PJvjCJomI8n5im2zu9nNbnbzqR6jEesPECZr+wesmz99jPLvdWDlAq7+6STaf+L17ynmbNdr+H9H5fhudgPctJgFzz6ENgsg9GiJVan8CR/Rbj7NY7/uIvKtFWwdwFTvAQC28zkCv/YNRN9dIvrbSxjWP+AXNRqw+ZTiEl9ayCGvhEsuo0G3RQlGMfhk22KxYD6fy1pPODLwbcGhVqshGAwilUqhXq/rCSyrYUulEtLptKqKydaw2+2Yz+eIx+N6Kn7//n20Wi20Wi01P41GIzUisXmJkRjCYSlyGAwGOTI8Ho8YKf1+H16vV3EwVgpzWSUTYjKZYDgcyi3DpZR14oxVscWGoo7H40Gr1UIoFFI9OJuhQqGQXovJZIJSqaRYmdfrRaPRQCAQECvG4XCg1Wrh5OREUaDlcomTkxMtWLVaDePxWBEovv5sOmJFNhk+y+VSvJXbzVQ2mw2PHz8WayKZTCqqwrgU4bKRSAQXFxcIBoNys/h8Pnz1q18V4PY2x2I0Gqkqme1Qh4eHODs7Qzab1WvtdDqRSCTkWFoulwiFQuIFMTbD96fRaCAej2MwGGC1WiGTyUjIA26A2M1mU7BlPslnJIfOo0AggIcPH+Lo6AibzUaxmnA4jFKpJG4R65Yp/NB1QF4Iq+TZ0EVBihXy5HysVisJLwQpNxoNucV4DbLdi6yffr8vl898Pke1WlVVPXlJdNQxjhYOh5HP59UoZTAYMJlM4HK5xEUJBoOqemf9+3Q6Rb1e1zlyfHyM0WgktxUjj8FgUK4SigVsRAuFQvB4PKoAp0DK6nWz2SzHCBvGGAGaTqeIx+OCAVPkSKfTcDqdcmrxteQ5PRwOAUDvNeHRvIYvLy8FUo9Go9hut2ruo6uPAgJFJafTCbvdLkcMId7ZbFauLAKdeU4ZjUb9HL1eD1dXV0gmk2JDMS5Khx8jmIvFQuLDdrsVs4m183SpUfSlUEx3GnAjKvE6ury8xHQ6RS6XQygU0r2X1zJhyqlUSpw0Xr+TyUQCEZsGyULbbDb47Gc/K2cZBb/JZILr62sAN7wj1n+Xy2WxixhZZfyRP084HNa9LJfLKXbKaCQdfhQyCav3eDwSeyh20s1EoZuwZzqidrOb3exmN985m+kUiS/PYZoD8b/XwPg08h2sm1HGAGMk9N2/yHYL3zcb333B2gLG1S4+89M69r/7Tez/agXOyhbGBWBcALm/2YHzay9e9aHt5lM26/MXsP/615H4r57fEfm28zls/93XYfuNbyh69bKzaXcQfv/DK7E+rNHqk5yXFnLa7Ta22y329vYwmUzUOEQ4pclkQrVahd1uV6Utfwkmt6bT6SCTyeDs7Exg3WAwCLfbjcvLS1itVuRyOZydnclJ4XK5FGfiwmOz2bRA0nFCFkgwGEQkEsHV1RX8fr94J4wu0EnAul2Xy4VsNquIDJ/wm81mLaChUEgtQ3xS22w2EQgEFN1iRIKAVMZM6KoJBoPw+/0YDAZ477331CYVDodRLBYRjUaxXq+Ry+Xw7NkzeL1eAWYZn+LrabPZtNxThKLjhJBpLlSsvX706BHG4zGcTqfquAltNplMiqwBkIjk8XjQbrcRi8W0wFG8I4Q1GAxiNpthOp1qCc1kMqjX6ygUCncEukQiISdVpVIRPJeiA5c6VjyTGcPIB2MdjKhR0KFLhRwnujdqtRqy2awEO3KG6BpbrVaqKeffpWhFUYSC5Gg0EjCYUTc6Dhip83q9qFar8Pl8EgRHoxFcLhdee+01nJ+fqyGKPA/ypgj0Zq07m8AYS/L5fHJGmEwmOULK5TI2mw0CgYD4J8PhUEv+eDzGvXv3cH19rQhPMplEr9fTuXubHdXv93UezudzjEYjHB8fo9/vo1arIRqNYjqdKt51dHSk6CSvT7p6HA6HYlydTkd19pPJRCLWfD6Xg4bRtlarBbPZjEQiAZPJpIhjr9fDixcv8M477yiq5HQ6VenNewAhvxQNWdXN+89kMpETqlgsIhaL6RpiaxJfI8aZ2u22Gpp4HYzHY/FskskkarWa3BuMROVyOV2X2+0WDx8+FIDYbDbj9PRUAoXT6ZQDK51Ow263o9Fo6P7Kxia+j/F4XMdAHhSbzijqxmIxRSaz2Sy63a5EbJvNhmq1imAwiGq1qu9/fHwMg8Ggc4uOo1QqJQGfzhbC6/maDIdDhMNhRb0ikYicURcXF2g2mzg8PBTAm/9eZDIZXccGg0HnAuNjFJUJsLfb7frzwWBQlecUC3lPIR+LDi6K8rzeeX4CN5Hfw8NDDIdD9Ho9ucXIbtvNbnazm918yGy3MP/930by3/8KDNM5Gp/98EfXxX8y+T2/zPr5JdL/w/BDK4SDT9ew/sbXfxRHu5sfw9muVlhdXiP0n34Vqb/0FaT+0leweXSGdbvzqg9tN5/G2W6x/m58w+0GrsqHi8K2NoDp7Dv/ymoF9/M+vJebO81V1h6Q+S9evFJG00sLObSzMzbE5ZJtU3zKzEXN6/XCYDDIjcM/w2pwQnHppPD5fKrq5VLabDYRDodRrVbvwCxZyX2bIcNf1MmQsdls4jCk02kBlilsdDodxGIxtabQgcOFlA0qk8lEtdOMUAGQk+fo6AiNRkP124yB+Hw+tceMRiPYbDYJCvF4XMtyPp9XXIuuDQJFKQSkUimUSiUMBgO89tpr6Pf7athhZMLj8WjpoXPKYrEIlBwIBOBwOFCr1VTVy9euXC6rejgcDqt2nUsTI2R0UbH5x+FwKB5lMplQq9XkLKAoVKvV4PV6sVgskMlkUKvVBHdmbIQOBzb8sJqY8GbGker1/z97fxJraYJfd2LnzvM8z/eNMeVQVUmySUoCKdluWPKi3bI2luGNAG+8sA0vetNGu71rwIDg3rl7I9ttQLAlm5bgltWNbjTYtsgiWVNmRkTGizfeeZ7n2YvIc+o+soqMqkpWklnfH3iorIgX79373e9+7/3Pd87vtPDhhx8qasRzoNVqKQrEu/9kkbDFhi4miieMtfBcHY1GiqQQeEq2idPp1J+z+Wo4HMLhcMDhcKjCmfEggp/pnlgsFuj3+5hOp0gkEmod83g8EtIAYDabYb1eq8GH8FnWxfO91e12Ua1WxRvh+3A+n+t96Xa7Jc7QHVWr1RCNRvHw8IB0Oq3zme1tfC24kDOaNRqNHrlmeO5wSaaDhcLP+fk5AKBWqyGRSGA2m8FsNus9yiY0wpu73a7icHx/MUrImBddOYFAQMs7nx9f73g8LmdSqVRCNBrVe5rV5RTWjiviCV8maJjiMMVFOmcA6DnTwbFer3VtpFOGDg9exwBINM1ms2qDo/AGQC4ZRicnk4mES5fLhVwu9yg+6vV6US6XBQ5eLBY6P5xOJ7LZLGq1muDmwWAQtVpNx56Nd+FwGDabDdFoFLVaDbPZTJDp47r0fD6P6+trCccAFIdyu92w2+3Y7XaKNTGCxWMBACcnJ/p54ff7FdPkzwi+tuTmUEjhtXO9Xku8dzqdqNVqgp8fw+3ZJtZqtdTgls/nFQHz+/0SL3m83G43RqMRDocDZrMZstksDoeDhGRjjDHGGGP+guEy81O4n/Ef/NkF6c/MH3+O/H/eR+jq3cLk6AH5fz2B9//9o6/sYRrz13gOhx9/GGPMzzOHA8K/9/IdM+cAfTj6QPr37rAbj3/iP9u9ukLgn/4J8v96jPy/niD/ryfI/L/K2DZbv9SH/6fnvYUcOk4ymYxs57PZDNvtVlEi2vw7nY5iSowwbLdbHA4HRKNRTKdT3b33+/2660mujclkwn6/FzclkUig0WggGAxqAeHSwIgIozCz2Qz9fh/xeFx3nAnEpRuHXBDWFQ+HQ4zHYySTSZyenqLb7WI+n6Pf78vG7/f7VUO+3++Ry+UwGAwUaQgEAnA6nWg2m/jwww/R6/UE9U0mk4JocvkLBoMol8ta3p4/f46XL1/K3UDIcCqVUu0uAalOp1NxgWKxiOFwqDppv9+PbDaLq6srtfgcMzNYD04+y8nJCTweD66vr3V8uVBTqCDPKBAIiKPB1yyRSGC73QqYzFYiwm0pZvl8Prx69Uow6GNRJRaLodvtKsJBOCoXNjqzKCb0+3018Hi9XjEurFYrOp0OUqkUdrud3GCsRp7P53IZkeOUz+cfvd4ul0ucFcb4WN1eqVRgsVjEQqLIweOSyWTQ6/XEM2EMj/yQi4sLcWISiQQOhwNGo5E4T9vtVsBhulXIoWGNO+NBhDlT4ORjin1ZnUdXDlk76/VawgghzcdC4+npqWrQu93uI+ccXViMwLXbbTmP6K5xu91wOByIRqN4/fq13kcUGnkusQVqOByi3+8jk8kIVP7y5UvY7XY5lljRPZ1O5f4Zj8eq4SbvBYBYV3TRWSwWNW7N53Mkk0m5wfhaVatVHWsKkxaLRa4tj8eDYrGIVqsFm82GQCCA169fI5FISNijSJdMJsWtIcy91+vJucfjUyqVBKDm9cFqteLi4gLAOz7LYrGQoE32C6NmfIyTyQSXl5cYjUYSoinsUhzM5XIYj8d6jfl+JaiZLhY6WHgNoyg8GAzEKGs2mxLgB4MBGo0GisUigsEgZrMZGo0GPB4PXC4XxuMxfD6f3JHr9Rqj0UhONp7HPCeazSbW6zWcTicSiYScaG63W+c2GTl0um23W0UkCXZ3OByYTqeCOBcKBZ2bjNlFIhG0Wi1cXV2J4zMcDmGxWOTkstvtuL+/RywWk2hmjDHGGGPMXzzbag2Z/3bxZ1w11jlg/6L6Xl9j/9kb+H7vByj8x58j+X/6EQ5/8jkOX7pfjTHGGGN+0dlPJgj9395dY/iR/Cc/wrbR/Av+4Q6H7718d036k8+xrbzfNe0vc95byGG8ZbVaqc1oPB7L9dBoNLDf7zGdThGPx3UXs9fraXHM5XKK2/AOKgCxGyKRCFarle52cwnZ7XbI5XKoVCoSkhgt4R10l8sl90EwGJRLh3fQKTTQjUJrPuGaDodDd2jJ0LBYLIhEIhJ+6BLYbDZaag6HA4LBIHw+n3gRXI68Xq+agprNpiIzXFZKpRI++eQTNVy53W6cnp4q7kA3zf39vaIHBDLTucMFbTwe4+LiQovr2dmZqs+54DD6RqGCwN3RaIR8Po+bmxt85zvfUZMPBZDj18put4vXwdgGG48AKIpBtw+dRRRYAOj1YAV1s9nExcXFIw4N3Vfkl7CeOBQKCdDK83I0GiGTyQCAFmQySprN5iNRhM1qnU5HcG5GrhiroduGDA3ClcfjsarDB4MBYrEYPB4PxuMxQqEQms0mgsGglmI6SOje4pJN9xdrxqPRKIbD4SM3A4VJi8WixxmLxXB9fY1kMim3STgcFrPK5XJJuGSTGBlBjC/RKXV7eyuoLsWNw+GASCSiqM2zZ8/UANXv97HZbCTkJRIJVbGXSiW0Wi1YrVaEw2GJZWQmMabE91an09FSz8fpdDqRSqUk1HY6HdhsNjkx6F4JBALY7/fweDx6zXjN4LWGMb9SqSTWC4UCu90uN8exyMd4EiHVhHh/8cUXOrcIL99sNuj3+xJAjlvRNpsNgsEgRqORKs95vq9WK0SjUXS7XTnECMq+u7vT+4vnwXGDGrlBPp8PnU5Hx99qtWI4HOr8Ojs7g8fjQa1Ww2azwWKxkEuPx2az2WC5XIrLxSY7Xu8eHh4QjUb12rDpik4Wq9UKi8Ui5pLD4UAymXzExGFz32g0QjKZRK/XkwOP7jECtulAXK/Xchry+DMKSiGb1/mfVDEeDAbFjmJTF0VoOpZ4DtChw39LN91+v5dId3NzI3aaMcYYY4wx7zGHA8y//0Ok/2ChP7LOgOy/aGDXen/e2GGzxn4y+enVwcYYY4wxv8AcVqt31xh+/DW91vxMQk4wGMSbN2/gdDoVRSiXy+LKMEo1GAzEamC0guDh6XSqO8VsLCGjJJlMyt7OZYbxFy4MbAwis2Q0GsnJAEAtR+PxWI4UTjablXuHi0Gj0VClsc1mQ7VaRSgUUuMJRZzNZgOz2QyLxaLllk4RNp7QucMowW63k5BD/sR8PsdoNEIkEkGxWES5XFYcjHeeGf1hdInLJyNILpdLkTNWcpMtA0CxI4KnCYbl1zmuLuZCzaapcrksEC9hqXxdE4mEnFaxWAzVahXBYBAOhwO9Xk8RD5PJhEqlolgJXUgUdyi4DQYDBAIB2O12HcPjRqpwOAyz2Yx+v69lv9/vw2w2K4oB4FHzGQUXq9UqN8F2u1Wb0uvXrxGNRvX6cMGnwDIcDuU0WS6Xiv9R2CMUmQBbslnopBqPx8jn82rFurm5QTAYlCBjsVgExE2lUri/v8doNAIARVHo4GIUiTBj4J3zhK1ijLXs93s5X0KhEA6Hg54v4cDHkRiCXt+8eSPuE99nhFtvt1uxXMgxIWDX6XTKGUb3VT6fh91uR7/fx3K5lHDz4Ycf6rX2eDxqRctmswCg2ur1eo1arYZisQi3243Ly0u8evUKdrsdyWQS7XYb9Xpdr7HD4UCr1VK0yOVyKa517NBgpHO5XCr6SbcYAIzHYyQSCTFveK6Uy2W4XC61mNHhxMdKKG+329X5ywpzt9uNQCCgc+o4orXdbhXTstvtqFarAg5vNhvE43FYrVbs93sd73q9jnA4jGQyiU6ng3g8Lrgyo0c+n08w7OVyCb/fL1iw2WwWvPw4nkrnEwHQjBVdXFyo0Y0cK17T3W63AM37/R7RaFRuFzLTwuEwrq+v1QIVCAR03R2NRgJ40z1DIDEAAZMZfzt2XvKxUtwBIFfb6ekpqtWqXGgUR8mcosDM+C2/B6H5dJhREGOcb7/fo16vv++PSWOMMcYYYwCY/9sf4eT/UsHJf1ZF7p/eYXdz/3U/JGOMMcaYb9y8t5ATi8XEMFksFhIxuMxTMGELEgC1JlmtVt1dDofDYmeQb0LOCOG5XK5Ziz0ejxGLxR4tmWazWQ4X1tsyZsGFgy4JWv5Ho5EarcioYHMTHQ+Xl5eoVqt63Ov1WnBXih7NZlPL736/1wI9Ho/RbreRy+UEg+12uxInCHYej8dotVqIx+OK6pTLZbksKODwrj45NtlsVpGzRqOhRS8Wi0ksaLfbEsJ8Ph+sVismk4mECDJqWCvMu+QABDQmHHS5XGpJXq/XWo5ZsU73ic/n0+LIO/d83CaTSbDmxWKhO+LHzUt0LeTzeb2+dJcQ4krXlsViwX6/RzabVfXwbDZDp9PRokaA8DG/YzabYT6f45NPPsHNzY0YSVzc6BpjBGQymeDi4gKbzQZutxulUgknJycSlvr9PtbrNTKZjB4HxYTFYiH20pMnTwRVrtVqCAaDODk5wXg8RqVSQaFQkHOD0SMu2uQzsRWs2+0qRkfweL1eRzAYlFDKc3a9XmO1WmlhZt24xWLB/f09njx5glwuJ4gx30dsdaOImclkxE3x+/1irNAxQQGI1wM2eGUyGYkGZPKMRiM5TMhWIsOFcbrr62uJxox1LZdLcV7oDKO4aLPZ5Fyaz+eo1d4R6tnARXGD8N1er4dIJAKLxYJ0Oi1hgo4vxoCKxaKie+QZDYdDxGIxMZXK5TLy+bzcOYfDQe1wdM2wkY4iBBvmFouFOC6TyUTHhTwiimLFYhHb7VbXB3J4ttst7u/vEY1G1Q5HsTscDov7MxqNVE/OYw68i96lUinc3NzoGNBxRNZSsViExWJBqVSSO4eOzEgkgi+++AKnp6d6/mSgLZdLJBIJmM1mFAoFXF9fS6Tmddrn86FQKGCz2aDdbiMQCMilt1qtJGTZbDYBnVlvz2tzp9PB6ekpBoMBms0mYrGYWFCMcDmdTolCgUAAlUpFDWJut1tutEAgIGGXjizCs7/1rW/9Ij9jjTHGGGN+9eZwwLZUwfah/BfHFYwxxhhjjPm55r2FnH6/r0X+2EWTy+XQ7/exWCzQ6XTk/KAo0el0BKQkd8VkMmG9XqNUKiGfzyv2MplMMBgMtCTS3cMoCd0N/CWdIGByVeg0AaDHyeYeumT4/Rlb4lI3mUwwHo9VoU6nA50hbJYZDAY4OTlRJTUdA9fX1wiHwzgcDqrPDoVCAgUz5kKmA+8QcyFltMtiscBkMinKxTgD40BkrrDFaTAYSLDo9Xq4uLgQOJhuBAJ96Qjgsr1arZDL5QC8i8CNRiOEw2G5ePhv6eSxWCyKJAWDQcGG7+/vFduZz+daiFhTTCeD3++X+EbHgt/vl8hnMpkQi8XQbrfFUvF4PIjFYnj16hV8Ph8sFouAr2xBqlarKBQKqiG2WCxotVpwu92KYpBVYrVa9djb7bZElN1up7/n8W42m3IVRaNRnTdutxuZTAYPDw8S8dhSxCYc/hnB07z7z6gHBSbG2yhUHA4HPZ5cLodut6umoN1uh2w2K0cSq+XpDstms+j3+4pc0VVFRhE5UZPJRPGmWCwGs9ms14oAWsJfKSqxSWk0GglIzPORImaz2dTnMLpHdtF8Pkc2mxWYuVar4enTp3LEdLtdtUUFAgF8//vf17lJEPVxuxgXfEZh+D0oIG42G9RqNYGY/X6/HBf9fv+RwEKoOV1pdJsAQLValfOErUb39/cYDodIp9OYzWb46KOPsFgs0G63FdFioxS5NgQc003EWE8ul1PEjEwfNqNR9CQomXXYdNB4PB4EAgGUSiVYLBZEo1Hc3t7CbDarYYrXXfKe/vT1iO5Cxv0Ys5zNZri9vVUckhBvissUkcbjMU5OTsReYluc1+uFxWJBrVYTfHu/3+s1mM1mWCwWirglEgmEQiG0Wi0Jc3RRUZg3mUw6jmQILZdLZLNZcdsIneZ7gqDweDyOSqWiaznB13a7XdclcqQikQiazSZarRaSySTK5fIv/IPWGGOMMcYYY4wxxhhjvsr5mYQcVmszHsCldTAY4Fvf+pbcC+QWWCwWBAIB1Go19Pt9RWz4eWyWYh04K5gpFPAX68Ph8IgbwjgHXUJcJnnHnzZ8Rl4IyX3x4gU6nY5iPlxaarUaHA6H7gYf3+Unp4HxBjJBeFd5t9upqYlxL8Zo6EigIMDK481mI4cJgauMu3A5559dX1+L88Fq48PhgGq1inw+j3Q6jd1uh7u7O70+x8BWxrvoajKZTKpoDgaDePv2Lfx+P3K5HNbrtSqTj+umAcjxwWM+nU4FBHY6nYpXMOJDdpLH40EwGMRwOJTjYbfbCR5LSCojFKwfDgQCcDgcuLu7g9PpRDKZRCqVkkBwOBxwdXUFh8MhZ81+v0en04HT6cSzZ89QLpdxf38vFwRhv8d16hSNXrx4IScN43Jut1uxLjqvGBE5rnxut9/lvkOhkMDSm80GrVYLkUhE8NpIJIL5fC6RhK1cTqdTsOrdbgeHwwGPx4NSqYRsNitYayQSkfBQr9dhMpmQTCZRr9e1GNtsNj13QnLZ+ETxh4KVxWLR+5pOllAohGKxiH6/j0AggHa7jf1+j0gkgtvbW/j9fvGvTCaTBDLGlgjyZuyN79doNIqXL18KvHx2dibILBkt+/1erqhIJILJZCLxh8IjHXztdlsV8gQiR6NRhMNhubyePXuGN2/eyPlGwfPh4QFutxv5fB6TyUTncjwelzNwOBxiOBxKTGJlPblT8Xgcr1+/xvn5ubhEAOR6IvR7Pp9jNpthuVwiGo2qOYn17rxefvbZZ2ruo5MQgDhhyWQSjUYDkUgE/X5fDKdqtSqwO4XrUqmE0WgkgYVOpMlk8ug1IgeLTJ90Oq02rH6/r4aoh4cHpFIpRCIRJBIJvH37VpXjx0Lv/f0763wul4PH45ELCsAjaHmlUoHb7cZsNkMymVSV+Xq9VjyzUqng448/VkSP7WpsH6SQfczO4fc6jpo6HA4dT7ayEapOCPZms0E2m9U1rN/vw2Qy4YMPPsDr16/lAjPGGGOMMcYYY4wx5i8es8cDazEPayr5dT+Ub/S8t5ATDAbh9/sF+ywUCqr8HQ6Hik9wcSFzZjqdYrvdIpVKPYpmlctlvHjxAg6HQ9Bitly122216lDw4dLDWAHdBrTJk4OQSCRQqVTg9XpxfX2N09NTNQvd3NwgEonINRSPx3F9fS2OBKu5eaeZd925PPMONeNfg8EA0+lUDBrGiCaTiVwfh8NBkYMXL15ooV4sFmpPqlQqjxwcXArZskXXBf8thQWKTVyAKpWKwLt0mBAWvd/v0ev14HQ6FSOiK2Q+n6Pdbiv2xRYuOqMoRJD3Afx4MTteosi8YMuS3+8XX8JsNgsyfSzQ7Pd7QUq5GAIQgwcAEokE9vu9gLu82x6LxXB3d6d2m2KxCK/XKxElGAwilUopjkTo8HQ6xW63k9vJ6/WKt0K2DhuofD4fxuMxxuMxgsGgQLB0S9AdRpGNTVtkeDQaDYkmjE/R7UFgbL/fx/Pnz/Ua03Xg9XoxGo0eLeA8ts+fP8dyuRTD5rj9jdwnVkgfDgc1PbEJinXXjF+xZpmRL4qpXGzp3OG5R5cbBTe+L+v1uuJvFDfJ9yGE+eLiApVKRRwgumnYesfjCLwTMuiCIldnOp1KwCXsmewUgrgBqKWo3W4rcsYGqcFgINGXUR4KyXS+0KmXy+UkLjDaAwBnZ2d4+/atPpecmN1uJyGM53g6nVa9936/h9/vV2vUer3G6emprpusSGf8kxXe+XwepVJJLppsNgu73a7nNxqNBEFnOx0F7uMYKM8/p9P5iKd1fX0t1lg6nVbU7jvf+Q4+++wzdLtdRKNRccAImX94eECxWJSThSwcCkaRSATlclnXisvLS8Wljq9Ds9kM7XYb8XgcuVwOV1dXqgYn0JtxN14rya+iQHZ2doZ2u61453Q6RbfbffRzhA4ott3tdju5dSgAxWIxOUB53hljjDHGGGOMMcYY8xfP4dkJ7v+nWVT+4SksF6df98P5xs57Czl2ux2vXr1CKpXCycmJHDlsI7FarYKp0tLP1qJ4PK7oEu+SWq1W1Ot12O12dDoduN1unJ+fa1EmzJO8mM1mg1wup1gX4Z3D4RDhcFhLGONbi8UCHo9HjU/BYBC9Xk98EcYMPB6PYLPhcFitNWx4eXh4UJNKLBaD1WrV3V0uNJ9//jkcDoeqgg+HAw6Hgxg1jDZwySWwmG6j8/NzlEolzOdziS+TyQQul+sR8JeQZ7qEGNug44hMDjJZjhtiRqMRfD6foil+v1+VxuFwWO09/B6VSkVwWr/fj2q1qkiP0+mUo4b15rPZDLPZTO6lWCymJqRwOAyPx6MlqdVqIZfLKcbG48u2K1Y3U7jr9/t6jQk65TnAGuXVaoVarSa+jtfrRTgcxt3dHaxWK4B3YiS5GoTscgFnUxU5Gscuqel0Cq/Xi4eHBwwGAwSDQUVhWM/OBqjFYoFms4ndboePPvoIs9kMk8kEHo9HrT4Wi0VgZb4/SqWSmtC4BPM4U0zisZnNZhiPx4rLEehM+LLJZNJrtV6v1fBGQDFbf47jcuPxGE+fPoXP55OYQhcVWVgUTSjmHQ4HhEIhDIdDfS1GBel8m8/nuLi4kPvh4eFBX5MiI908AHT+kM1E5xxB0nS2UXQMhUIS+MiDAYB4PI43b97g+fPngjazqt3r9Spyxe8JvHOdUQRghXUoFMLr16/h8XgQjUbR6XTkRqEA+fDwALvdju12q7ganVAAJM7ZbDbFL3u9nqDh5ATN53NkMhlFMP1+vyJwyWRSzj6Px4PZbCZ3UrFYVNyKEcX9fv+oNY5OOvKy2NBnNpuRz+fFCWJrFoHcFE0oiJM5xrruV69e4ezsTEJLoVDQuUjXC4V0RiP/NMeJleXHbWy1Wk2xXUZoyWHjdYU8LTJwCN8/PT1FNBpFpVKRWEXQ+nA4hNfrVQTW4XComYo/czwej6JsjIoZY4wxxhhjjDHGGPOzzdYJbNKBr/thfGPnZxJynjx5gvV6jdevX+tuLKuuzWazmnTYCuR0OlGtVnUHuVAooFQqqf2JCzdbXBqNBuLxuACy/HPWgZNTQ94FBQAyVugooJOAiwljGbFYTNENxmIYAWBkhF9rPB7D5/OJycOo2HA4RCKR0BIei8WQSCS0fHCRZlMVFxW6FwhaZhSCNdo+n0+PNRaLPVqayOjY7XZqzSqXyzCZTBgOh4KnUvRIp9NqnDo/P1cMjLXJBAR7vV5kMhlUKhW9JowlMMaz3+9RrVZxcXGh2NnhcBCro16vK/oSjUaxWq3g8Xjw+eefw+v1Ih6Po9PpCPDLiATwrrHI5XIhFApJ7NhsNpjP5zpGXEYbjQYsFosic263W8LUd7/7XUQiEQl5tVpN9c10zrDSmsvxs2fPBJ8Nh8PodrtotVoSnLhY0mGRTCZhs9kUGWPjE11JFMeAd04qigc8R6LRKCaTCbxeLyqVirgfdEgQ5joej/HixQvU63VF9zweD5rNpmJ9s9kMqVQK1WoVHo9Hr5PVakUymRQHh+4mRgItFgsajYacPWQz0cU0mUz0XOiYoGuI7iAAgm7T0QZA/Bq2ANElFo1G8fbtWwmbqVRKoiUjW36/Xy4Ot9sNs9ms1rnpdIpkMqmaeAoGvOZ88cUX8Hg84px4vV6cnp5KhLq+vkYsFkMul0Oz2cRsNsPZ2ZkcfN1uF6lUSs/z5OREzjaXy4VKpYKnT58KuMxzkDyaYDAo8Zbi9dXVlYRRuvXm87kEBALDyRYLhUJ67W9ubuDz+RAIBPQ+Y/U4j6vT6ZSzCgCur6/FYeJ1hNeOZrOJs7MzNBoNCYEUQ8kC++yzz+Teokspm83i4eEB6/UaXq/3UWsVhR6Xy4V4PI7tdiu30mg0kqhCJwtda16vF/P5HLe3t+JpxeNxVKtViWR00qxWK4xGIx0zl8uFVquF4XCIZDIJl8uF5XKJWCwmNhOFrN1uJyHYarVKLGs0GvrZwPOWbjJe7/leoCvO7XbLpWSMMcYYY4wxxhhjzF88lt4E4S92SH13BfPv//Drfjjf2HlvIYd30XlXm2JAsVhUXKTf7wN4dye/3W4jGo3i8vISzWZT9v3BYKBoEt0jbLliDTarfAOBgJbLZDIpfgy5M3R78Jd9smHIlfB4PDgcDuh0Oo/ahLxer54D66CBd7/UA1B8iaKG2+2Wm4h3eNmGs91utYCenJwIFDudTrVMMcJjsVjkhGg2m3jy5ImWa7qU2HJVLBaRy+Xw6aefIhgMotFoPGIQOZ1OMVAYE6NQ0+v18OLFCyyXS1SrVbXXmEwmXF5eqr2LLUl0ffCxt9tthEIhiWVnZ2e4urrCYDCAxWJBMBjU8YhGo2JIECRrs9n0eLrdrrgeZrMZ2+1WkGjWy1cqFSSTSUwmE2QyGQDvhAHgneujVqshk8kIAssI1nHzER8Ha+sPhwOazSYymQyGw6FgrnzdbDabjufNzQ3C4bCWYa/Xi+VyqcWPgFaKb4zL0RFBoY68J3JSIpGIolC9Xg+JRAKz2Uxf43A4IJFIoFQq4eLiArPZDAAeuT5GoxFmsxk++OAD1YhHIhF0Oh218jDSw9p2Pn6eK+VyWXDgRCIBm82G5XKJ9XoNh8OhOmjCjinSHUN4KSIw+shYH5vLjp8TW+ym06mapfr9Pg6HA6bTKbLZLEwmk2JybCebTqdiMpFzxXYpiqQUPX70ox/pteH7fr/fK/o5n89hsVgEjCYryuVyCWDsdrvhdDrFTdput2i327oOUEg8vm5QEOX7huf1sWjLenO73Y71ei0uC899v98v/pfNZhO4naINK7TNZjMGgwEajQacTqfE1Ha7/UgcYUsVRYzNZiMuFl93ijQUP3ltiMfjmM/nSKfTErkdDgeazSaePXuG8XgsQZRV9g6HA8nku8yz1WpFuVxWSxgZWV6vF/1+XywjxjsZt6rX6wKqs16ckT2z2SzRkqBkvjcSiYRchhRr2bBGIDfdgeFwGC9fvnx0flOIJiiaDXd0BNJlR3dfo9FAIpH4uX64GmOMMcYYY4wxxvwqzva+BM//83tYhq0wf+s58KWr2pivdt5byHE6nWo5WSwWcLlc4mO8evVKERHGBIB3wkyr1UIqlYLf78fV1RVSqZSaagBgMBjAZDKJI8K4EX9JZy0wP5cuD4/HI1cCox1csBnXWa/XWrC4SOx2OyQSCTltWH1L5g4bbygM0SnC+ApBy8fMGIJdGdcwm81qYyIImBEyLjbJZFILJh0tdKLQAUQGSLvdlntiOp1iMpmoXef4GNLtMhgMdBebMTWv1yvHFBuS2Nzj8XhQr9dRKBQEcyWvaDab4e7uDul0GicnJ7i7u0Or1VLcg4yJbDYr0YvVxwShRqNRtFqtR9Ehfn2+7lyoWE+82Wx0HBmxInOJz5O15dlsFm/evIHD4RDPpNFoCAS93W5V3w5AMZRIJIJut4tkMvkIXGy32wWtrtfrEgbYYNXr9RAKhbREv3nzRgs4AGQyGdhsNjw8PGCz2SCRSChO4/P51JwDvBNtcrkc6vW6zj+r1YpQKKToS6/XU7U4mSMUASqVChaLhZrUuBiTTcXaaLJEGEfabDbi0EwmE71/6DBidI7il8PhwGg0QjabRbPZlIOFxyOZTOoxApBYRw5UOBxWo1YkEoHT6ZSDyu/34/7+HrvdDp1OR8eo2WxKxKRYSQGW7hwAqlan+4oCajAYlHDW7/fV5EQhg5wXioRms1kA4NVqJacWz0O/34/b21udP263G16vF5FIRI12gUBAUSVWiwPAs2fPJOgNBgOYzWZFH+v1OqLRqEQiVp17vV4sFgtFpwiH3m63AlaXy2XY7XZFIul2I+T4gw8+kBjz7NkzdDodfPHFF/D7/bi8vES5XJbwRzAwBb1SqaRjxvY1Ouva7bZEeQASZKPRKLxeL7rdLgKBAHK5nIDuh8MBACQeUtiOxWLvfhh9GXutVqv6mUCHEZ2Ey+VSQjEFHF53BoMBer0e1us1nj59CofDAZvNJl4VXYusF+f7/fhau1gskEgkdE1yOp3odDrv+2PSGGOMMcYYY4wx5ld7zBZs/rufoPbv/VsYPLWg/PeCaP4vfwuW8xOYDZfzVzrvLeQQWsq79LTB39zcIJFIiAFC6CldNx6PB61WS7yI40WRi7jVapWbggtev9+H3W5XLIaOHrIkuGwQXsz4Ahf9QCAgQSYYDCoawLvtnU5HEFZGOCgUMG61Xq9VO+10OlEul9VIk0qlMJ/P0Wq1tKDUajVcXFygXq8jn8/D4XCgXq/DZrMp7pRIJPD555/LDcSlkcIEXSfRaBTlchmJRAKBQAD1eh1Wq1Vfx2w24+7uDufn5xgOh7pjTUeAw+HQAuzxeLBarRCJRDAcDhW1Go1GgqGyleoY4Au8c9mYTCacnp6i3++LsWO1WtHr9eSWGI/Hcoek02mJVmx54t1vCjh0G5A7RNYSXzcuiGy6YqU7Acx0/NBRE4/HBYOmCORwOHB7e4tsNqu/53nLYzabzVQtTcYLhSRGKsjgoaOCr8N6vUaj0UAymcRoNEIsFlOMj806jO8Ui0WMx2OJdbPZTDBbRqvYmsRz/djZQXAv+TgEBPv9fthsNoxGI2QyGQG4Y7EYYrGYGngYFyNTZz6fY7PZPOLFHDvTfD6fuDL8Xqxc5znFBjIylmq1GjabjThCxzwSt9utSvTJZKJjRGGYbVp8PDyf+v2+nFJsPGLkcTAY6BwdDAZqm3O73aq4/8EPfoCTkxMJawSl85pFFyDh3eS3pNNptNttsY+GwyE6nQ4ymcwj5wffT6xXbzabcDqdWCwWyGQyEqRvb2/FyPF4POj1egDeCeSxWEwuE4fDgclkInB6Pp/H7e0tfD6fhCy6wehIpHBKxxMdjIfDQeKK0+nEZDKBxWJBKBRSBXilUkEul1NksdPpKO7FuCmdiRRNTCaTXIaz2QzpdFoxqMlkIqHJZDLJ7XQcaaU7iDHSu7s7XVcOhwMikYjcX4wChsNhibk8b9ksRleW3++XOFSr1bBYLPDkyRNF2KxWqyDfJycn2O/3cukkk0mMx2N0u10Ja4xb8X1hjDHGGGOMMcYYY8yfP7vf+RjN33Jg/44AgIMF2PiAh/9xCjjPf70P7hs27/0bKutruRTE43E5K/hLNXkQm80G7XZbzhreReZyBbwTPaLRqO7sE5QbCAR059Tr9eLm5gYnJycCIxNaabFY0Gq1JDBZLBakUins93sMBgN0Oh05gShMdLtdwVFdLpeEBj6+zz//HL/5m78Jk8kkJw8dMHQJhMNhDAYDPDw8aAkDfuxqICS0UqkgGAwiHA6r3pfRBDoAOp0OotGolu1cLqc7wNPpFCcnJ4qr8e49o2JctHq9nqDEhHQSaMo/K5fLEke63S6CwaDAvmzXAaDl3ufzyclApwoXaLYnMfbW7/eRzWZVKU5Q83K5RDqdhtlsxu3trVqpKJawYpwLLB8Dl0S6CHg8z87OYLFYxE7i4yRDho1NdEkw5sWaYoo4XNAo8FFYIGfD6XRiNps9Whivrq6QSCTkRKOTiuwcupy4zHc6HbhcLtTrdXz7299Gu93G3d0dcrkc9vu9Ylp0L3GB9fl8EiNYf87oDSG2rC6nK4jxumw2i+FwKE4MXUjFYlFQaUavut0unjx5gslkglqtJo4PYcidTkfONQqfBDlTpGV0kU63UqmkpiOylPi+ojgVj8cxHo8lmHi9XkWeKGIxtrVcLpHL5eTeW6/XilMRZk7hhC1sjGXx/c/3VK/XE7/IbDYjlUrh1atXGA6H+OSTTzCbzdDpdBSBCoVCGAwG8Pl8ACAmDs/rw+Gg6CibkMiWIXCaQiLh1vl8Hr1eD+12W5EuxvLq9bpcVJFIBKlUSkIXgecWi0XXVFZmE5jM78XPWy6XYlWZTCa5C9vttq7Xm80GLpcLT58+RbfbFeA7Ho8rnkU2TqVSwenpKU5OTnBzc6PXjW46ipdkMvF5H3OBGLezWq1otVrw+/2wWq24u7uTc4r8Gta2s0GM0dhCoYBms4l4PI6HhwdYLBYkk0mUSiXs93vFEclposgzHA4FL2czFYHebHSz2Ww6LynM+3w+QZGNMcYYY4wxxhhjjPnz5/A3voXGbzp/6t/X/3YIpi93C2N+8XlvIYc1xLSkEz5J6z2dJHQ2ED5qtVpliWdTksvl0l1+/jfjSgT52u12mEwmXFxcoNFo6I4z75gzCkT2hM1mEyD1mK/DpYrCAQAtBj6fT1W8rGCu1+vimlA0OAb2sm6Yd4fJtyDsl7wHHi8uuWz4OT09xWKxUOvK3d0dCoWCxJPVaoVcLqeGGUKkKUYcDgdVFU+nU4xGIy1tbLaiy8dms6HVaiEQCKgtKpPJqNWKTgc6gbgMAkAgEECtVsN8PsfJycmjpp14PI5Wq4VIJCIOCJe60WiETqeDjz76SK8vocyMsLGinuIOBZn5fK74HJkkjKt88cUXODk5UY0whYPJZIJUKqUlvFKpKGpC9wDjX2RysEWMDhUuchQECJemOOT3+/Ua7HY7pFIpLJfLR802dNrc3d3B6/WqvYhAXC6Ufr9fvCKv1wuv1ytGzP39PU5OTrBcLlVjznOPsGEyXNLptFwWiUQCtVpNQGQ2Cnm9XrTbbcRiMcWmhsOhnCjZbFbOEC7uBL0ySub1evH555/jyZMnEoPI0ZlMJsjn86r8JhiX3CkCdRmn6/f7AgKzcej4vRaPx1UDvl6vsVwuJc5uNhtYrVYkEgkJmc1mU7XoFL/YkEQwOeuol8uloOTD4RCXl5cSCxndSafTyGQy6Ha7Ev72+71ibgAUpSSDic5DOsboLvvkk090rdtsNqjVaojH40ilUmg2m4pTsj0qEAjg6dOnaDQaElF53Z1MJrqOEpYdi8UElx6PxwCgSNZyuVRDFMG/ZM3sdjs11TGSCUDikdPpRK/Xw2KxEAfo29/+Nv7oj/4Ibrcbz58/x83Njc734+dP4cvlcmG73QrEDkACKVvPyMQ5Fsna7TZMJhMymYxihTyfhsOhmrzYijgajRCJRHRuE0Lf7Xbl2iJAn+8VCpB8H5Hrs1wuFVdMJBJ6LSeTCUKh0C/0Q9YYY4wxxhhjjDHmV2G2HisO1p/+9xsP9LunMb/4vLeQQzcBrfTD4VBRFv5Sv1gs1N5DCz/jN7TlU1xIJBJotVo4OTlRTIPVw3QDDIdDcUH4fQDol+vRaCQIJ636XCyq1aqYJscAXKvVCr/fL0eN1WrVEsqlYDab6Ws/e/YM1WpVMY90Oi0BgGIGBZFms4nJZIJYLIbpdKrqcNYmcxnJZDJqwcpmszgcDo9cM7wrzSYgxk1sNhsGg4GgugD0HAkRpdhzLALxTjX5KHR9sFWn0+ng7OwMy+VSLVQE0VI4Y406obLb7VaLFQAxQRKJBF68eIFyuawFiUISAdVsiiGziG6TYrEIk8mEarUq4C4dWicnJyiVShIaGLOjG4s1xRQxGNkLh8Pw+/3o9/s6hxhTohOKUbxarSb+zt3dHZ49ewaz2awo1Hg8Vj0yhSECeNmwRmDqbDbDfr8X2BYA2u22BK9UKqX4IOMddD+x+YznOgDVe08mEzmJWHFN+DXjTP1+X41a/Np8juPxWHXKVqsVl5eXGAwGEgFYH042UaVSUbMQX3/ClRltCgaDapJrt9sSsiiwjEYjLfUUOsnpGQwGjxw8FEdZ171er9Hv95FKpeB2u/Hq1SsJdQRT93o9OBwORTjpRJtOpzqHY7EYKpUK3G63nEWhUEgCDJuS6F7hORkOhxWpzGaz8Hg8uL6+VuMS28Ly+by4NIlEQuwtugfpTDqG89psNoRCIUQiEZhMJpTLZV3XeAwp+rVaLazXa9hsNqRSKTnxVquVhFDGBMPhMG5vb2E2m9Fut5FOp3VdYJyv1Wqpop5NURRceR2iu46iyM3Nja5fvJ4BkDhHdxBFuEQiIbcaHW50ydFBRYHObrdjMplISD05OcF8Pke321WLoc/nk8jEny/8mUIWGd8HdJAmk0kJR81mUzcifD6fHhtjsoRY09lEsZGxYGOMMcYYY4wxxhhjjPmrMu8t5PCOfSAQwP39PSKRiBYxl8uFQqEgsYCLPgUFVsYuFgstCKPRCMFgEA8PD2LI0JXBX9iLxSIajYaiFgRQxmIxgT9ZUzudTvHRRx9hsVjg4eEBZ2dnitys12v9Qk5hhXDa0WiEs7MzANAd/XA4jMvLS0wmE7RaLfT7fTidTsVIGPdhpTEjNxSduLCRgxOPx1XtDQD39/fw+/3wer2CzJrNZoTDYex2O7x9+xaRSATAuwga7z6T12C323F9fY1oNIp0Oi2HDRcqCgK8i0/HVD6fl/uGDpbxeIxisYhyuQyXy4X1eq3F3uVyCdTL14lskkgkorvmZEicn5+jXq9jNBqplYfuGi5eBFMTYMwWL8YpLBaLnDQUqwCoupvsnvv7e4TDYbk6uJBRgAKgmAtrio/hvjabDXa7XeIgo0UUrtxuN374wx8iFAqpuSmVSuH169coFAqYTCZYLBY618kHIt9lvV5LECoWi+j3+ygWi2g2m/B6veIi0THGCMdqtUIwGJQDh8eBbitCg9vttuJIbE9KJpOIRqNqfvP5fPD7/eI0bbdbJBIJJBIJOafISaIYypik2+1GtVrFyckJOp0O4vE4er2eGn+sVqvENr7X+f7ie2y5XKo5LBwO49WrV7puEORLMYXPP5/PS3wcDAbw+/1oNpuKXbJiutvt6r/pVuPXJiSXoHCKMcFgUJGz+XyOQqEAi8WC2Wz2SDydTqdIJBLiBPl8PqxWK5hMJvR6PYTDYaRSKWy3W3S7XdjtdozHY4TDYfG+ZrMZer0e7Ha73F50ClGoYaTUarViNBrJHRgIBPT96DL0+/2IRqOK9iWTSVSrVTgcDsRiMVxdXQmGzuMRCoWQSqWwWCz0eI5Bw3a7HU6nE6VSSQ4xcqroAuPrRIGNsa12u40PP/wQ2+0Wk8lEjKtEIqFrNq8d5GGdnp4KPE2u0JMnT/Dw8ACr1ap4IIV1uteGw6GEMooydBrZbDbkcjmMRiMJrGQJkcdGCL7b7Ra43OVyyYHJn1sejwcXFxf6+UYWFV8XY4wxxhhjjDHGGGN++tj7S5g3P/3vnf0DDjvj96qvat5byGHzx/39Pfb7PYbDIZ48eYLtdquFtNVqIZ/PYzqdavk8PT1VK4jZbBZ3ZTabCYzMX9yXy6Vqdx0OByqVingVvNPMthmCXReLBYLBIJxOJ2q1mtwBm81GLgoKBcA7F0IkEpEg4PF4cHd3B6vVimKxiFarpUiV3+9X7e/d3Z0Wbt6hJReDd4NZd867zwRsso6aDhVCccnf4dLNJqNUKoVGo4HFYoGnT5+qqn04HKoCnHf6KTAdV6QfVyLzWLBVCABarRZisZgWu+N6X8ay6ELiIhONRiW0cQmiU4BclsPhINcWYbLHwgqrjT0ejwQq3v2m+EEXAx0HjC1xQQXeuZA+/PBD/OhHP4LP55MAMp1O5ZhhDIfQ6maziYuLC1itVgyHQywWC7mc7Ha7mnK46PKxk0HDhi8yjY6dI1xw2dpGB1OtVlOch1G7fD6v16lcLsv5Qn4QHUEUSPheoPgIQEwRut3I06HjiGDg0WgkiO1wOEQkEsFoNNJ5S+YUWUsUIshToZBxenqKh4eHRwBqcpnoSFkul4qjECpOIY/CJaNAZrNZjBM+P7rb2DhFNg+ht/P5XN+XQiABzoQp0wF1OBzQarXEcWF8j/XXTqdTcFsKZPv9Xh9ut1vvIdbDE94LAP1+H9FoFMvlErvdTo+Psb/xeCzh9djVQbGKjhe2mA0GA4RCIQQCAQklPIf47w6Hg84jcneSyaQ4VYQbsx3K6XQq5sr3ZSaTkcOK4ifh42zF2mw2SKVSEpocDgfu7u6wWCxQKBR0zmQyGdzf32MymWA8HuPy8lLNUXwdTSYTarUaIpGIomJ2ux0XFxeKx47HYzkF6Wyka4ZOSp5n/DlEsY6PmZEwXk8Yjw2FQnC5XGg0GnrNZrMZ8vk8NpuNXJGTyQRPnjzRtYIA7GNgtDHGGGOMMcYYY4wxf/4cvvcS6d+f/cS/c7UPSP7zGxw261/yo/rmzs/UWsX4g9frRb1e1zJBBwrbbg6HA96+fasl/XjB2263qneezWaKnXA5IFS33W7LLcCIhs32Dn9NC7/b7RbjZTgcotFoIBgMCkBrtVoFBmVsxmKxYL1eo1KpwOVy4dmzZ2o4IVSXteFc0FqtlpY4slcCgQCcTqcWfd7tvbq6wvn5OQBImKGtn6wGxhrIfojH41gsFtjtdqptzmQyql4n14IuAN7l5zEiR4KOA3IfLBYLGo0GbDabnAObzQaxWEzxBE4ikUCz2cR8PhfXIhgMotvt4nA4PAJNE94ciUTklnI6najX6/B6vXqNGMHhUstYznQ61WtUr9exXC4lEjidTkQiEXg8Hrx580bcJK/XK4GDDA6KiK1WS5ExRi1CoRDK5bIWbIvFosaqZDIJm82mSm26hACo9YoCI89ZvtZsDFutVlou3W634kfRaBTT6RTVahWXl5cSLyeTiRrFGEmkm4HxMR5fLr1so3K5XFitVmr5oQhKoLbVakUmk9E5nkwmxQChwEW4Ld8PdC2w8j2fz+Pm5gY2mw3RaBS9Xg+ZTEaAXbol1us1kskk6vW6BACej7vdTo+XbqtQKCSR0O12q7qetd0/+tGPYLVaVVHP9qL1eo1UKoVOpyPRy+l0olAoSNyl22OxWIi5wvcMBTlGvFhlTrH4GLrM92koFNLr7/V6MR6PBWOm6OHxeBCPx3F7e4vlcqnPS6VSauMiH6ndbiOfz2M0GqHRaOi9xugiXXcAxGMZDAZIJBJiNPG6x3YzitrD4VDMqmg0KrcP69uPY2KTyUT8omg0KpGW52UsFpNzju+XxWIhyG8ymVQz2Hg8xpMnT3TdIf9st9spFsZ/M5/PkUwmcXV1pYar1WolsS4SicDv9yMUCqHRaMgtSOcdBfvpdIrb21u5Hglk5nWi2Wzq+5rNZgmGk8kEjUYDp6ener9TmCHfyu12I5lM4v7+Xu4ucsDYjJjNZt/3x6QxxhhjjDHGGGPMr/SYvvsZsv/1DLYJ9OHsHJD4p6+w63S+7of3jZr3FnJms5mYHslkEolEAldXV2ofYXTo9vYWh8MBgUAAXq8Xt7e3goOuViv4/X5BPilmUKBhtMblcumX59vbWzgcDkFOeXeZSworbwmypcuEsQQu9owC0dlwcXGBwWCAVqul+lz+sj8ej8Um4eO7vLyE3W7H27dv9bgZiSBHBQDS6TTK5bIAzoPBQJXex9EgLtyHwwH9fl/w41gsBrfbjW63qwaq7XaLQqEgUYbtYD6fDw6HA7VaTWJRv9+Hw+HQ3WeKAMdRg1gspqgQ4aqMFDGKUywWJaAR9MuvweNCMYiMCS7grFJn5TlZK4St0ul03KAzGo2QSCRQr9dhMpl0d56vz+FwEDCZnA26axjb63Q6SCaTqsX2eDwC2XKJI5Om3W5LZLBarcjlcpjP56hUKnjy5IncZceA1tFohPPzc702PK/oLKALwuFwiAcDQDE2tlRtt1vs93vxpOjIstvtjzhTzWZTNfGpVAr39/dwOBw6Dt/5znfw9u1buN1usYdarZYWaAAYj8eK9LXbbYFzWTVN0W4wGKBQKCgiw1gi3WuMzzmdTpTLZbmW2DDG9xzfj7VaDdlsVpHB9XqN1WoliDdBwE+fPsVyucRsNsNms1F712AwUA30brdDsVjEdrtFtVpVRHM+nyOXyylCt9/v0e/3tYRvNhus12u5TxiHnM1mSCaTggBTQKM7BABWq5VcPhTP2OrV6/UQi8UkPlEQJGCdrK90Oo3r62sEAgE8f/4cm81GYjLFvs1mI2GGQi05UcA74ZjCHx1mjHHN53OEQiHFzOjQY/SKUHdyj8gpYjsaxRg6uXhdpfB2fA4Xi0W8fftWLVHb7VbXsYuLC4HGWTHv9/vx9u1bRfD4vgkEAqjX64oyvn79GtFoVE4cxh2PRSheJ9jEx2ND11YoFJJwx4gaY6j9fh/j8Vji83A4RKFQePfDz2zWcaCwSV4TK90JhzfGGGOMMcYYY4wx5j3mcIDpDz5F8v/wB/qI/R//ELsvf8c25qub9xZyCoWC4g10JPCuKzkKw+FQdb8ejwedTkd8DDp4GHPiIhwOh/Hw8ADg3eIQiUQwHA61aBOIyepwVj9zOQYg8SCff9dNX6lUtDRQUCkWi2i32xJV6CJpNpuIRCKCKw+HQ6TTaX1Pxn34vbg8kANCqz4blrbbrZbf5XIJm832KOrA/2aEgg6bYrGIbreru+zpdFoulOVyiWq1itFohBcvXqDdbus5My4VjUbF92EV9na7RSwW02M2mUxym3S7XcxmMzx58kR3woPBoKqB+TWPodB2ux12ux339/dit/j9ft25ZytMIBDAfr9Ht9uF1+tFKpWSE4rnSbfbFUCZbp3JZCJ3BONidLhw6WJMiw4jCgUE9H766acC9xJGyzvtkUjkUf3z1dUVYrEYTCYT6vU6PB6Pol1ms1mwbcbm6Dpi/I8iBhd8Ok5OT091PAg4TiQS6PV6qkdmbIeRsVarhWAwKBfGaDQSCwqAeCZcRKfT6aNa5UqlInZNoVB41FZExgpbjOhQoQjLVre3b98KjPzkyZNHn9Pv97Hb7TAYDJDJZOS4ocCwXC4fHU8KBASTE/rMyBfflzz3bDYbvF6vGqPo9rLb7RJxh8Oh4kqj0QiFQgG1Wg273U7CMR8XXXPHgOhj15Xdbke9XlftNV0om81GoOT9fg+TyYTlcqljSMcMxcB4PI5Go6FqbLaQnZycSARZLpeCH1Og47WNEG6ybyiYkGc1m80UmxuNRnC73frcY8h0uVxGs9nEycmJXFgWiwXNZhO5XA71eh2JREIMJMYD2RxHwQuA4ph0h1EYLRaLePPmjXhQq9VKvCme+2yE4zWDzrD7+3ucnZ1hu93C6/XK3cTrFuvCQ6EQdrudxDW+79g05XQ69bjowqHoBkCxPB67J0+eoF6vC17N15OvbSAQwHQ6hd/vRyQSwWAwUKSS4H065IwxxhhjjDHGGGOMMeavyry3kHPMryFwuFQqyR3AuAfv+NIh4nA4VE1OdwGrrukiYFyHESYyUdLpNJbLpcC5/MWc4FubzYZarSZnS7lcxnA4RCaTwcXFhdgpvMvOqBA5GYxxjEYjCQt0sgDvoiBmsxk+nw93d3c4HA6y+gOQWAO8484UCgXdjR+Px4oZ0WHR7/d1x3673WI6nSIWiwEAer0eotGo7tpTHCDkmfyPu7s7OJ1OBAIBrNdrzGYzwT7D4TBms5kWYy5gdE8MBgMxQABooXK73SiVSohGo4qOrVYr3Nzc4PT0VEsP4w5+vx8+n0/OIwKLY7EYNpsNOp0OVqsV0uk0zGazwKlmsxnT6VSPH4AcS4zx0IlD5wbFHjp+2CBFWDYjJ4yJMdJC9w+PxX6/R7vdRiKRkCjH1+FwOOj1dLvdaLfb8Hg8cnednZ3h7u5OopzD4YDVakWn05GT5bg9iU6R44pjMm/C4TBCoRBarZZgtRQwyZKq1+twu92IxWJa8ikqsdaZDgRGe7hAs7GHTiU6VAgUJsPpOMZEUS+dTiMUCmE8Hou/M5vNVAlPOHepVNL7Z7fbwWQyodvt4uOPP5YwEw6HxZni+xnAI7deuVxGKpWSwGg2m+V+4XuUDJh4PC7XT6/X03lBl02j0VDD0uXlpc5Dxji3260g3KPRCOPxGOfn5zrmg8EANpsNZ2dnilTR0cXrCAA1sH322Wew2+1wuVwIBALodrvY7/eIx+MIhUISNfnvVquVxJPnz5/j4eFBDkRyXyjAPH36FOVyGdFoVO/Vfr+v48ljxffSer0W94Zcmel0Kjccrwn9fh+bzQZutxu9Xg+JRAIOh0PtTnzdut2uXHV0q4xGIzSbTZyenmI2m6kZzGazoVwuw+FwqB3r5uZGXC2ykijCEgh/d3cnNxLPGbqlGBfr9XqYTqdyu5GL5nQ6sd1udU3l+3uz2YhNFg6HJQ6lUik9ZgrKJpNJLsdkMgmr1YrXr1/D6/Xq5xTfS+12+2f8sWqMMcYYY4wxxhjzKzpmCyzPL3/88eWua8xXP+8t5BwOB7kvyDkwm82y8B8OB6TTaXQ6HeRyObRaLeRyOUynU4kFdA2kUik4nU7FYQqFggQLNuy0223YbDZkMhkEg0GUSiV4PB50u12kUilxY8hX4OOjI6jRaMDv96uFpVwuq5WFbgjGWLrdLuLxuNq04vG4IJ5nZ2eK9wwGA/R6PR0Htjwd19uSxxIMBhUDY6vQMfCYteIErgaDQVVxA1AzFj+HSzSbqWq1muq3uQhTbKEAAkBxBLa27HY7VKtV/TeFEFa8UzyazWZyWAFQVTjbhAiZpYCw2WwksGUyGQl41WpVMR26YugKYh0yhRez2YzT01OJb4zkMDLEVqFEIoHdbodgMIjNZoN2uw2TyaQYFaNZ0+kUuVxOVeTJZBKvXr0SG4XV8WSI8DFQlIzH4xgOh5jP53I10ZXE8+jYcUUBgm4xwnwpJlGEcDqdMJlMeg8wrpPP58XKYawHgHg8fr8fk8lEAsJsNkMgEFBN9Hg8xnQ6hclkwsXFBZrNps4LvnfIlEkkElqIybohgJefXy6X5bpizMvlcgm2zAr7Xq+HYDD4qMGMDBoAcmEcDge9Zh6PRwwnp9OJ8/NzjEYjzGYzxGIxPVa+TqvVSkIA43ndbhej0UjnrNfrhd/vR6lU0nuTsc3BYCCeEK9ndJkR1r1cLjEcDuXsGY/HcLlcAKDXdbPZYL/f4/z8HC6XC6VSCXa7Xe9Zth85HA70ej2s12ucn5+LWTOdTtXa5HK54PP5JPQRED8ajSRmZjIZtb7xvU1wNt9XrCF3OBwol8uKvBFE3W63cXFxoQas2Wwmpg+dO6PRSEIpnyudaaxoT6fTcLlcEp143aPAG41GcXV1hUQigc1mI0GSsOPtdivXTjabVd05BW/G7XgO8Xp0cnKCVqulc4XRPzq0CFA/rmsvlUooFAp671MY52Pz+Xyo1WpqVqR7jdccAtgLhYIch8YYY4wxxhhjjDHG/PSxPL/E7O//Gh7+R1F9tP7+OVZ/99e/7of2jZz3FnL2+z1arZYW8lgsJgv9YrFANBrF3d0dPB4PGo0GstmsFuDD4QCXywWbzaYKY9Y+O51OWCwWlMtlLc9er1e/vJfLZSyXS7XGcDEdDod4/vy5IhhcZHknvVarYb/f4/nz52p9uru7QyKRUFsJHwNdKPP5HJvNBq1WC8ViUa4RLvpcDvf7PdLptJYgxs24kHc6HcV9GAMjoPnZs2dizjgcDnEs+v0+UqkUhsOhGCqsZqbAQOHL7Xbrg+wiLpEAcHp6ina7rWhWtVqVwEIgL+9sM+ZGdhHbmo7dF+v1Wm1WrJ9erVYoFouYTCb64MJK6DEdB2RcBAIBVVvTLURuUrvdRiwWUy02+RwENDMiRAGIcanBYKA2MXKSzs7OBJclL6RerwvCy9eFVfeMWgSDQdTrdcTjcQAQ04ULJQHPjD+R8VIulzGbzZDNZjEej8UL+fDDDyUy0S1BF1UmkxG8ma6NarUqx87d3Z3av3K5HJbLJcbjseKC5IBcX18rWsIGpUAggEqlAovFIgdHp9PBhx9+KFGI7qzVaqXldrVaPaoDZ6MT2UcUBabTqfhBh8NBYq7P5xNLhgBeq9Uq4ZeRJn4vxrYoiFAg4vvl9PRUgiWbt4B3kT9yTOjGcTqdahKji4gCLl934J3ryuv1ot1uIxKJCJLMc4uvfzKZVARtNpuhWCzC4XDIrTUcDjGZTPQ81+s1+v0+rFarXD7RaFSvdywWw2AwkGjk9XollJDHUqvVALwDBdO19oMf/ACTyQSFQkHfd7lcIp/PS0xhBCgQCCCdTmMymWA+n+u8o9hstVphMpkeiY98n3g8HkWI6KBkZIrXye12K+A2xVbCkWezGUKhEHw+n1r8zs/P0Wq1FGVlVLJUKqlt8NhFGIlE9LmMLt7f3ysSRgYSo2UEzIfDYTm33G43vF4vstksSqWSgMyJREKOzcvLS0ynU9RqNUynU6RSKR1LOtF4jSKPyxhjjDHGGGOMMcaYnz4mqxXTiyD6zyyP/nwRN6H3gQ2WL9Edxnx1895CDp0wHo8HlUpFkZD7+3vdFfX5fOj1erq7zbgSwZZcZvjLNZceu92u6mefz/dIMCKM1e12Y7lcwufzqW1lOp2i0+ng7OxMTTZkWaRSKdRqNS0/ZPlYLBZcXl7izZs3uivr8XgAvGNDkHvCxY/RMQouVqsVgUBAFcPhcFhuh2Aw+KhZiQJBu91WWxCdOavVCpvNBvl8Xu4VwqN5h5nODn5NNs8Mh8NHkE6yi3i3mq8VF33GW+j6OBYzzGYzBoOBIhYUpNhqQ2YKj+F4PEY2m5V4FQ6H0el0JD5YLBY142SzWdWzLxYLfR8+L5PJpEYcngd0H5EdNJ/P0el01FDDaEQsFlNMjSBdgnJrtRpSqZRiPBT/ptOpAMfT6VTNZjze3W5XogYdNccw72g0KtYThTW6J6bTqaDFyWRS348RD5PJJOAxWUo8NsPhUPE7MjzYoGSxWCTE0YnBiBSX0sPhoGhXJpPBzc2N4iuFQuGROy4UCqHZbMLlcqliHIDYNbvdTm1jjD31ej08ffoU9/f3sNlsOD8/l+hJOHYikUC329Uxo7ttPp8jkUgAeCcGl8tlRb3oJqFw4Pf7VT2fzWbR6XQQi8UEV06n0xIcHh4eJCjy2sJmJYvFIkGDXCS/3y8XkdVqhdVqFZeIbixCuFutloTOVColcO9yuRQkORgMijFzd3cnZxRdc+TipNNpubAIfWd8ifXwFHQLhQJmsxlarRbcbrfie3y/BQIBOUsYVSM0nUwki8WCfr+vKnO6ot68eYOzszPYbDZBpYfDoa4HdE0xtrdarSTODIdD1XmPRiNMp1NVrTNiZTKZ8Pr1a11LE4kEBoOB3Is8PwOBgGKLvK77fD7FnebzuURWr9eLdDqNWq2maxjPI8a4LBaLuEqMIlI09/l8EuYobNMV5HK5EAqFkMvlcH9/D6vVilQqhcVigbu7O4TDYdXa0yFkjDHGGGOMMcYY81d1Dr/9MXa/+52vTTCxpJLofmT9iX+3cwLjf/vZL/kRffPnvX9DZaSEv7iTbULAaqfTgcvlwng8VnSINdeLxUJxlWg0KghuLBYTAyEYDOqX+Gg0Kgs/oxOMJdCBc3p6ipcvX8LpdKJSqWA8HuPXf/3X1dxEeGWlUkE0GpXDhXEHLpvhcFhCCiudycEglJVLCxdfVgwz0tHtduUIoKuBrhpGclj/TEAnl+9GoyGwrcPhEGOFjo/FYoFarSaeBSMmxWIRy+VSjh1GRMhwoasCgOq4O53OI+Anwbw2mw2TyQTL5VLCByHQvIM/Ho/lRiGIerlcotfrIZfLSSDh3XTCpdvttirh+RrSlUM3AQUtLvOsBw6FQnr8XEDJVPL5fLi/v9ddeXKECNGmO4VxHboLyOzga8HlnQ6X6XSKXq8Hm80Gl8uFWq2mRZd37sn8cbvdgut6PB4tw3QkcYlOJBK4ubmROFcsFtFqteSKIM+IrTl2ux3VahWBQACBQEBtVwQdj0YjiYV8bDabDYVCQbXYHo8HmUwGn376qardGRtjo8/JyQnu7u4wGo2QTCbV2EM3C89Fnjur1UruNYpndEswisNo1+3trQQcwpQpqFE8qlQqEvmsViv8fr+iiQAU4fvoo49UW358jA6HA8bjMeLxOLrdrs6rfr+P7XaLdDoNABIXCO5lPIpOI4/Hg2q1Kjj2dDqVUN3pdNQCRiGOYhBbzi4uLjAcDgXuJeiddfM+n0/niNvtxqtXrwQzp1uK53Umk0Gr1dLzfPLkCcbjMQaDgYQLCsvRaBTBYBDX19cSt91ut65p5OHwWnJ9fY3xeCyRkHHE1WoloHcikUC73VbEc7FY4OTkROIeeTTkVlEsZhOWyWRCq9XScw4EAhKACJtPpVLic9FFyP/vdDrV1NVqtdR81+v15PZhW53X61WzndVqRaVSwYsXLzCbzVAqlRAKhXByciJ2FYUpgubp7KQTiO7JTCYjJ9BxU58xxhhjjDHGfFPGZLU++jDmr+9Ynl+i/49+C9W/7UH9bznR/IcvsPm3fw340pRgzDd33lvIMZlMik9kMhk1wwBQHS95K/wlfjweK1rBmEGz2RQ8lyBY3unmQkO2DBdVcmwCgYBcN4y5sAo8EAig2WyqgYaODo/Hg36/LwYPW3q63a4Wg3a7je985zt4+fIl/H4/vF6vGmBKpRL6/T4SiQQymYyYNrwrTRgp4aa08RNsTMcBIwRc7GjjLxaLSCaT+O53v4tnz57JCcA71tPpFB9++KFquU9OTrBcLrWwk/vBiBcdPWazGQ8PDwiHw1gsFoIyb7dbLBYLNSuxacdsNsNut+PNmzf44IMPkE6n0W63H8GA2UbE6ASZLIlEAna7XdDUfr8vF0kqlUK1WkW9XkcwGBTLgryPcDgsXke320W325Wow8Xb6XQKwEyxhuBXfk+32y3or9vtVm2xw+HQa9JoNFTbfAyP9vl8umtP0YeCz3HV/Nu3bwWWZeNauVzWeUiIMONnfNyTyQRPnz7FbreTc4fsDZ6HoVAI9/f3arSiYNhoNOD1esWJIWOEcUXG1o6jdGTmhMNhiVROpxO3t7dyW9Etk8vl1G6USqXQarUQi8VQr9cRCoX0XrRarXJn0BEVCoVwc3OD1WolllMkEsHNzQ2SyaSEl+VyKcB0Pp/XuX15eYnb21uEQiEJSHwevC4wjsdYGhdwRsbi8Tjq9ToikYhiYXa7HYPBAKvVCvF4XK47Cq0UiV0ul95vbrdbgFuKPBTJeK7RtcYhtLnX62G1Wgn83O/3EYlEEAwGFc8jOPj4XKUA5/f79XkUStiatdvtVKdOSDxjSQQQOxwOJJNJnJycoNlsYjqdotvtIhwOy/lIZ9nhcMDLly/h9XoRCoUeAb4ZkcxkMohEIjgcDliv16hWq3IC8Tmt12uEQiH0+31d21erFcLhsByYhEjzMRDwzmjYaDR61PTH9xtbrLLZrK5Z/X4fFxcXEs2n0ymm0ylWqxUymYzioPf39wgGg3LXvH79Ws63bDYrOLLJZNK5PZ/P8fz5c9zd3ek8JSOKIH5jjDHGGGOM+aaM+YOnGP+DX9PH5O//Gqyp5Nf9sIz5Ocby/BKl/2EUs4xJW/0yZkLzN+xY/b1fA8yWP/8LGPPXet5byGGldSQSQafTUZXxer1GMplUswfht+v1WrWuvOscDodhs9kQi8UU19lsNhiPxxJ16Djh1+FdU0aBaONnKw5FCIKS5/O5Ig/1el0MBNZKM3rASBCjSw8PD3jy5IlqZ1krzOhUr9cToJgtULvdTowJLqO8Q3xcG0x3Q6fT0R1nMk8mkwkcDgcikQharRbOz8/RaDTkmvF6vWi1WgKGTiYT7Pd7eDweuFwu3N3dIZPJYLvdIpfLKabTbDYV93A4HIIGs8qb7pbpdIpMJiMnTSgUQrVa1XGbz+fweDyq46bQQccVHTSdTgcXFxdqYiLclC01AOTOoiOGx2w0GqHRaEjgYp09F2NWBFOsYuyB7pDpdKrKYNbBA+/4S51OR0wZAIqGsJ2Gn2OxWOR4oPhBdkkwGJQoxnat+Xyu6Mix88nhcCCVSqHT6aDb7erYjcdjxGIxcYEYBSP7g6IfjxnP/36/L3eV2WyWC4jRmHg8rmgQ2T1k/Ry7MhhVcTqdEjO63e6jOBEdSx6PB4VCAZPJBLVaTY4ZttKRJWM2mxXhYZvZZrPBycmJYkwEyw4GA8Tjcdzd3WE6neLjjz9Gu92WaMUmqXw+L0gurwtkFgGQc4ywaZ5fx2Ila76HwyFqtZpEVIoLZNCMRiMByAk3DofD2Gw24mrt93tdhxg9I0CZUGA6uWKxmF5Lxp263a44PHQQMV5F4ZusHfKk2AJI8YXXNLpM6JjicXj69CkGgwH6/T6cTidCoZBA1XRMDgYDzGYzQd0JjaYbka4mu92O2WyG1Wql58NzfD6fS3ikAEKHJN8DZHxtt1tV0fNaSFGXsGteJ44rzpfLpUQYuj7H47He616vVzcGCCFmex55ThSVGeniOcbmODq+drud4lZsOCMbiceIgpExxhhjjDHGfBPG8uwCow+COJihj70VGP9WAdZM+ut+eMb8jNP+GxEcfpJWYwLa37bB7HL+0h7Lvj+A7+HwE//OvAH8n3Z+aY/lV2XeW8ghB6fVasmtALwTMMhoqVQqitGQJfPq1as/U6/MGtxms4nz83OEw2Hc3NwgkUiI68AaWbfbrRpkRnyi0SiazaZ+6QagRhs6gFiXPp1OBWHt9XqKZGw2GySTSQyHw0dLHR1F5NtwETs7O0Ov15MTablc6g7+2dkZ7HY7Op0OKpUKCoWCYKp0LbGOe7PZiBPE1pz5fC5hajweq22LDTMXFxeCRAOQuMK6Y4phTqcTXq8X3//+9xVfcDgc6Pf7GI/HuLy81EJTrVYVs6JgQdHpcDioWjufz+NwOGjpWSwWasA6HA5YrVYIBoMYDAZyj5jNZpydnenrcklqt9uIRqNYLpc4HA5oNpsCHFOQoBgwHA7V4kU2EQURuq1WqxU8Hg/G47FECAJ8eWedwOrjyBrrlAm95nmyXC5xdnaGarWqc42iVy6X04LPu/8U4uhaovPFbDYLjE2uDl0ihBnz/CTzhqwWnv/HyzgXXTYoEZLrcrlQr9d1HhHYut1utejSFUNXxGw2g8vlwn6/RyAQwJs3b8S6YSSLLjXybggR55CttFwuFYciFNhiscDn84k9RBYNmUanp6c4HA54+/at3BSECU+nU0wmEwld5KyQSWO1WhW1ZISJooDVapUAxPc2ozo8JhS1GO0ka8jhcMDlconZ5f8yW9xut9V2RibQMWeJ/BeycAid9vv96PV6Oi94LlKI5ONuNBqw2WyIRCJqXKKoFIvFdEz5HMmJyWQyePnyJdLpNLLZLD7//HNxr9gcl06nUalUUKlUAADFYhHhcFiCE/Cu0rzZbOq9l06nJb7RjUcXE2New+FQdeL8GUAAfDabVXPbdDrVtY1Oo+l0KqYTYdy8HjO2RjAz/269XiMSiSheSycjG8UOh4PcPYxm0q1I4Z5uqtvbW8XkeD1hZJNiGvls5BGRe2aMMcYYY4wx34RZJ3w4/ITtb+s0YZcKG3Gcb9iYw6Ef/7fbDWsmDWsmDfOX2IGvcvazGQK3czgGj//csgAK//c6dtd3X/n3/FWfn4niGAgEdDeeyxzdGeQ7bDYbvHr1SjwRNrSwPSmdTouZw+YoChyj0UhRGi5BXEB595rRGv6i3ev1tLwVi0XM53Mt+oR68u56t9tVLMRkMunxkZlAyz2rq0OhEDweD+7v77XsskFpMBggEono7jUALUNkiZDZQUeK2WxGp9PR0rzf7+U64CLDOmybzSZQKsUkn8+Hm5sbfPDBBwCgmnYeMzqQnj59iv1+j16vB7PZjHg8jl6vp2YbRiDMZrOiOry7zcWK8GT+N10gdGqQHRIIBLBYLCTIhMNhWCwWOWQ+/fRT3X0/Pn79fh8nJye4vr5WJTQXXsZNeGecToN+v4+nT5+K33E4HNDtdhUDoeuFDVWDwQCHwwG5XE7V8PF4XHXObIyiy4KMIToVuMSR/UMANO/qcwmn24rOkEajodgKm9ssFotg24zDLJdLpNNp3N7eotVqIZFIyAHRarUQCoXkMKBIQGcE41XHQhor7IPBIKrVKiKRCHw+H5rNJkajEbLZrEQRLv6MbO33eyyXS8GCKUaEw2GMRiOBzAlRPjs7g8vlUjOZ0+lEOp3G27dv0e12kclkBPnlsWaEjlBiv9+vym06aoLBIBKJBKxWqxxD7XZbbqsXL14oHsPoUa/XQzwef8TAqtfrYiXxesDzz+/3K9aTSCTUTkVWCiNIZFyxZY6CKx1ebEUKhUISIQjt3u/3as5jBTcdRKvVSq4dALpO8BgkEgnB1sn3IRSb4iI5XnQPMhpG5g/PYwqX+/1ex6HRaMDlcsmFxoa5drsNt9sNj8eDUCiEUqmkc8hms+H169d6zIxvMXZIYZpiot/vx2AwQCqVktOGzVyMVZIPxdfRYrEITm2323Vd5+NivIvRNQpNw+EQsVhMP0MIo3e73chms1itVqjVasjlcjCbzSiVSooz8vrL9yQFqdvbW+RyOVW2G2OMMcYYY8w3fUaXXli+5Bga8w0YE7A6+9LV/psfYfDvfoT7f1TE/T8qovcPPsLhb3zrq/+Wf/ApEv/J9xD/3kYfuX/Zwvbu4Sv/Xsb8DEIO71T2+30Ui0UAUB2x1WpFJpPBarXC9fW1FqJQKCTuwm63QyQSUfMSW18OhwNqtZrcMLTBJxIJgZC73S7MZrOabHhnniwV/qLNqBXwrmWL8SECPmm973a7ei7pdBoul0vVxowbkXMSjUaRSCTw8uVLLfCr1UoV1xaLBa1WSy4AAKoWN5vNWK1WKJfLustPMczpdMoRMx6P5YaZzWYCgtbrdTkyRqMRTCYTIpEIRqORWrEI7OUdZrpZ6D7w+XzYbre4uLjQ8srFZ7FYPILcAsDNzQ1yuZxcN2xCGo1Gci2RQ8MYGY9/MpmUKMalKhQKweFwiB/EJqb1eo3RaIRnz57pzj0rhafTqZgprAomk2g0GqkxisDqaDSK4XCopZRLMRuwyN9hLNBms2mR4zL48PAgwY0ODy6ZdHvQ/UXOjsViUQ01z38umJVKRY6xw+Eg1wZfO7vdDq/Xi5cvXwpQyyWYMTjCZLfbrRwmi8UCk8nkkduAyz3FRTqISqUSisWixMNmswmPxyPxktEuOpsoXh4LYXTp8PwkE2W1WimWxHONkRcyejweD9rtthgu7XYbNpsNdrsd4XAYPp9PrxFrpcmkYfudyWQSU4s8GrJiDocDYrEYZrOZYkEEhIdCIbGqRqOR3EEE+/r9frmM6PxgjfZ6vZZA4HQ6cXJyIjfW3d0dXC4XXC6X3Bx0ZwFQexvdWGdnZxgOh4pFEZ5LwZZC72Qygd/vRzgcRrVaVY02XWR0J8bjcfT7fcTjcdze3kqYpZBE+DC5VXa7XdcRvu8++eQT1Ot1udtqtRqy2ax4XvV6Hel0WoIWm6HoCOT5y5r7cDiM7XaLh4cHxGIxQewnk4mEJdbNx2IxXF1dKbbFpi+bzSaBjjFPRpoYf+V7N5FIKHq4Wq0kGPL5+P1+5HI5LJdLDIdDtFotDIdDhMNhQdcZ8fT7/ZhOpzg9PcVmsxHTiuBqQvqNMcYYY4wx5ps+/psZdtPZ1/0wjPkZxvzn3Ws6ALbvX2P/t76N6t/2Ynz647V/mjeh9jtu7P72d77yx3TYrOH4//yJPnZvb7/y72HMu3lvIYdMnOfPn2OxWKBcLmOxWCAajaoulhEFOkVCoRDS6bR4HbTtH0c7BoN3/iu6IrigkPVyenqqNpGrqytkMhndvSVQmFwItouwnWSz2YjtQQGm2+0imUyKZ7LZbBQbAoBGoyGHCaMN8Xhc3BM27rx9+1bLKF0ElUpFy4Pf7xcQd7lc4ubmRst7q9WC3+9XlMxkMokR4XA4EA6H8fLlS2QyGbEo4vG4lu5OpyP4KQGeXBx5p3273cLj8eCzzz6TI4o1ycViEZVKRZwMABIGTk9PUS6X4XQ6tRzTcRSPx8VKMpvN4vsQ1hoMBnW3/Hvf+96j5ZDuCJ/Ph1KppEajVqsl1gjFDAp8hAOTf8FjT/cI65a5/G02G31YLBbEYjEJZtFoFOVyWcwgioa8g3+8BAMQ24ZuAn4fxryWyyWm0ylOTk7UMMQWotlspvOBC2cikcDV1ZXeL7PZDA8PD3j+/DkASMRwOp2w2+1ynZERNZ1OEQwGFWchYJzcGQDixfR6PS2jn3/+OS4vLyVQOp1OlMtlnSd0u5AHZbfbkc/n8YMf/ADf+c53JKIRnBsIBGCxWHB3dyf20mazkbhGHpTP50Oj0ZAgS64V24iOmT7k69AxwugUBSAKnb1eD5FIRB+VSkVOi0ajgcFggGfPnukawGamYxcWo1S1Wk1tb+QS8XWn+4bHbDAYKKb20UcfoVarodVqoVgswufzSaidz+doNBr49re/rWYt8mPoviKjioDlUqkkyPtqtRLfhy1hdLYQrE6B6XA4IJVKieV0f3+PeDwusbrVasFkMj1yxzAGO5vN5PRhxTbdL+VyGR9++CFub29xcnIiyDpjmwQMU2CmE6fT6SCVSqHdbut6wX+/3W4lSE4mE6TTaQwGA8UDl8ulRLR+v6/nwCZDt9uNRqOBdDotEfnh4QEAxFbjdYxiKB08m81GDjQC6Y/bDBmXBSDxmv+f1zZjjDHGGGOM+aaMrTuHaf+T/84yWwP73U/+S2P+Sk7kn30G/GQsDdytA0ypOGq/48LhJxSTHSzAqOiAJRj4y32QxvylzXsLObzrudvt0Gq18NFHHwGAqn4ZPQEgfsV6vRYMeblcKk7E5hfa8hlByefzWhhKpZKqc1kNy3gPo0SpVArr9Rq1Wg12u12LF0HAvOMPvHPrHC/nbO+5vb2FyWRCLpfTskb2CBfPfr+vx8lF/vT0VMBiujroPCJT4Vik4aLPO8aNRgP9fh+FQkFiQ7/fF88DADqdDgKBgOI2b968wYcffqi/S6VSMJvNivIQNMrF2Gw2IxaLYbvdYjQaadHqdDqIRCKKcpGxw5pg8i8YZeBCzEgY6+U5bKFqtVpynfj9ft3x3u12+OKLLxAMBrHZbBAOh9UWxGO33W4FcOVSa7fbAUDtU4wl0bHEY+xwOOTg4F1+NlVdX19ju92qBv74/GQkaL1eK/bDRpzD4YBAIKBzla4BumIY0+FzZBW11WqVY4c1ygQyBwIBRRJ53Aj1JYCb0RI2QM3nc1itVkVMQqEQstkszGazXku/3y/HDpdluqDIh6K7ZDabqdmNgpzD4UA0GoXf70e1WkW73RZQ2u/3Ix6Po1KpKGZEYDKFGIpVxxBbVlWTo8PjxjprOj34fuY1hCJeOp1WKxpZNIwr8n1Kpg3PY5/Ph7dv3yru2W63xbfh+bNerzEcDhEIBJDL5TAYDHB3d6fmpWg0KqYTXTi73e7Ra0W3HZd+Ctk8JxkFZFMTY3o3NzcAoBjifr9X0xSb52q1GhwOh96LHo8H4XBY11+eCxTF2QBGUa9er2O1WuHy8lIun8PhgFarBeDHFey73Q65XA5OpxPL5RKlUglOpxPFYlHvodvbW0GxI5EIvF4vAoGAuExk27hcLjidTtzc3CCTycgpNRgMYLVaJZYUi0Wx0egipHtqNps9Egqn06nipvP5HIfDQQIUzxmKSIFAQM5A/pwYjUYoFot6r5lMJsU9yaqiQMhzpNfrIRAISMAGIGaPMcYYY4wxxnwTZv/yDQJvxo+X/wNgH++Bevtre1zG/HyzXyyR/y8mcDcOf1bQOQC934wDf46xeJY1ARmjseyv67z3b6hss6G9fTQaoVAoyDXhcDhU18omE9YG04nCpY8OG3JwGo0GPvjgA9zd3eHNmzcwm8348MMP5UAh3BWA6pbJpvH7/djv97i9vYXZbBZDY7FYyCXABiiz2YxMJoP5fC6+ByG5dA31ej2EQiF4vV6Uy2VxRdi+s16v5XqYzWaIRqPYbrdiYZBvw+gNWRv5fF7HzmKxyDXDRZ2uE0KAj+M7jFMEAgF88cUXOD09xWg0AgBxYbiIpFIpAWQZ3eDiy/9Prg1FEXJQbDYb6vW6qt8Zk2EEjVXCmUwGNpsNNzc3iEaj4p54vV5FjT755BPViROUzZgMK9vZZubz+RQjMZvNuL+/x9nZGer1utxWtVoNh8MBiUQC7XZbolA2m8X9/T1OTk5gtVpRq9XUbsNFkRGzyWSCfD6Pfr+P+XwucOxgMJAgst/vVaENAMFgUCDit2/fAoAcHoVCAcPhUJXIdJU9efIEtVpNYGs2L5EpZLfbtSRvt1sUi0X4/X6dt2T7uN1uHVc6V+bzud5zZrMZuVxOy/VkMsFms0Emk0G/38dyuUQul1PrGt0nm81GIhGFUjrhgHfxIMZ92ATF5iCHw4E3b94Igk1wLsW22Wwm3ozdbpdTirBiioOEkBOqfDgccH5+Lo4VYcL9fl+cF4/Hg0ajgUKhgKurq0f/hkIbuS+TyQShUEjXpuM6ckYG+/0+HA6HXkcC1W02m5q8+JxYhU3Q+HQ6RbVaxeFwwNOnT+UOpMBChhevGbxGmM1m8WpsNhuurq7wu7/7u2qZOxwOsNlsEs4oKtEN0+v14PF4YLfb5VwhE6ZcLsPr9SKRSIirxaauSCSC29tbnJ2dqTmL8GC2kQHvRKhOp4NoNIpQ6B0gj849OpNOT0/FmeGxILybghRdlfF4XHHDfr+v6wcjURT0WKtOftf9/T1OT09htVolFE0mE10T6b5kBA0Ams0mcrmcuFdsDeOxKBQKej/xGnD8842wcrfbLeg4/9wYY4wxxhhjvimz//QLBH/vR/A01nAMdwj/QQ2u/+JH2A0Gf/E/Nuav1ux3OPzJ54j9X3+Iwn/8OeI/+HHWap4yYVIw4uHf5HlvIYcsF8JCCZflnWmr1SqwJTkfXCqHw6Hgq+RDOBwOsVx4RzQUCmnRaTab4hUsFgs8f/5cS990OpUoRKt9IBCA1WrF/f09EomEoLCxWAyr1QpOpxPJZBKj0Qj9fh/39/f6d/ye6/UauVxOvB6PxyMIptPpRK/Xk7DECl6yLtjUcn5+jslkgsFgILbF7/7u78r5w4gXoaZms1miFOMHXq9X8QpGm8g0uby8xBdffIFwOCzB5ZhlAkBQ1e12KyGFPJRoNIrZ7F3+lQDS4zgLAaCsAWdler/fF0x2OBxiOByiUCiIIcPPn0wmej7kDM1mM6TTad1ZZ33ycDhUMxFjbsCPwa4E1Hq9XlWvE4TMtidGeMjhINiVok4oFFJ8aDKZSDBjnItOlbOzM0VkCF9lWw8hzrlcTuf67e2tlnM2VCWTSfT7fVVIM0LDJTYSiaBarWI+n+P58+eoVqtyWzEKValUkE6nEYvF8ObNGzidTrhcLsVuKH7abDZEo1F0u11xYwjHZhOX2WzGw8MD1us1Li4u0O121ZhVrVbhcrnk+JpMJnIpkK1CzkkikdDXPnaUEbacSCQUaQMgzg6FBro1wuEwgsEgWq2WmrFisZha4obDoYDHFG8ZT1yv16hUKoLakjdDJlQoFILf78fd3Z2at3hseS5yeQ+Hw48atY6f73Q6VWSJ8aFIJIKXL1+qFrvT6SCTyajBjjFRNsnRuUbOi91u1/lMRg5daclkEo1GA6vVCsPhUM6q09NTVXfT2cTmMvJlbm5uFMXjsWf8kCI2RbF0Og232y2mWTabRbValYNqPB5jNpsJyjwcDpHJZOBwOHB/f6/3Ddvk7Ha7okl0Y5HTY7FY4Ha7de2lu4xw5H6/j2w2K7D67e0tBoMBzs/P1Qx2dnaGu7s7xR95XOmQOT8/x3Q6xXA41DW5WCyK5ZNOp/Hpp58in8/LJdlut+V+otALQC5Ms9mM+XyO4XAodtp0On3U2GaMMcYYY4wxf+3ncMB+uYTlv/kBHP/qT7AtVXDYrL/uR2XMLzD75RL7yQTmzU/JWf2UcXYOQNcQ8P66znsLOeQzMC4UCoXQ7Xa1MMfjcf2CzAXwcDjojjg5I1xCFosFhsMhXC7XI84CrfJcrMh96XQ6cm1wiSUAmHdMuUzU63UA0CLBzwPeWeVPT0/lCuh0OhiPx3j+/LliKaPRCJvNRnequXQmEgm4XC41r1BEiUajan1h7Mrj8WC328Hj8eDNmzcIBoNqnWEMhAwLxl1arRa63a64Ok+ePBFzJZ1Oo9vtCtQ6nU4f1aUfR42OhRou4lxgj8WoQqGAxWKBm5sb1aWbzWaBqBmB8Hg8jyI7ALQccZHcbrfodDpyMxFs2+/3Fa0hU4XL5mw203lF51KhUJALw+12YzAYKOrAx0XOEAUUskjIx+D5Stg1l1wukwTUDodDuWSOOSlms1nLNxdYxn64yHo8HsWuUqmUHFDpdBoPDw86J0wmkxbtzWYjsPMxGJk8F7qr6OYgm4QLtsfjUQuVy+XCeDyW84ycH9a8kwO12WywWq0UyXG73XA6neh0Othut/q3hL+m02m8efMG6/Ua8XhcjWZ0S1itVjVRBYNBCQCJRAJv376FxWJBKpWSQ49fO5vNqnL6OLbH43J+fi4O0f39PcxmsxqiPv/8c7l/KBpRWORxIJg6lUrp9d3tdri/v1e0abvdygnDxjOPx6N/y4W+3W4rykV2C//c6XQiFosJQO5yubTo09FBtxTbrtgKxiYzVmV7vV7Y7XbFwwibns/ninaGw2FMJhNMp1PE43GJTQDkbiRQ3GKxoNFoPGoU5Pv3D//wDxGPxxGLxRAOh+VsbDQagsTbbDbUajUEAgFF8tiuxQgiz2W+d0KhEBaLhWJq/X4fFotF0TzCzyl4M35HEZyRNLomI5EIyuUyJpMJstmsnuNxDPA4+kR35Hq9xtOnTyXuVatV2O12/VwCfuws4s8lCt7r9RqtVguRSETvAYrx4XAYzWbzZ/yxaowxxhhjjDHGGPPLHUsshv5T+/v/gwPgr2yxaxmRur+u895CjtPpRLPZVCXzcDjUQp5MJvHw8IDhcIhgMIjD4SCnDaGlbBghPJaV4n6/X3fE6f4gxyKVSuHm5kYLaDabVXMKBRz+f/7i7nK5xOehOMAoEqGxt7e3ePLkieIAjK0sFgtEIhGxeNgg4/F4Ht0NJq+BywAfy7e//W1cX1+LbRMIBNRgk8/nAbxzK/BOe7PZfFS1TdFlMplgPB7rzj7FHS6g5JQwiuFyuRTF4OJIoHQ8HtedcDJ8KKzY7XY912AwKNcH+RRWqxWRSAS1Wg1Op1POJZvNprac2WyGw+EAu90uNkg0GgUAxca4RD88PCAcDqvthhBSxsyGwyHa7TZOTk4k3Dw8PMhpBEAurkwmI3cW663JBCGUmeKOw+HAbrfDYrEQf4hV8Q6HA+12W+weclBYAT2fz8UIYuRkNpvB5/MJRtxut+UCWi6XWr7Z2uT3+8VeicVi6HQ6+po2m02ijtVqhcPhwOvXrxEKhRCPx7FerxUBqdVqOifo0rFYLIrBzWYzuTlevnwJi8UidwqjcxS3yIoC3sWmyDzyer3ikLB5q91uC1jL2JrD4UCj0UA8HsdoNBIDhecn37MEXLdaLTx9+lRwawq4o9EIkUgEr169gtPp1HFlxMpsNuPk5ATdbhf7/R6z2Qzz+RyRSETRL7rIKA5YLBYMBgO52bjc05Vyf38vgXa326FWq8mB0ev14Pf75fpiQxh5LYxoeTwepFKpR/Gw43Y2XkvK5TIcDoeYP4Q/kxfmdrvlaIlEIphOp3Lh0BlGgYeil8fjwWazQSqVeiQg0ylJHhEAOf0o9FJIJktsvV4jFAqJixMIBDAajSQSRqNRtNttsY4YSaWYRVbScrnEdruVU5D18S6XS+cKI4YEeOdyOUWlKOSznY7iPSOGgUBA3xuAXFDNZhO73Q6pVAqff/65zvdgMIjdbodyuayfWZPJBKenpxLOm82mrmvk6kQiEQwGAzQaDfh8PqxWK1xcXPzMP1iNMcYYY4wxxhhjfpmz63QQfvOTnVWe2mOnjrN7QPTzLRz/+Z/8Mh4agHdCk+nXPoDp1z6ANWVweb6KeW8hJ5/PK4pTKpUQi8UEXHU6nTg/P9dCt16v4ff70e12BSTe7XZwu92qgeXivFwucXd3p0YaNlmxPpcRITI//H6/qmbpVFgul/o+5FCwzpo2/0wmgzdv3sDn8yGZTKLVaiGbzerucr/fFwCXTVVms1nPcT6fo1gsotFoKK6USCTkbiCDhu4XVh2zvapWqyGfzwsCSw4DAc2MklBocTqd+OKLLxCNRlVfnUgkVDW8Wq3ksiHMdbFYwGq1itXDmJDNZkOr1ZL7hLEQumgYcaBjIZFIoNlsqu7ZZDIJ0BoIBORWIuC51WrBarUiFosprnQ4HBCJROD3+3F1dYVcLodwOKxjSVdXqVSC1+vFer1GNptFqVQSzJiMEYp7hBAvl0u0Wi0kk0k1y5AHQmGJXAwCjekgYhMOAH09p9MpwDJFMp5fhUJBgh8AiZC73U5iJmvpj9lNXE4pIgDvxNB6vY5QKIRoNKqlnRBtAltPT0/lGiCjhnwnioSsiycEt9/vy4XCKm3+N8G5rLsfjUY6dxnZsVgsjxZaj8cjRxJB0BRhKdaRf0PBttvtIp1OCz7OFiq2GFFg5HNnhA6AXHtslSI4nSIBnUCDwQA2mw3lclliTjabffR+K5VK4uV88MEH6Ha7+js6mIB3ohVFxLu7O8RiMbm8jgUO1mKT52KxWJDNZnUO2O12eDweNdmRDcTzjxyuTqeDVqulx0bOC0HidDFdXFyIq8Oo5Xa71TGjoEORg7EgtsGFw2F0Oh055uh03Gw2iqYxwuRwOBQf5bWE11kKodlsVnwen88n4fnNmzeKtzJuxdd0vV6L/cVzn0D60WgkES6fz2Oz2YiJQ+GVryeZO6vVSo6g9XqN5XIpNk42m0Wn0xEYud/vSxijo8pms8Hj8aBSqeh4sTWL0byLiwtcX1/rOFGcovvHGGOMMcYYY4wx5q/yuD6vwj56/GeBmz1i//wV8v9qpI/U793B9S/++Jf62HZnKZT/rh/lv+vH8lnml/q9v6nz3kLO3d0dzs/P0el0AED11lz6P//8c+TzeTX4rFYrmEwmtTWRjcIWF5vNpppcuhKCwSAajQay2axqisPhMLbbLaLRqD6XywbZL4xvsM2FFc0ulwuhUAiNRgPX19d48uSJWC7kx7BqnE1XbFxaLpf44osvxNspFAr44Q9/iHw+D7vdrogTOSy73U4sD4/Hg263q5YiuiAY1ziuY67ValqiuVQSKnp2doZWq4XT01NFb25vbxEOh/HixQu0Wi2MRiPFRdjqxKru2WyG8/Nz1Ot1xONxWCwWtdoQMk1IbKFQkCuJCzcdJeQcBQLv6unYgkSALaNn/P8UpVqtltrCGK1zuVwCR/d6PTXn9Pt9LbSst2ab1Hg8FmuH4NbZbCYRjs6Bp0+f4u7uDrPZTOcBI2x0T5VKJZyfn0s0efLkCdbrNa6vryXWMG5DCPRgMIDJZILL5VJ0xOFwwG634+3bt8jn84hEInKbHAsGhUIB0+lUwhNbvxj9ogtls9nAZrNJWCPzyGq1YjgcquWL5zZFv1arpYV0Op0im82Kz0LYM4UGt9uNSqWi6NZms4HL5UI4HMbDwwN2u51at6bTKfL5vADIXKITiYReB4ovFALYekXYdiwWk1uKUcntdotarabFmuweAmgXi4XEAQpgjCXN53PFwdLpNHw+n87XVquFeDyORqOBYrGoCnc6icxmMwaDgcRBOq9Wq5WAzXRUMQ5EIYzvp+PnMhqN9DoOBgPUajU8ffpUbile41arFabTqSJgdH0RNrzdbpFMJtU+R8GEQHE6YwCI/XIMmOffkc/FOCX5R2zG43Fn7fYxAN7pdOpYUdyaTCZiZLndboTDYV1j2+02stmsnHLhcBjdbleRL15zs9msBJ1jEHQ6nVbjFl196XRacS/yjur1umJo/Byy1/he5GsRjUZ1feXPJzqjwuHwI/cgodvAu3haKpVCvV5Ht9uVe4exQYq6xhhjjDHGGGOMMX/ZY81mcPgb34Lly99Nf9bZNppI/2dfwNU6wNU6IPRmh8A/+x524zEO33+lj22z9RU/8j9/zG432t/x6v/3Xjhg+bJUw5iff95byCEzhb9QM07EpZciDUUBOi24PF5cXGh5ZrQDgO4ws4L7GNxJ5wajWgQOk//AX87ZIsSliwsvf5Hf7/ey/DscDlSrVS2sx7EIRjdCoZBcKuTkEK7Lhp/RaASfz4d8Po/r62vVhnPJymQyqNfrEia4VDO+wHYnRrkYd6Awkk6ncXd3h2AwiFgshpcvX8LpdIr58K/+1b/C5eUlXC6XGn6OozNk67ANh3wjl8sFu90uIY2sGIKsvV6vWppMJpMEFLvdjqurK3FsPB4PFosFptOpOBd0fiwWCzkrOp0O0um0gNOHw0ELFxfVwWCgavhQKIROp6PIBrkzrB4mU4h3+8llurm5wXa7RTabVRsZBRVynegio2PK5XLh9vYWkUhE1c18/ReLhe7WFwoF1TyzgvwYtD0ajeTEYQyIkGWz2SxoNhfZs7MziQB0vlCkIAuJLod2u40PPvhAPKROp6NKe4fDgUQiIV4LF2F+LiGzjUYDfr8f4/FY7WMULg+HAzqdDoLBIOLxuEDWFF8ZsWP1NXkuNzc3iswxDkVOD5udKpWK3icU+Hhcl8uljj1btlKplADKPF8pSJGxRDAzGVQU37bbrRqdGF9jXI+17mwmYzyK15/ZbIbLy0s1nkWjUQyHQzgcDjkzAoGAIobb7RbtdlsxJIoajPP1ej1dWyhuMj5H4YUib6vVkgCYzWbVgEV3jsfjUYOb3W5/VGHOmJTf70coFFK0cb1eC0adTqfRarUkFtIV6fF49P6mWHTMvTocDhiNRggGg7pG0PFFGLLNZkM8HsfV1RXMZjOKxaKcbITh81pGAYbXmlwuJzEnk8nAbDZjuVxit9vpHCecnW7NcrmMcDis50L+1nq9luDT6/WQTqf1/qL4xvOPNxd8Ph8ikQiurq7QaDTk7guHwxgOh6jX68hkMtjtdojH41/Rj1tjjDHGGGOMMcaYnzzWXBaj38xiUnBi+HfOALPl5/o6u8EA0f/0DxH9T/8Q3n/2Rzh8iZX4Omc/nyP+g6n+f/Tl0mhJ+wrmZ4Ids7qXzpj1eq22lWAwiMVioThOsVgUn4Kg0GAwKOAtG13IYWH7C90AXHAXiwVSqRQajQa8Xq9+2ecdfi5irKau1Wpy/TDuFIvFHrlPCM2cTqcIBoOqLCY4tFKpoNfr4fnz52JyABBoltGf0WgEs9ksBw/vbAPvYiSsIeaCxJpoMkIoEk2nUy3djFNQrNrtdvjBD36AdDoNr9eL09NTXF1dIZlMqlo3n89jOp1ivV6rEv443sUqXsJVHx4e4Ha7BeT1er1yUrDWPBgM4urqCvl8HmazGXd3d4hEInj27Bmq1ariOM1mE6enp+LusGI4GAyqtplxKn5/xne4oPPY3NzcwOFwoFgsyglENhOjMWQnmc1mdLtdmEwmiXL9fh8ABM3lgsevRTGw3W4/4pt0Oh3sdjvkcjkJAlwsHQ6Hll+KXxT86IKimyYWi+kxUaxjxOhb3/qWlnDWkrNhi4IhocqM4tzd3SGdTgseTsgyK7EpXI3HY6zXa8GqfT6fqtsZiWP1PFuMrFarhE3GRxjHOWaoEDjM2Fm325Ujho1yBPYSYnsseHKZN5lMaDabAudS+Gg0GhJsyDWhyEJXFAHBfzq+FgqFxMKJx+PYbDZYLpdq16IQ02q1FO0cj8e4vLyUYEOeD+NX5ECxhpqf1263EY/HBb/dbrfip9Dpw2gnrxl0d3i9Xnz22Wd49uwZ+v0+otEo7u7uxK0hhJvV82x44+O/urpCNBqVQL3ZbPQ4CT6fTqdIJpMYDoeqpa/VanJrtdttxWMHg4HYVYvFAn6/H7vdTufCer1Gt9tFNptFIBBAo9HAYDBQRXssFtM1itery8tLNJtNiSsnJyd671ksFhQKBdTrdUGseU6fnJyIQ0Tx+Nj5RLcdz/dqtarzwuPxSGBfrVY4OzsD8M4xSJcjRRzyqeiQXCwWaDabODs7U4PYZDJBv99HPp8Xp4wAaWOMMcYYY4wxxpg/PSaHA2a3G2a3+xf+WvuIHzvbu3Iey8/YPvXXYcyf3eDkn5Rw8k9KsH3v+ut+ON+I+ZlgxwTc0tbeaDSw3W5xcXGBSCQCl8uFUqmkyBMA9Pt9OWUCgQD8fj+azabuclssFokHbMHiHXzyafr9vpY8VhP3ej2xVSaTCXw+n5wMFJiOG3mSyeSjxZhRm3w+LxeJyWQSsJN38Mfjsb7PsfuHtbS73Q6xWEzwYy6x4XBYf261WnWHPJvN6s651+vF9fW1Iij8s3a7jeFwCLfbrYry46ahQCAg5wVBooFAAOl0GldXV2qbWq/XsNlsErVsNptiVgSdkotyfCzZKBSPx3F9fY3FYoEXL17AbDaropv17oyX0emz3++x3W7lXODjYLsMIbqJRAJut1sLHAAkEgmMx2M1aG23W9XeHzutuLDTjUPnDv+OSz1FNTp6WOMei8XkkLm8vBQ/hMybcrkMi8WiVqRarfaoJt3lcsnpRXcMnSNOpxPdbhcPDw84OzvDYDDA4XCQkyEej8tdNJ1OxXxhdIt16MvlEmdnZ3rOFJXm8zmm06m+N5d5nvftdhuj0UiiVKPRUKsRXSzk70SjUbx69UpVzlycgXf8mPF4LIdUIpHAcDjE+fk5JpOJnFHkmRzHZxj7WiwWyOfzqNVqEpHcbjfcbje63e4jFgu5Lb1eD9/61rcAQDDw9XoNp9OJarWKYDCIk5OTR9cLclgYibJarYrAUfChyELnynK5FBwdgOKb/Hs2ozkcDvT7fcW/FosFnE6ngMjlcllxwXg8/si9QpbW27dv8fHHH8uBRPGE5zfB5CaTCZlMBtVqFZ1OB8ViUeKk3W5HpVKB3W4X42o0GiGVSqHZbIq5xdhnNptFKBRStIvvF7rIZrOZmE104fB67XK58OLFC1xfX+t9k0ql4PV6kUgk9Dk8vyi0+/1+FAoF3N7eolQqIRqNIp1Oo1KpYLlcolgsKnK52WwkQrHJjZXkZBLRxUPoOH8GJBIJ/PCHPwQA3RwIBAJ49eoVvF6vWF10J/FnAWOw/HnGc5VOwH6/D5/Pp2sJYdyxn9PebIwxxhhjjDHGfHPHEglj/t//GMN/5yMM/52PYHnxBJZo5Of+evsfvUbg7QS+0hK+/+YtsN99hY/265/9fI5ttYZttYb9l82/xvxi895CjtlsxsPDg4QWALqjvFgs0Gq1EIvFJLIQQnzsLCEolC1CFBfYOBOPx9HpdNRMxK9Huz3w7u41F0LeEd7v92KisJqc7USdTgcOh0PNTPV6XSIS7ybzg9EQVhvPZjMtU+VyGT6fT2waLkJcQNnScnFxgWAwiM8//1xVwIFAAIlEAgAwGo3g9XofMUwYhyL7JxwO61i1Wq1HVch0UbC9ZjKZoF6vS6xZLpdyaXDRDAQCihn4/X5sNhu0Wi3Fz1jNzqYdLvMA4Pf7JUiNRiOJPMC7CnKn06klqNPpKMJWr9fFDJnP5xJB5vM5zs7OxJ3g3ffjKAbjGP1+X26TZ8+eodfrYT6f43A4YL1eY7FYiL9DsYgAZ9aUB4NBuS0Y56Ib5Vj8YZSOx8hms6FarUr4YEyQcT9CWY8fRygUEsyaohgbz0ajEZLJpJwiFHDIChqPx4oWOp1ORCLvfhC0221BikulEs7OzsRHsdlsj85/uoDIeWLsaTKZyEFB7hAAMYqazSYuLy/RarXE5hkOh48YMIwf8XHTxUb4NQC1xLFJCgAeHh4Uy2K85XA4qOLdarWi1+tJdGGExul0Ip/Py0k3m82QTCbl/Nput5hMJjgcDqjX64JO051GQDNbnSaTiWKJZAYRZstzmXB2RrMSiQTK5bIEUbalETxM96HZbMbZ2RnevHmDfr+Py8tLRUL7/T4+/PBDPDw8qEmPrKjVaqU4G4HZvGbyeLBJiY/bbrcLQg1Agi+PG2vaKSBTSHzx4gUGg4GA0nRKRiIRvfcIoCd7hiIQr0+5XE5wc8K0XS6XGrb8fj8ajYbOxc1mI8chr5+73U4/G/jvCImvVqtYLpfI5XJqWaOgTlbUkydP5K6hU5ICG4/r/f29YNIETvN50x1GZxuvT7xxQKYOHXkEpRtjjDHGGGPMr9pYYjHsfvc7+rBEwl/3Q/orM2afD7PfOsfGYwZMAEzA4OMQZr91Bovf/35fxGR693E0h++/gunf/MiIHRnzXvPeQg5/uWeDB50giUQC9Xodp6en6PV6aiw5ZisEAgHBVN1fWs/ojjCbzWqd4S/vPp9PdeZ0hrhcLsTjcbViMe7Bx0NXDpuLGD1g7Tm/7uDLNwaZCAQkLxYL9Ho9hEIh5HI5/Jt/82/wwQcf4OrqCrFYDKFQCG63Gy9fvkQymRRHZbvdYj6fw263I5lMolarCTDKxiiPx4NSqYTpdAq/349gMAibzYZGoyH4MgUVLhOMZJAnQegua5BZmZ5Op5FKpXB3d4f5fI5MJoPb21vxMY4r0LkAMmZFZgZbaAaDgZw1FN6sVqscFWwbI7yVohyr0ylyMJZCwDEjSRQ/GEti3IdcJDo1gHcOCToK2NoVjUYRi8Xw6tUrRKNRiR3HcTQ2RtFd5fF44PV6xUXZ7/cYDofizrx69QrT6RSBQEDRkuPWLt6xZ8yC7JnT01NVJxN2XS6XMZ1OEYvFVG+9Wq0wGAwQjUbR7/flcHjy5AmazSZGoxHS6bScUYS8jsdjvfb898+ePUO5XEar1UK/30csFhMcmKwq1qZTzCLjZrPZKK7DBZdiFSvGGVHj57jdbtzd3alxjIIY26vI22k2m3KW8D1FsSeVSolnkkwm8fDwgMVigUwmI8AxXTEEEa9WK4l40WhUAsB8PketVkMsFlNMj0JWvV7HxcUFRqMRbm5ukEwm4Xa7EQwGJTrQwcSvwzga8K75yGw247PPPhMHjNG4cDiMy8tL1WNT2Lu7u0OhUECr1VJFOAUsuod6vZ4alChSErTOWvjBYIDT01N9bcLCjwHY0WhUXDDG8Gw2Gx4eHpBKpfDw8CDX2ng8FhSeYGBeh71er/hQjDIx2gdATVBXV1d4+vSpIp/AOxF6vV7rfDluoZrNZnIv8ti5XC7c3d0hmUwim82iUqnItRYIBLBcLuWEpFjExjGK2fV6HW63G6lUChaLBdfX1/q+vDbQJcWbAslkEqvVCuPxWO4lsm/I/WFMLBgMIpfLSZAD3oH9M5mMrm/39/c/1w9XY4wxxhhjjPnrOpZoBKPfPcUsbdfH6L9zif3f+vbX/dD+Sszs7zzD2v9n1+i1zww4HH/uvzXZ7LAmE1j+D34d03/wGzB/8NQA/xrzc817CznHVbBsLYpGo4o7EZIaDAYBvItFAECxWJSDgDZ54F0TDQBZ6ek0IQfH6/UiEAjAbDaj1+sJ1nr8OLj05fN5bLdblMtlVRszrsOFiHW+4XAY6XQaf/zHf4xQKCQBIJvNYrFYoFKpAHgHE2XdLgUeugAY1wKgx8LvydiR0+lEsVhEp9NRqw/bgobDIfb7vdxFXEq4+DudTkynUzlkKFzxLn2n05FYQSaKz+fD/f09fD4fCoWClme20ywWC2SzWd21puhCtofdbkcoFEKz2VQz0nFMiowiPmcAqhom+4SxDYoArAKmmMIq7mAwqKiQy+VCtVoVM+XYgcXHRNdCr9dDIBBQg85isdCy7f9S/WbbzmQykVhBNwWdKqlUCre3t2i320gmk+LskH9Et9Ox62a320koWS6X6Ha7apUit4nRFgJkyUhiSw5jIYyXud1uFItF1XXTncSq62w2i+vra4Ga//iP/xjBYBD5fB6ZTAalUgkmkwmDwUDOhclkgvV6LXGIr+NiscDTp0/FbuLxOD8/R7VaFUuFESIuyHyPDwYDpFIptNttRYHIuCLMmk6pfD6vxZ7OO9aC+/1+cYrm8zmKxaJ4S3QkWSwWAZMpILO9bjAY6L1MN0g2m8V+v0elUsF8PseHH34o59vbt2+x2WzETAmFQnJauN1umEwmzGYzRUIp2lmtVphMJjx9+lSuHwogh8MBzWYTiURCPB0KTTabTWK23W6XYEGxwmQy6Zyki8Tn80n0GY1GaoljxNDlcolrQzGoXq+jWCxit9uh0WjI9chjwudwenoqRhJdeKyu5zWN9dtOp1PvG4KAT05OdN0j4JruoEgkIhcdAcSbzUYOFsbu6I6hC4ecmtVqhUAgAI/Hg3q9DgB6f9G9x/dmu92Gz+dDPB7H/f29QMij0Qir1Qq9Xk/cn/l8rsa5Vqsl+DGjmKxVJyj84eEBNptNDC4+R76edCAaY4wxxhhjzK/KrD4uYm977BbZW4FV2PZzNyp9k+ZgMf3Uv9uep//cf7v/jefo/50TrP1m7G0mDD8KYvK7l7AmE1/1wzTmGz7vLeSEw2E169D9wtgKBQj+ok4mRTabxc3NjVqnWCNLEC45G/yFmoLFbrcTyyUcDsu9YrPZBBhOJpOqu72/v0cymRTYlXEYfh+6QxiRYC13uVxWnIKAZgBakhihcLlcajvi8rDf7yUM8K4/oxChUEhRikgkIhfIdrtFJBKRQ4hLL48pIwD5fB7z+VzwTTpw+LUsFoscJoy5dbtdfPTRRxiNRuj3+4hEIhIUCL1tt9tiFzHWxcfU6XS07PDuNhewZDKJwWCg2nRW/bZaLTx79gxOpxO3t7eKgDEyMxwOkc1m0Wq9q7hjBfZ4PBaUdbPZ4IMPPhAPg/8bj8dRKpWw2+1QLBb1+dvtVo4INtSQL9LpdJDJZBTt6HQ6chClUin0+30kEgm8fPkSJpNJUFm6AsxmsxZ2tj2RD0LnyPHzP25/YkQHgOIo8XhcYprdbkcwGFRUhnGs169fy9lC4cnr9QpQ63Q6BfZmPTi5OZPJRP+fUSBynMhUIecmnU7j5cuXAKC4Hx1K0WgU3W4X/X5f4gCjSIQR+3w+dDodXFxciDfjdDp1/GazGSKRCCKRCL7//e9L9KLLhq/RcTV1oVDA7//+7yMej8s5VCqVsN/vcXJyIvjybDbTORuLxTD5MldLgY4QXbrE2BJGUPp0OkWxWFRTls/nQzQaxe3tLcLhsPhHdPExikmIcSQSkePMYrEgmUzqHGArH91T4/EY3W4X0WgUFotFrkJCfnndo0DR6/WwXq8lFpMnRhGTTXiM1zUaDWw2G7kLGWPjNdlqtSo+SdHXbrcjl8vB4XBIIOLs93skEgldSwmbplOQ1+fBYKAab0ZAKXbxtWO0D4BYOhR4+JjIyKG7k+168XgcFosF+Xwe/X4f9Xpd1yC2uTUaDazXa5ycnGC9XmM6ncqNRicjoeAErZPnRudhu93GdruVi4fux/V6rePt8XgUH6Or0BhjjDHGGGOMwbsoUeg9o0O/orOK/jmOnN/4EJP8n71BtHWZMPobRZg9nr/ER2bMN23eW8hZLpeIRqNYLpc4PT1VjMTr9QoczKgHYzvj8VhiTTgc1p1Wfg5FhVarpRruVqulOBW5OGw1Ijy13W4L6DudTtV6ZbFY5IYhONdkMsmpc8wUqdfrSCQS8Pl8YrQ8PDwgl8vp6/R6PYFNGXuYz+eYz+eIxWKIx+Po9Xr691x4J5MJisWiWpkWiwUSiYQ+r9VqqZaaUR06VlwuF66vrwXx9Pl8GI/HmM/nWgDD4TDC4bDapqbTKcLhMHq9npq6AoEAWq0WwuGwFhIug6wXphBwOBwUzclms/jBD36g2mhyXy4vLyWK3d7eyh1EvgUjUrzb/93vfhdPnjxBv9/Xktbv99VORGCxx+PBw8MDttstgsEgwuEwdrud4ml0dpjNZokifLwejwfz+VxVxqy+poBCJxjFn0KhgLu7OwmPXCwp+qRSKeRyOTQaDVitViSTSVWhx2IxXF9fw2KxKPLD+mkyYp49e4bFYqF4F0Ubs9mMZrMpl89kMpFjgM4Qr9crZwFjMdFoFPv9XqBtPh5WntMhRe7KcDhUJOTq6gqpVErcoHa7rdgPAcN0krG+PhgM4u3bt/D7/QgEAoqJUVCaz+eK9hCqy9jjdrvV60PXSSgUgt1uR61WE5uo1WrJwbfZbJDP5x/9Gd07ZMawKpuOPLrqCP7m+3O1WqHVasHr9aJUKuHi4kKg4HQ6jbu7O7hcLrhcrkeNc/f397BarY8a3AhkB6DvGY/HEQwG0e/35TIj6DoajSIajcoxlUgk1O51enqK4XCo/8/IItlDXq9XQhevCWRBxeNxVKtVsbvS6bTav4B3oksmk3kkIjWbTbTbbQkXbrdbsPV0Oi0+2Xq9RqVSUdQqn8/jzZs3cLlcYowxMtnv95HJZBQdtdvtSCQSuLu7e1RNP51Odd2la8psNuuaTOFtt9vpWrff7yWCsz2Onwv8WKyj+8vj8Qh8nc/nFYn95JNPMBwOMZlMJHCPx2OB8heLhdyLVqtV8UmCoMkTY0sf3WC8AWGMMcYYY4wxxhjzPuP57k+PZO/ctndMnZ/0dw4TTMbNI2N+hnlvIYecjVQqhWq1CrfbLfGCIgkAOQsYNSJnxOVyIRwO4+7uTlEgLmuEz/KXdgI8eYeaDgHecb28vMRwONRS0uv1BPh0uVxyfcznc6RSKVUFHw4H3RGny8ZsNqPT6ehxEgC8WCy0XHIBJwTXbrfLqUDRqtfrIZVKYb/f61h5PB45J/g8u92u4mdsJyLvhzBaAKrKJoSZ9eh8nKzH3Ww2yGazOt5cQqbTqcDEjCux6Wa328Hn8yESicBut+s1YZtNLpcTUJZw2/V6jUAgIIiqz+eD1+tFp9PRcgUAt7e3gtKSzzEYDARfZkzl9PRUi7rP53t0R5wuDy7db968QTwel3OEryXB2nRB8XHd3NzA6XQil8upNpztMxTN6DbabrcoFAqqoR4Oh1pGk8mkIj/NZhPxeFznPFkrhFIvl0uMRiO4XC6EQiHF/rj4Wq1WCUoErXKJjMVi8Pl8ErvG4zEcDocqr7fbrc41xj64JDP2tFgsEAqF8PbtW0VLAoEAkskkAMj1xef+5s0budYOh4NAzGazWSwRCofJZFJRHT6Xfr+vc3Y0GiEej0sUYmPWfD5Hr9d7FMEcjUa6djidTgwGA0UOQ6EQksmkYOKHwwGnp6eYTCaoVqsSgXk94n/zuVLA6/V6gobzGFM0SCQSqFQqiMfjAN4BvlmZfewcGQwGSCaTamqzWCwSRbvdruDudPvw9RoMBmqho1i1Xq9xenqqJi26PMjdslgs6HQ6ihJRbGWkjg42cqQoKIdCIcVN6dxxuVy67vA1ILyXDDI6vnj9oJuFsSTC4On82u/3Oq7H0UaeF3TB8Bgz3kqXDzk/bC202WxyydClyWtIuVxWRHM0GsFqtepnQD6fF5iZsHpyv0ajkdhC0WgUw+EQkUhE7XV8P9JFR8E1n8/rmEciEbx580bXTsZ7+bPNGGOMMcYYY35l5qe1X3/zWrF/rvG97MK0x+PjcQC85SX2w9HP/XU3H53+wo/tKxuTCSaHA+X/8Ldx849/Ezf/+DfR/p//NkwOB0w24ybXX4V5byGHzUhkeHDhIkuALTBmsxm3t7d4/vw5ms2mGldms5liA9PpVIsif0n2+/0CGfOXfTaK0Cbv9/vFXUmn07qTz+W40+noc+j24b+lkMHFmVwGgmTpKqAQQV7J2dkZ/uiP/gj5fF5cEIoYjJoxplMqleB0OuHxeFAul8XYYVSC8OcnT55gMpng1atX8Pv9alGhaBIIBPTYx+MxgsGgYj2smPb7/WpaoTvjcDjIuUAR5ziCQ9YIoyq73U6VzMFgEIPBAKPRCBaLBcFgUNwQMkam0yni8bigwgAUZ6vValrGGLPodrvi0FDwoxtlPB4r7ubz+eByuXB7e4vpdAq32y3uyc3NDdLptNxMXq8XmUwGr1+/RiaTEYDVbDYjkUig0+lIbOGd/m63qyWdIhoXWYpuhUJBMUG6yBqNhj43Eono9SC7idyVTqcjkYeuFYqErFaORCIS8ege8fl8yGazciQNBgM8f/5cSznB0Pv9XiIeAMFlCRUPBoN6zLFYDC6XC9PpFPV6HdvtFq1WS1HB/X6PV69e4dd+7dd0znGxBaDms0KhgE6nI6cIIyzL5RLtdhvf+ta3xKChWMia+Pl8jmq1ilAohGw2K4B1PB6Hw+FAs9lEMBjUcj+dTtFoNMSTIS8pEonoGrJarZBOp7Fer1Gv1yUKAO/gtHSiUTDm6wZArh2z2awWMMKrs9msnH8EBdMlM51Okc/nFUE7Zl0xTshGsh/+8If4jd/4DYxGI3Q6HZyenio2lM1mxQXabDYSzHw+H9rtNrxeL+LxuFxqq9UKbrcb7XYbni8ttoxhEZTe7XZhsVjUSFer1ZBMJjEavfvlgU4um82muCC5M6FQSAIPm6mePXuG+XyO/X4Pu92OarUKk8mE4XCoOBmjjbx+kOHFCB0fHwHHNptNMGQAcmVSlOS5vV6vVTFP7g7PwcVigUKhgGq1ilqtJocdr9fBYFDuGcKJa7UaIpEISqUSQqGQRLVAIIBgMCiXE+H55EWxWc7n8+n6wXp5Y4wxxhjL+QlWf+/X9WE2+FnGfIPH9v/9HI7Bn2ptPACBtxPsru++ngf1SxprNoP973z7J36YP3gKANi9vYX/n38P/vs5XO0NXO0NfA8LmP/Npzhs1j/397Z9Uf6qnsYvNOaPn6H7P/tNzP9lGie/84Bn3ynh2XdKSPyDEub/Mo2b//NzWE+LX/fD/JWf9xZyyJ0hR4J3uTudjoQGRqgYt0qlUmg0Gqp0ZryE4FeCUPf7PVqtFpLJpO74RyIRNUw5nU5UKhVYLBaJEwTL0jHRbrdRKBTQbrdlz+eiQ/goQaSEcabTabUnUQyhm4fLJhcuxltisZjAnYybEMRKbgj/mxGCY6ZFOBxGqVQCAJyfn2MwGGjZZ3SNfBLWmjMyRJhst9uFw+GAw+GA3+9HJBLBcDhUHIzA03g8LocPXR2EsBKmTGdUr9dThXA0GlXMgK0tgUAAoVAI1WoVNptNSx7bilgHT75FoVBQXIuuJnJ9KOYwOtTpdFSDDED1yN1uF2dnZ6hUKnKssNI7HA7j4eEBmUxG/JhGo4FgMIhAIKCaZkZier2enCuxWEzuBsK2eS5SrKTj4BjEGgwG4ff7BZ6lGLler+HxeCS2UCQgCJnLLNvJCPNlw1SlUkEmk1EFNtkdrJKnAEZXBgWVYDCo+BwfMyG0BMleXV09Eu/cbrf+PUG/jMRRtDl2lG23WzFxeA2w2Wy4ublRDAmAGtMohoTDYVQqFYmsdCz5/X7k83lUKhU0Gg1FlLLZLADI6WSz2eByuTAajeSSYqyRwF66QNxu9yPXEqG1PBYU+ui0IKeLTW3hcFiRPTp9zGazRCe/36/zr9frKcpmMpnkEmTDUygUEkScrg5ycOhaYhV7uVxGLBbDw8OD3nc8BzudjsTCYzEVgFwnPKZkJ/E507VH4Wa73eLy8hK1Wk2Pl1+XAOBPP/0U+XweANRmRRdMpVKRm4lOQZ6L3W5XjijgnajGWJvL5dI1hmwftkYBP4amh8NhRaEajQZSqRRWq5VA8IxW8hpKMZXcpVarBYfDIQcn28z8fr/clG63G4vFQlwd8stGo5GENUbYKB663e5HLjBjjDHmV3cq//5vo/mP7fhH//j39HH1v/8Y83/33/q6H5oxxvylzGGzhuv/9wahTwc//vhRD4fvvwIO31xbjiUSxut/PwPH/675Ez/u/wMb6v/eb8Pi9+Ow3QLf/Qy2/+r7sP1X34fpDz/9xY/NZvsXf85f8hx+62OU/wMzUv/wAX7HEmbTj5+T1byH37HE03QLr/83UViL+a/xkRrz3kJONBrVQsI7una7XVwccjS4DJVKJUSjUQBQzTSdHu12G8+fP0e73dZCutls0Ol0FIfgXfJ4PI7RaCRuA1uRBoMBCoUCms0mstmsFlW2BLEanULJMfeBEFM2EU0mE9V1c8kwm80olUqwWq2PwLvhcFgukvF4LNbFdrtFPp/HaDTCeDyGy+V6xNdhgw8XKJ/PB8eX9XRcnCuVChaLBeLxOFarFVarlY4NHUCbzUag5+l0iv1+ryYol8sl9gvvVlerVcRiMcVHdrudqrw9Hg8mk4miZhQvvvjiC0U0+HrTyUABiY085EiwaapQKMjVw/jCcRyM0RWeK16vV8IARUDyNgAIdEo3EmMaicQ7sjuXbrIwnE6nFlHWbdM5Q5Atjx0fl81mE9yVUS26tBqNhiJdvV5PvJbZbKbnyBjMZDLR63Xs/OExZkzj/v4efr9f0GrCvE9OThCPx1Gv17Hb7XTuv379Gn/zb/5NxW+63S4+/vhjCXMULo+dHBQVGbMhePZwOCCVSoklwmX67du3Egh6vZ6W681mo+dfrVbVUHV2dibH3PFSfH19jRcvXuic7vf7sNlsSKfT6HQ6cqaYzWZFEQHoazGiR1A5HWuj0Uj14eFwGJPJBN1uV9wrRn5arZbEI7rw3G43hsOh3DZsdSIcORaLoVwu6+uzVclisYjvdH9/r/fY+fk5rq6uBKQGICGT1ygCtyk6k/2Vz+cxnU7R7/dV9+71egWMJjz5+fPn2O12eP36tYQSCsgejweHwwG3t7cCztNFcn5+rigaW7b4/mWDFHlbdrtdj5UCHK87ZFBR6KGrho4sgqfpXGKD3LNnz9Dr9dBut5HNZjGZTFCv1/HkyRO1wNntdrlwKJLz54nf71dzFt2ZnU4HyWQSvV4PAPT6E0TPKBfdOmxXY5yQEHGKZLy+lMtlHA4HnJ2dSQhl5HY2m8kFRoCzMcYY86s3Zo8H9//Rb+F/8T/5F/hfXf7Xj/7uP/zv/T/wO//bP8DqvywCZoNrYcw3b/aTCXavrn788cX11/2Q/tLG7HSi8b/+bTz8J2m8eFb5qZ93Hu+i8Pfu8cV/9PNVhtv+5Aq22f7P/LlpBwRf/v/Z+/Ng3xY7qw9bv3me5/l3pju9Sa2WutVqdbA7IW4zBweCKZOhwOWCogpMlQNO2bgCLsdFwEBMQlwVJ5UQp4vQgAkFIZgONoGWXutJenr33enM5zfP8zzmj6O1dA6SWvepJT09aX+rTknvvXPO/Z3923vf8117rc8aYPuNQo+Pa6zZDLr/qzkOIr3v+rlvHFZx+p+EYU0lfwivzJhvN68t5Ph8Pi2MvV4PBwcHWK/XaDQacLlc+gW53++j1+shl8vpqXIqlZKbh8swGTdkIbClhY4SxrVubm7w4MEDuRXIOthut2g0GnjjjTfQbDYlJhBMud1u9YTa6XTKgXFwcKBf4MnoYcsJly4uPMlkUnXM/B7ValURAXI/yJhgvMPv9997ss5/5kJANwSdDuPxWO6PcDgMu92ueuvNZqMliqwRq9UqZ8pisbjXAOV0OpFKpWCxWPTEnyJSMBiEyWRS+xSdDMPhEIVCAS6XC++99x6y2SyCwSCeP38On8+n5Xqz2SAajUq4oovBbDZLtFosFoq90W1BwYBiCdlGdNo8ffoUqVRKcGUKMFzu6cQhV4UwYS62R0dHGI1Gimmk02kxVbrdLhwOBx4/fozLy0s5qRhZ4/LPdp9EIiHO0LNnz5BKpeT0AG5FSb534XBYEbB6va4q9Wg0qqgImSP5fB5nZ2eYz+dqy5pOp/B6vUilUgBu29f4M6ZSKYxGI1QqFfzUT/2UzlkAODg4kFOFQpHNZkM0GkW9Xken08HP/MzPKBrGGnQypObzOdrtthgtV1dXSKVSqr0n2Bm4rZCmKEpmFB1OdJmwzh24dZlVq1UxSvb7PXq9HobDoURaclScTie63a7cOOQyUShbLpcYjUaIxWJqxctms+IO7XY7bDYbwZyj0ei9a5MuKJPJhEAggH6/r4gdYzjj8VjiSDgclmuEn886cAAwmUzodruC+k6nUywWCxwcHOiaZgyH5y65SXQsUrjebrfIZrOo1WqKCVEcZgsTr6larSbRs1qtSqgsFou4vLyE2+2WoEYXHGNqg8FArimyyNgYxRpuNlXxmovH41gsFqjX6/cazmazmd4r1tMzYsvrntFTsqrYmFWtViWcNhoNFItFeDwenJ2dqRGQkGsK0owzmky3VEA6btxuN3q9no4Zj/3V1ZVE8EgkIiGt3+9LuLTb7WpEJEdpvV7LVTmdTtXuxfOLTiVjjDHmJ2tMVivO/txb+A9+z9+C27z8tp/zwFnH70p/gPnv+ukf8qszxhhjvm9jtqD8Jz6N/O+6wmG0+1pf8saTMl7++Y9eGb6bTuH9py/hP5/e+wh+0MXug5cfu9up9VvzSPleX0x6mG7i9E8c/ABfkTG/0by2kDMajRCPx9HpdFS1zDjA9fU1AChaEg6Hsd/v0Wq1EIvFAADlchkOh0PV4az9rtfrWpwrlQrMZrO4BmazWfXV4/EYTqcTjUYDyWQSXq8XdrsdFxcXGA6Hqpq9uLiAx+NBMplEvV6H2+1W80ir1RJklALDYrFALpdDMBjEs2fP5NDo9/tagCiYEKbJilo+Ffd6vchms3JC+Hw+NJtNTCYTOJ1OPfFOJpMSicbjMW5ubhCLxe612dBhQYcSRSQAWmLYNEXGCyMmsVgMNpsNrVYL7XZbjiiyfWq1GtLptCrUp9OpRJ+XL1/ecyORUTGdTgWodblcqFarCIVC2G63qlQulUoIBoOKbNntdjQaDez3e/FK+JSfTi06K66vr3F0dIT9fo9qtaqFlREMukKcTqdqptPp9D1A7Xw+h8PhUNMZ33fyTMgmoSPJ5XLpaxuNhlxHd1kwrHBnxISgbPJuGJ/je8waZrfbLXcSz2O+B+QH3a3k3u122O/3Yv1sNhvkcjk5q1jRTOcVRTLGdhgPY4Uyl9Nms6l2Ijq0UqmUokF0eDAueDeqU6lUcHR0hE6no2YligNs6iJj6OrqCtvtFolEQuKJ3++Hw+GQQyiVSomhwrgMhVGKC4w/MZrISur1eq37xmq1EpvpbvyOkR+yiZbLJYrForhKjA0GAgE1uzGWyJgQRR065hhLtNlsePr0qZr6gsGg7kVsPyI7iT9PPp9XDTmvVwKEc7kcbm5uEI/HUSqVsNls5Mxj1C2TyUiMIgvr6upK8HjyhRjrZKyS1e5snNtutzpXySxiOxqjdLvdThBkcskYWSXMmY4nQuMXi4Uiip1OB36/X5HCxWIhNg4FptFoJIGdzWmdTkeiOQXtQCCAer2u8xqAXFCNRkNCI+OA/Fl4zVHI53lms9ng8/kwnU7VZEjXVbfbRbFYRL/fR7PZhM1mUyOb2+1GMplUW9b4Y346Zowxxnw8Yw4G8Md/6R9918+LWkf41H/wNSx/22d/CK/KGGOM+X6P2eNG9L9f+8hf9+TNElYn6Y/8ddvRCPj1p/c+flTcTqNfmn70LzLDcCV+TPPaQo7T6dQvxPyFmhwZPoUmCJlPg81mM6rVquImFotFv9Az8sAn6IweEJJLvg2Bux6PB+l0GqVSSQ6Y4XCIRCKBSCSCWq0Gm82m15bL5RAOh1Ul3Ww2kcvltJiydYdgZTp2ZrMZJpMJCoUC6vU6bDabntKybWY6nWK9XiMUCumpMdt32MLCVpzr62scHh4KPGs23x5yt9uNWCwmQDIXfkZSGJOg84ILPQUur9er5T4ajWI+n+Pm5ka8E5fLhU6nowYlCkp0qLByl7BbLlGJREKCFIGyHo8HV1dXOkYul0tcDzbMsD2GCx25LHa7HcFg8J5ARVCvzWZTLTiXcDqgCCxeLpfilDC6xiWQXCEej+12C4/HI85Rv99Hu92WMEK483Q6xdtvv622HwBotVqKB1GYoCuBQo3b7YbJZJJ4xPYowrxDoRD8fj8qlYpcZTx+5+fnEkfsdvs9DguBsj6fT0wnvs7NZqNrJplMYjweq3ktmUzi+voaDocDLpdLLoNKpYJWq4VEIiEHxmw207VM3hKXdYvFgsvLSwQCAfh8PqTTabRaLbUJkRtDVwmvicPDQ9hsNl2TJpNJDglG2NiMRacGxaBOp4PZbAaXyyVRzmw2o1KpyJU2nU5xdHQkGHSn00EgEBA0PBwOqxWNTh2KE7xXUfDq9/tIJBJyfLEJimKF3W5XHXwul0Oj0VBkJ5PJ4Pz8XA1g2+1WggAAua/i8Tiurq7w4sULvPnmm4hGo+J30X3TbrdxfHwsR5vX61XElLXuvI/yHLLZbDg4OMDNzQ2Wy6UYVBS94vG4vlej0ZBQTEg4xbNut4v1eo1MJoPNZqN2NDJlttstisWiYo5sh6NYRYeTz+dDNBpFrVbDcDjUceD9JhQKCW7ebreRSqUQiURwenqqdj+K0ARYTyYTdDodpNNpCWLPnz/X68pms2g0Gkin03LJ9Ho9zGYzte05nU643W5Eo1Esl0tMp1Nst1vVoTPKtdlskM1mxcOhC4z3TsKRu90u3G63YpzGGGPMT9ac/u9yCFsmr/W5n/FeYZq0/oBfkTE/FvO5tzH/PT8Dy8Pjj/uVfH/GZII1m4E1l4U1l4XlGw/BP0lz/h++Ca/927vuvtt0/73Zj42IYXl4DIdj/ZG/7ugzJcx+z2d+AK/ImO82ry3k+P1+ASjj8bj4FXQe9Pt9hEIhVTiHQiHs93sBQzOZDOx2O8rlshpDGo2Gasen0ykmkwkCgYAcEOPxGMViEdvtFtVqFb1eT4DgWCyGSCSC+Xyup/KM6gQCAVxeXurJM1k+6/Vazgwuw4yD0dVB0CahzOv1GuPxGKvVCtlsVgvxarWC1WqVi2U2m6HZbMrtslgsVOu8XC6RTCaxWq0EsaXYRZYL4a1c3OmqSKVSWmqvr6+RSCQEhOaCTzECgJ5+e71ehEIhXF1dYblcIpfL6Wdkg02z2dTXVatVZDIZLTJswmFsJxQK4a233kK1WkW1WkWxWBSXiAJap9NRBTEdTSaTSUDswWCAg4MDtNttLZcE2u52O8WXuDyTAcMImM1mkziw2WxwcHAgsYNuEOB20Q7dya2y+jyVSomF0Ww2MRqNMJ1OUSgUkMvl0Gw2EQqFBL9ut9v3ILx051DguOsksdvtqo0mEJjCGx0HbLfiok4o7263UztRv98X1JtCI3ku5DllMhk5S/jz7fd7AWnZxMNjB9wKh+SFMLq3Xq/13tEpRPGTNeUUv+h+uLi4kFOm0+kgEolITAOg5ZnXkdVqxcXFBRKJhGJdZGbRLUF2DY+f0+mE1+tVKxT5NcfHx1itVuj1enA4HNjv92g2m+j3+xLf2GLE4x4Oh1GpVHSsyUqKRqOYTqc6N91ut6rdGSuzWq1qwaNQQoeTy+XCfr+XqMMIEK/LWq0mAdXv96PVaiEcDqv9arlcKtZDsYDiN+9Ho9EI2WxWrKFgMKhGPZvNJhYNHXPD4RCRSERsGoLJKRLTKcV2KQpjANQWNZvNEAwGde3xHu9wOJDJZAQ4v76+Rjwe17VnsVjw6U9/Gu+//76cQxRleSzpeKOLhrXldGHSRdftdjEYDJBOp/X3Cq/7cDgs4ZFC5Xa7VUMdY7p0hI3HY0QiEVWej8djPHz4EFdXV4qKOhwO9Pt9ub7sdrseHtBZaowxxvzkjc22/e6fZIwxrznVP3Nb4Rz5S2X8kf/076Dzn5lw/p99DsN/63Mf90v7TY35rYfo/UIOvV/IovcLWcy+8BDmb/xO+0mY/c+9A9vh9+68DbnnGP/+Hw83Xvl3x5EPDj7y11nNO+xfW1Ew5vs5r33YKVyQccBGFQoCdHAwOkQWCGuWV6uVuCQUVAaDgSqZHQ4HCoWCGBxslmKrSCKRkLhDOz5wG7Hw+XwSOYLBIK6vr2EymWCxWBT54NPlUCgkrg8jPAQMN5tNLQd83WwsefLkCc7OzhRlIleDIFmPxyPwp8fjkUDByM7Z2RncbreiFozXkG0zGo3EguAxmk6nWpzoXrq8vJRA4HQ64XA4xLrg0r7ZbFCtVrFcLvHWW2/p+9P50uv19D37/b6Wpu12i6urKxwfH2MwGCCVSklUA4B3330Xjx490rLNP9tisaDRaMBkMqHf7wsaTUGEEYpCoSAgMF8nQcVc0lj1zAXPZDIhGAwqqlIsFiU+1Go18UJCoZA4HnS6MCa12WwwmUzQbDYF62W8x2KxiONCoZJRDdbGT6dTiZJ0t/B6IGyXrUF0oPh8vns8E7fbLadLIBCQwDUej1VP3Ww2YbVa4fP50G63BWR2OBwIBoPo9XrIZrPo9/uIRqOq3WbU5m70jlEYVkozerJYLLDZbJDJZBQvpHhIUZSNQIRAc3EejUZ48uQJ/H6/HDpssLvLwDGZTBIpx+Mx8vm8zodOp6NzldfBZz7zGVQqFQlTtVpN7p6LiwsUCgVkMhlcXl4qbsiIXLFYhMPhwKtXryTWeDweiQgUbVqtFhwOBzwej1qO1us14vE4vF6varEnk4kYR71eTyyWbDYrpxlFSTYysRmKVeDT6RS5XE5sqnK5LBGYETuHw6F71F0nEQU9RvkAiDfE+zDZWx6PR5wnm82G4XCoiu5QKKT7NYVFgo49Ho9ExXK5LDg8AFgslnucHYrjdrsdX/va1xCPxyVCdrtdxSwpHAaDQTGsgFtY/enpKbbbLQqFAhaLhX5WujH5M9HZyCgfr+P5fA6TyYRIJIJyuYztdisxk66caDSK/X4Pu92O/X6vKFY8HkckElFjGeHnm80GtVpNr59weJ6DrVZLDrdOp/OR/2I1xhhjjDHGGACY/d6fReKLfvzR/+nfx3/0234FvxR5CgD440f/FP/Rb/sV/K7/5T9F73/xc8A3doBP0pgcDsyzPuDOS18GLZj9q298fC/qI87o0PXaXJxvNy7rGu1PffLeu2839uEey63hLPwkzWsLOQSWAretIb1eT09zM5kMHA4HTk9P9cv5fD5XtIRPbJ1OpwSa/X6PaDSq1hwuE2RaBINBgS+3261ad5LJpKIRZNXQGUNhKJVKIRAIyH3BVpVgMKhlyu12q4qWjTrHx8f42te+ds8tQUfQ5eUlPB6PlkOPxyNoLJcWRq8YPyCTo9/vK3JCYWE8HmM0GikOwIV4u93i+voaPp8PDx48kLNluVzei4rwyfN6vUar1VIDEJfjbDYrpxOByu12W/EjLks8hoQe/+zP/izef/99BINBiTyEtQJQ9S95Q3SLhMNhMTiePXsmcDWFLC5sbNJZLBb6XnzyzmPIuBGjW3QZcTHebrdqsiJAe7lcqrnmriBElwr5Hvwagk/ZoLNe31oJuYSXSiUtw1zY6UrhOTYYDMRj4cK32WwQCoXQbDZRKBQE5nW73Xj69KlcDFyYnU6ngLvRaFR8pel0ip/6qZ+6V5nNGFMmk1HDUSwWQ71eF5+oVqvh4OBAYNiXL18qrsJoFiNkFMMYmWHrD5vDyK66ubnB0dERXC4XLi8vxaphqxNfGz+8Xi9qtRqWyyVCoZDYVsvlUvGm09NT7Pd7PHjwQFXmjHuZzWa0Wi0cHR1JoKhUKshkMnLJpdNplMtl1Go1OJ1OHB8f6/0nMNhisWAymaiVjdcPRTOKl2ya458F3ILFydKiUJlIJNDtdvXzBoNBWCwWwd7ZpESYNtu+CoUCrq+v5TIbDocSm9xuNwqFguKCFMjYOEYXFe+FFBYZ7WIlN+9JyWQS5XJZ4GU6weiM8Xq9Eog2mw0KhQKazSYsFgvC4bDcTT6fT3B5Xh/kGjFKRtC20+mEzWaTo6rT6UiIpStqv99LTJ9Op/D5fIpjAhCzh9GnzWajhkCLxYJKpSIGVSKRUISMAHeKSN1uF+PxWK7Ger0uxyNdc4zUBYNB1Go1uN1uRCIRjMdj/UyJRAKXl5e6RxpjjDHGGGPMR53Z7/1Z/K4/90/wPwg9h888/7afk7V38Yf/vf8XBn/ok+fMMT08wDL4rbGirfPHQ9j4SZvof/FFlDofvYnrtBGD9+L1YqjGfH/nIzFygFsQLsHHdLDwaXehUEC328VisYDP50Oj0VD8aT6fi8FA9wZ/+aaQweWa8QvGV8hgYKuUy+WSW2az2Yi3wqWAAsNd0cXhcGA2m0mEMJvNsvezlprMn81mg/V6rafwhLsSkMu4CVkzd5eY5XKJfr8vl0ipVEIgENBCS3eIxWJRbIbOBY/Ho0gQGRoWi0WuJNYqezwexONxXF5eYj6f4+TkRE/UnU4nIpGI4hms8SbfJR6Pq8aXkFzyY8g0Yl37fD5HJBLBYDCA1+uFy+VCIpHA8+fPtTQ5nU65L8gOymQyaLVaCAaDaDQaePvtt9FsNuWwIYzV6XSiVCopQtJsNuFyuWC1WpHJZLBcLtFsNrXQHRwc4OLiQnG0yWQCt9uNbDar9yEajeL9999HKpWSe8xqtSpuwuWPgNv5fC4nCeHAbOOx2WwCcO/3e8xmM8TjcUUJKdwx2kQ4dr/fx36/h8lkEuCa54Tb7VZkic4gtg31ej0BjwkJpwBVq9UUA5lOp3KNMPpGwdPn86HVakkkJMMqHA7jgw8+0HE2mUxqc+J7z68nwDgej6Pf76tqnMs8nUN03zEuw5a6u9EUq9WKRqMB4BYavlwuBWYmI+cuf4qCJM+rq6srcZjotnC73fdcgeQpkbHE84WuNx4z8oIoqlKEiEQiaLfbivYw1kQmUCgUQiwWQ6vVgslkQq/XQyaTgdVq1X2MvCS25JHvxOgRf1Ze0/1+H48fP8aHH34ocYWwZ34PcpQajQZCoZCOC8UQipt05hBE7Ha7sVqtBAxvt9sAgEQiIfcghZJXr17h+PgYLpdL17Pb7cb19TWSySScTuc9vgzFzdVqJbYTheL1ei1hCbhtPOOx5XtMIDEFfcai6HrjucjoKd/D9XothhCFNADiUl1dXekcpuPs/Pwc2WxWUVI61ugOtdvtyOfzmM/nKJfLis7RJZhMJu/BqI0xxpifrMn+VQuG29eLiPyll78V8X9c+gG/ImM+aVP5bVskrMPv+nlu8xLz3zuAyWb/Ibyq7+NclGEfffsq7Z/oMVtgslo/kS6rzeqj83721x7sv/bsB/BqjPlu89pCjtvthsfjEYuCkajLy0vFMKrVKhKJBCwWC9brtRpTHA7HvYgNgcF0tdzc3AhabLPZEA6HxUkgBJeNWfv9Xt+DsQYu34w90b3COAeX0F6vB5vNhlgsJmGKcFla+blwMS7h9Xrh9Xoxm83ELyFslUsfWTDJZBLn5+fIZDJqRMrn82Kr3G0b4p+1Xq8lzDDuYzabMRwO78F4yWtwu904Pz/HcrnE8fGxBB0uMDabTaKZ3W5XnTCXVj7VZ6sR34dIJILtdoubmxvkcjmMx2OEw2GUy2VEo1EMBgPxQLjgUyDodrv6udg+Fo/HJXzdbS9jQxTbh+hWaTQaeq0UhOh2mEwmWvT5dJ8RCb4vfJ/4NP3i4gLpdBoejwf1eh2NRkPAVDoIqtUq0uk0Op0O4vG4In2Mh00mE3S7XRwdHaHb7eq8ZiwnEAjA4XDg5cuXOtf5RJ/CIfBNETQSiaDX68ndNJlMBEOezWYSMdmYQ4YIHW10CRHKTacJIdClUkkC1OnpKSwWCx48eCCRj0IpG3xarRYGgwEKhYJas5bLpZwvw+EQyWQS/X5fYmkkEpGoUq1WBQIPBoPw+/3o9Xrodrvwer2IRCKKXfIYkH9DAaVWq6Hf76uJjYs0xVQKb6vVCu12G5FIBLvdDpVKBYeHh9jv9/rodrtIJBISTHl+8Jq9vLwUXykSiQhavd1uxbAia4X13BQsyUPie0phgHFInhuZTAbValUCajKZxHA4RCwWQ61WU6yQTXFvvfWWzg0Kq71eD+VyWcLV3fOWrp9wOAyHw4FsNovBYICbmxt4PB5xjywWC6LRKG5ubvDo0SPM53PdS8mbcbvd8Pv9Em/5M/X7fbni7t7jYrEYRqMRdrsd6vU6Hj16hMFgIEGfDkDyh9hyx/sfHU4EhvPP5vm0Wq30d8BdgPrl5SU+97nPieGz2WzgdDpVj07QMUVLQt1jsRhevnwJm82GbDar+yqFtslkgt1uJ4HNZDLpfhEMBvXfKDoZY4wxP1lj+fIL/J/Pf+67ft5458LuX4SwqVR/CK/KmE/KWA8KCMVfn73ypx79Kup/7JMFjN1Np3A158Cdxmz7eAffP3358b2oj3kswQAWv/2nMfoffwamn37jI9eT/6Bm94VPYfmvfxbLf/2z2P/cO9/x8x7+ue8uPN6d9daCwPlv9tUZ873Oaws5XK7Jx2CTD/kJrKyl24VPWglW5aL84sULeDwexGIxVTj7fD5cXl6KTeJ2u/XLNpkkXPLX67WqqgkzpuOGT8UJxeVyvVwuEYvF4HA4xHzY7XYSUwg9rtVqiEQiWtS3260WPYoIFFcCgQDa7baamgg4feONNyTY0AHQaDT0tJ+8Cv58T548UbSEoFi6hBaLBbxer2JiAPS0mgsVG3rS6TTcbjd6vR6sVitCoZCaYcbjMQ4ODtDv9zEcDrFer+WKSaVS6PV6aDQaCIfD8Pv9YkgwMsaIhM/nU/MNnRjj8Rjz+RzHx8dwOBw4OztDKpVSZS8rfLlcM47icDjQarXEUTKZTFrgrFaronN0IHB5Z5yPIFqTySSoK2Mfi8UCh4eHmEwmGAwG96qIyXTy+/148803Ua/X1fBEwYig1tVqhUwmo4a13W6nJ/ms3+YCvlgs5MiiK4bRpLtsKKfTqfiKx+ORs4F8jtFoBLfbLa7RbDYTq4hRLS6W/X4fwWAQ0+lUtdQffPABIpGImCq9Xk8cEC76PHfsdrva1bg0LxYLuRx47tLJRtcGRcFUKqXI3Hw+x263uwegpiBAUDTrrQmzvri4UNMb40P1eh0HBwcAboUnihG8xhgpSqVSaLfbCIfDarnjoh6PxyXu8HoiEJhxxPV6jUKhIFcT3SJ0m9DtMxwOEQgEFEljDXm9XkehUNB1zvOd0TKeM2Q8UZSjWESHFhubTCYTotGoGDZ0L9HRE4vF8Pz5c/h8PmQyGUXCXC4XIpGIXJCMvN0FkQ+HQzgcDgGuC4UC9vs9yuUyIpGInIvdbhfhcFiCqM1mQ7/fh91uh9/vh9vtxtXVlUT0arWKo6Mj2O123UMHgwF8Ph+Gw6HcWWwJrNVqisAxysjzlJE2NtRRUKfTiuclxbVer4doNAq73Y5Wq6UYGqOiBMIzDlmtVhGJRASjprjE9jMKQuv1WkwucowMR44xxvxkzn65RPrfXeAvvvyt2H2HX5d3MOOv/c3fifRf/OIP+dUZ86M+7f9eGn/ywa++9uebscP+E1h+tH/vQ4S+0kbovdsPzxcvsB18NDHg45zwex28KCe/56+frBwo/KPbh9Nmjwezzz/AymfG3gwMH3gx/an89+ulfvQxmVD9059H6c9+HsN/fwrvn67A+6craP+ZJUp/9vOo/6nPf8uX7BttnH6x+Np/RPO/ziPyX37p+/iijfko89pCDt0iBFKm02n94my32zEejxEKhdBqtbDdbsWjoQhAOCuBoN1uF/v9XlGCWCwm4CyjR/l8XryDUCikX/YJ6qQDIhwOqwkHgJbh8/NziQUEXTIWxUgXAH0fLvCsD2+1WthsNmKgrNdrRUP4ZDcWi2E8HqNcLiOZTKLT6Ujs4jJBbg3FmVAohOl0qmaqeDyO7XYrkYECGWGb0+lUIpXFYlE7jslk0r8jrJVgYrbcMAZUKpVweHioJ9rHx8dot9vY7/d6ik0eznK5VAQtlUqh1WqJW8SlLRwOA4CW5Uqlgmw2e2+xAiCXy2g00rEeDofo9/tIJpNaxg8ODuDxeFQHzIgFuSeMW5EHs9/vUa/X4fF41JjExXG9XsNut6NUKiGXy6l+mvX23W4Xq9VKsFMuhrPZDFarFeFwGFarVTEMChk+n0+sJjpAnj59img0iu12i4ODA4k7bOi5urqSa2m73SISiaBer8NiseD4+FhLKsUo1ouHQiEkk0kdE15jFGOi0ahcOrwuzWYz3nzzTUVxyCAqlUqYz+eKNdJxVSqVkM/n1QJHsalUKsmBwnOIzieCmOkqYgMXADktuADznxm9osuFSzwryu8CZx0OB54+fYp0Og2/3y9hjhG2XC6H9XotyHY4HBZbiILKixcvJMyQucSl32q1quGOFeuRSETiKF0pfr8ftVpN9xa+1l6vJwcU3TMU3nhuORwOPHnyBB9++KGalPg6KTRR2GHVts1mw+Xlpfg4FN4oLn/44Yd4++23BUinq6xWq0nEYyxvPB4jlUqhUqlIlI3H41itVnKc0UnFKCOvGTYL8vzzer3odrtoNptykwUCAblzGGGdzWa4vLxEsVhEp9PBw4cPMRgM7gn+fr8fl5eXeu9ZRU520b8ssFCcYq37ZDJBIpEQ84fHKRKJ6B5JRhLPJ7o+N5uNfmaKfqFQSDFe3sfI4aEbcb1e32vAM8YYY36yZnN5jewfbuK/+rd/O/4ftZ+99/Hnv/o78Ld+6XMo/KdfAfb77/7NjDHmx3H2e2xPL7750fnewcEfx2xfncP5woXd/nuLQS02Vlj+2dcBALNffONbmEGrgAWWJw9+06/zI43Zgv7/7OdQ/7uPkP+laxz94hXinm8ybDKBIY5+8Qrp33GD+n/9GOP/yecUA9uNxzj6jz/A+T8r4qob/o5/xGk9jtrfLCL5f3zPuP99jPPaQg6t5pFIBBaLBefn5wJB0oJOAcDpdKLVat1rN2o0GnA4HAgEAnoqTWArHRNcjhk1uQvSZKPOer3G8fGxhA3+4s4WF0YGGE357Gc/K2eG2+3GaDTSE2NGNfh6uQzRUcSYT7VaRSwWE2+HbBE+ta7X61pgWHNLhsl0OlWMhU4kVk7zKfZ6vUatVsNut4Pf70cikYDb7cZ8PsdgMBC8mLweuqAIXs3lcnJUkJditVqx2+3Q6XRwcnKiOmKHw4FisailjEvxYrFALBaDx+OR0EGXi9vtFox0Pp/D4/Hg8vJSrBHWzrNyORwOq47dbDYjFosJhDscDuXMoFuBTBIAautipIZP0Pl6AoGAYiuMs1DEG41GiMViEgMzmQwmk4n+fb/fx2g0QrFYlCtlPB4rVsPYF/lCbNbhIliv15HP53VMQqGQeC+bzUbCGONKd8XFuzwZp9OJbreL6+trXVe73U6uMavVKmHJ5XKJz+JyuRTNabVaaltio0+v10Oz2UQmk5Fb6fr6GtFoFNFo9J5rZDAY4NGjR3j69Kleb6vVQigU0nXE43xzcyNwcTKZFCQWgERWk8mEcrl8jyvDn5eiHEWf0WgEi8Wi2Aw5KxQE6Iqp1+vIZrN48OABarUa4vE4hsMhJpOJhOTpdCqWktfrxePHj9WKRUg1GVvAbSOW2WxGNpvV5718+RJHR0fiAwUCAfFeeG7SPcVrdLVa4fT0FIVCQfdAusnItaGrJhaL4cWLFxId6BYkYLpUKqHb7SoK2O/30ev15A7cbre65xGWTIZWIBDA8+fP8eabb6JarcqxZzabJUblcjm8evVKzpK7AOPJZKLY69tvv41er4cHDx4IRs2IJevWeb5TXKQISu6Ty+US7JvOFgByClIoZfR2Pp9LOAcg59B2uxV0vlqtIviNWvl+vy9eDt2WFAu9Xi8qlQqi0agEODZfMW7b7XYFiiegvVaryYnIuvH9fi8hfbFYfG9/uxpjjDE/FrPt9mD+/30N+1+s3vs4/IPvY3Ndwv4bUXFjjDHmkzm5v/QVTFbfm/t29MU4sL/9PXNr/1YxaG8G9rYfrtVq+Ac/i+j//Ab54ABm03cWWazmHfLBAfz/ThnTf+Nn9O930ykKf/aLKPyZOcp/7wCXv3qg/3baiKH89w5w/O9cIfbXv4j9evUD/VmM+Y3nI9WPp1Ip3NzcqCWKPAlyFAjjZYWr0+nExcUFrFarnsxOJhPxMBiviUajcskEg0HxdwgDvlvhzPaT0WikX/JNJhMODw/lFKD4Q54Fa64ZJViv14r/TCYT1S1TxInH42raIXQ0EoloYa/X6/B6vViv16omJiOGToC7Nbp8us6oBJcFglcp6vDJ76tXr+B2u9VkFIlEcHl5ibfeeksNO6x45tLNhY9tUaygttlsePXqFXK5nMQ1q9WKWCyG9XotOHU4HNaC4/V6tWwDELj2/Pwc8/kc0WgUuVxOLVjpdFpNM9PpFJPJRF+zXC71GiuVihwdqVRKfBHGp8g0YQyKUGY+SZ/P5wgEAnr/ebzi8bgcIDwfR6MRlsslksmk2oDYLMRFd7/fi5HkcrlgNpt1HvA9YWU9220mk4lae/heB4NBBAIB9Ho9/ewEs5pMJjFG9vu9nFGBQEBiF2Ndg8EABwcHWm5Xq5UEFUKqKfwxdsgllSIjIzA3NzcSVVlxT3cbYcV0tLCKmVBuwoEJm00kEoqo1Ot1pNPp25vHN9x2ZD1xEQYgzpTNZoPD4UCv15PDwuVyYT6fi+Hz8uVLbLdbOJ1O5HI59Pt9VKtV1VWzBY7HJBqNiss0GAyQyWTkGqJTptvtwmw2C7qez+cVaaLoM5lMEAwGEY/H8fLlS3GnGAfdbrdyfjQaDUSjUV3Dh4eH+j50rvH62Gw2EgzJnioWi3j16pUimIQR+3w+ZLNZxTdfvHiBaDSKRCKBWq2GWCwmcPJ4PBb0m5B3tgcS/O31evHy5UtVtPNYRaNR3TPZzsT3L5PJwGw24/r6GqlUCs+ePUM6ncZ8PleclmLYfD6X6LteryUU83qkqEV3Cxu2yCAjTJ4OQArKZCcNh0MxligAvvnmm4qb3tzcCOBMAZmuJtaSM44F3Ap3Ho8HgUAAnU4HVqsVuVxOLVh3eU50k1FoorjL+6AxxhhjjDHGvO5Y1nuMd67X/vzZzgGLoQl+LLNfrbD6vyUw39he+2s2OzMuf/UAhb/yVI4U/9db3/J59tEOOP8hgdDNFgz/rc/B/oeav6GA8y+P1bzD9g93MPl9P3sP0Lw9u0TyL/8aCv/br2L9Z2JY/5kYjv/sFMm//GvYfuNBnTEf77y2kHN9fY1SqYR0Og2z2axICGMj4/FYHAuyY8gfyGazaLVayGQy6Ha7sr47nU5Eo1G0Wi3FEsLhsBbUTqcDp9OppRq4rT6/ubmR6MLIA/9sOhRokSfgk0s9AIzHY3FpKDRst1s8fPgQs9kMrVYLy+USuVxO9cXj8RjZbBY3Nzf3xB3GMbjgE0LKBW+5XIpzA9w+mWa1cK1WkxBAjkOhUEA0GpXjKZVKqfVnNBqhUCjoe7rdbkUpnE6nnnpzaavVaoqe9Xo9xWcGg4Gaw6LRqOJPbHOhA2i/3+tpPZctwmIdDgfa7TZisRg6nQ7m8zlWq5XcRKwRZovXcrlENpvFarVSg81sNhP/hk1U5MqQ/8I6e4vFguVyqUrzcDiMwWCg5ZScmFarJQGEgh4jYa1WC9lsFvV6XdG88XisJqJYLIb9fg+fzwer1YpqtaooGM8d4LYivVwuo1gswufzwWKxoFarSZijE4D8J/JyCFWlmyGdTktcSqfT6PV6qFarggczFkQmTrFYRKVSQa/XU7zm4OAAl5eXglGHw2HFu8bjsZxhNzc3EnnS6bQie3xfGJ3abrcSS1lZ7XK59DPSqcTvRQHX7/fLwWCxWHB1dYVQKKTXGo1GVX9Opo7L5UKr1dI1s1qtBCy2WCzodDrY7XZ6T+jYAYByuYxf+IVfwHK5xHQ6FadpNBqh0+kgk8mIaRSLxbS4B4NBOeT2+z06nQ6i0Sj8fj98Ph9OT0/hdDpVD09AO6998oroHLzrJOM1uVqt5EK7K+7k83lVovO6BIB6vS5+VrfbhdvtVtMTr3F+fzqSYrGYWGXxeBwXFxdIJBKoVCqKQ1GAoTOKnK27LU68VnjeTadTHB8fIxAI4Pr6GolEQm1vFGPI4bkLqKZYy9dKMScejyMajcJsNqPf7ytmx9cB3EK/6Zw7OTnBwcEBSqWSYlaEEvNnHQwGCAQCujbD4bDcdeRvjcdjzGYznJycYD6fy8XE6zOZTKJSqeDx48eKYRIsT4ZPPB7Xvd8YY4wxxhhjPsr4f/ld/MUv/muv/fl/7eW/gvj/wWAtfSyz3yPwX30Ji/8ihcHi9cS38j8pIPcffxG7O78j7JsdeKtLWFa3Ioqjv4WrOsXuG9zCH/SY33qA8B8uwe/46E7iiGuGwB8rw3JU/Jb/tlssgC99AHzpA2xPL74Pr9SY79e8tpATi8XUAEOHC7kxbJJinIe8GbajzGYzWeWj0ahAqIvFAqlUCgAUqSCclhEdOkO4wE2nU2SzWVn96Y6x2+368xhPsVgsGAwGWmAISnU6nUgmk/r//DqPxyMOz2q1krPoLviUDTh0Ifj9fgyHQ8FVyb1ZLBYIBAJ6Uh8MBhVpoYgFQO4QOlh6vZ7cLQTJkjdDKCrjGXSX8GdgrIpP5BnDmU6niilxmTLdUVwZjyKUmsLN9fW1IgpspaELgmwaimis1aZLicyWUCiEavWbTQ533VysgHa73bi8vEQikZCAAUDHirG73W4nrsWrV68EeGZsjg4YVqWPRiPU63Ut0olEQg4PRoPohqC4kM1mUS6XMZ1OVTHtcrnQaDTkBorFYnC5XGKa3K1vp/uADhjW0NOhwBgIwchkc7Cq22QyyVmWTCYFj261WlgsFshkMgAgEPd0OkU+n0ej0dCSu9vtFEmjs4BxIdbEu1wuCZg8fnR+MJbj9/vFdxoOhxLM2GxVKpXusaX4vh0cHKDdbiuexGY5CmYej0cuq3A4rPeXgh+XdHJ1eC36/f57bUr9fh+bzQapVAo2mw0vXryA2+1Wc1gmk5FThMwoi8Wi64YxydFopGNAh9ZsNkM+n9fxuLq6knBLLlY+n0ez2YTH47nXbsTYJxkxfr9fEHcKSrx3UMgaDocYj8eKBhICvV6v1RDHe+9isZCYGgwG0Ww2df3RFcP7Lo9fo9FQ2yBfe7ValcMrl8vpXsF7Gc9j3ksJeWec8y4frdlsYrPZyOVEds5yuZSrhfG6cDiseweFr7vnZjKZ1H3cZDKhVqvJ2bjZbFAoFBR3o+OIscjRaKQ2OAq7jNPyXg58s0mOTiO675xOpyKovN8QyGyMMcYYY4wxrz37PQ7+JlBZffe/Q8Y7F0L/d6/BGvmYx/u33oX1r0Zw9s+L2O1N97g5/Ofn12kM/loe+b/y/re8X7vxGOb/7msIvFtF6MstuP75yx9qLffl7w9+JCfOvzxm0x7X/2bq+/iKjPlBz2sLOXa7XU9d2bjCGAabo8hbyGQyqNVqesp8tw2GbSbxeBxWqxVnZ2cCKS+XS/j9fgSDQdVRZ7PZe1Dl2Wwm1gEbgpLJpJqt2Ao0m80QjUbF0gCgp/uhUAj1el1LH+MJ7777Lk5OTtRUcnV1hXg8rrjSxcUFTk5OxLoYDAYSmfL5PNrtNhaLBfx+P0KhEGw2G1qtW5sdl40HDx7AarWKt8Ka9bvLDt0vkUgEV1dXGI/HePz4sdwyo9EIqVQKLpcLz58/Rz6fx2KxQDKZxOnpqaDD+/1evBLGjmq1Gkwmk9xQXHTphOAT7n6/j91uh0AgIHEhGAxqybu5uVEz1GazEYNlMpkoKmexWNBsNtVuRCcCXQxms1mNNm+//bY4Hk6nU01Po9FIjiI2o223Wz1ZB275TfV6XecVnRUOhwO1Wg0nJyewWCyKrFE0Y+X2fr/X+UxHRjQaRa/XU0zu05/+tARBChs2mw3tdhs2mw0PHjyQ6Hi32pyiCaNXAASotlqtimKVy2UkEgn9jHTMeL1e1Ot1HB8fo1qtatlMpVLiQbVaLfj9fgGVO52OFnuKFmxFI2OFgGaHw4FkMolms4lisYjz83NFsZbLpZgmvV4PXq9Xoi2FP5fLhfPzczmWrFYr2u22Im4US7LZrMDMdHTsdjv9GdFoVMs8+TBksiyXS8znc7TbbWQyGYHQr6+vEY/HMZlMcHFxIecbo048B/v9PjweDwaDgZw3jFSylY6tR4lEAs1mU/c9RnpsNptEWrJw5vO5zm3yaSgok691cHCgf1cul8XnGY1GOr8odNLx5vF4BPzmvdflckmguhvFZDvWdrvF+fm5rn3ysnq9ngSR2WyGR48eod/v4/LyEl/4whfQarUwmUwkyNABEwqFEAqFJBpNp1PF2xhppfuF1yydQBSPGD0k2J1C8Gw2g8lkwoMHD8S0uStcXV5eSrTxer2wWCwCIgeDQbnLer2eWqYYQaSwwwYvRnKj0SjcbjeePn0qcTYcDqPdbgOABNpqtaq/A+ju2hu/WBtjjDHGGPM9jO0fv4df/Xe/gP9L+efR2fi/7ec8m2fxy//hb4P777z7Q351xny7cfy/v4zD/+TrwB/YAH9gg8rfPsCLrxb0z4/+5Dk8v/Iudt8wGHy72ZQr2J5d3nPr/MDHZILnrd5v+tvs3zZcyJ+keW0hhwwY2s8tFosAxPwlnlGG/X6vRTCXy0nsubm5gcViUQSFEQ6Px4Nms4lYLCa2B38RZ7MJHSRmsxmlUgmxWEwxrF6vpzgXa6LvAlNZ/e10OuF2u/Hhhx/KkdDv97VE8Kk5uQ6s4aVjJR6Po9PpAPimY2c0GiGTyaDZbAqOSlGDDgZySuh+sNvtSKVSqFar4sVwuRiPx1gul4oOkQNTrVYVxSAw1m63I5/Po16vS/Dg8kdIZzweFxB0s9kgmUwq7kTnD5uEIpGI4k6r1UpLFOHUrVYL+Xwe+/0eZrMZX/va1/DGG29gPp+Lt2GxWNDtdsVIKRQKOsYUgRibKxaLWuYZ0yKfhrE4v9+P6+trNex0Oh0kEgkAt1Ezp9MpF8Jms8H19bW4PQ6HA9lsFs1mE/P5HJlMBq1WC6enpxI3+F5RXPN4PAgGgxK7GN9rNBqKFFGsm81mWgbpjmB0kE4tu90uVweX6ZOTE7TbbVQqFVUqMzYznU7l+CFs+G7t8vn5OUKhkJwyjPvs93uk02lYrVbxl9jGxnOPTo14PI5yuax6abp5ut2uhAe2vFGsjMfjSKVScgG9ePFCzrcnT55IiKpWq4pZzedzVKtVCYRcyBmbG41GctYR3ksQttPp1LVA1w/PwXg8LqFxs9ncc3kx6jgcDrHdbtWydpfVRMGUYjOh32TbpFIpCT9er1cMlWq1ikAgIK4RI6UEmsdiMWy3W/j9fjkV6dZjexLbtygi0WnGWGE+n0elUpF7xeVyKfLEqNHh4aGcOoxZUbAtlUoCV1NoGgwGMJlMAlEzMnTXDZROp7FarWC1WgVVZ8MfHYTz+Rwmk0kw89FopKgUm/cY06Q4SVA6r5W7n8Nz2Ww24+LiQmK9xWLB48ePBYun24YiJR09ZrNZUVWz2azPjcfjqFQqeh8IWWfbFt8zh8MhTlUmk4HdbldcjQ8cfD6fWtaMMcYYY4wx5qOO9Ve/AvtvvcHf+Ou/hD/31d+hf/8Xnv9r+N/88u/Hr/35n4Xnbxsizo/S7GYzbJstbJstJP7zX8Pxn/qS/vmTVK1uzI//vLaQw1gJeRVc+il+MJ5ELgljCLPZDMvlUgtasVhEq9XSQuN2u+WAYAsWgQ6Q+NoAAJlaSURBVKR0krDFikwFwnBZGwtAkFM6SOi2MJlM2O/3Yvd4vV5BN/v9PoBbvsVwOMTDhw8FNG42m2qkGQwGgmEyPkbRZj6fy2FAwYSLRbVaxTvvvKOn0FxqWKnNBYluEr/fr8YpRhPW67UEDP531gjzf6fTqRwkXAAPDm4J491uV0IBj3mj0cDh4SF2ux3y+bycIoxu8YOtN1yaGFHZbrfYbrf46Z/+abz77rt4+PAhqtUqksmk3ByVSgXJZBLdbletWnSyEGp9V8iiMNJoNBAMBhGJRLBcLnFzcwOHwyGuC51OFAvb7bZal+iyIDOIQojf79d73ul0JPw4nU68ePFCTBYu7HRI2Ww22O12ZDIZVKtVdDodRdPIPOG5TAEtkUjIwcDq7k6ng06ng1wuB7vdjq9+9as4OjoSD6jT6YjJAkDXBYB7cOLhcIhisYjpdKqWMkbXCBG2WCxIp9Oo1+tYr9cSK9kwtNvtUCqVEI1GFTOLx+P3mnsYoWT9OCOIz549g9/vFyj86OgIs9kMz58/RyqVQrvdhs/n07UzmUzw1ltv4Stf+YoEXNbO0y3CKB0dSMlkUtcQwdNkX43HY7me/H6/hIHhcKjrcrFYIBKJYLFYCIjL40oBaTKZYDabIZPJaNFvt9uIRqMAoPalUqkk5yCB4Vz+Kc5YLBadM/v9XkDu0Wgkx9VdQc3hcEgUonDlcDhgNpslODKOxnshG7b43p6dnWG9Xktocbvduu4dDgeq1aoar/hn03nH+4nNZsNq9c2mgffffx+pVArRaFRunuFwqJji9Bv5bkLWeV74/X7xtnjf5dcFg0EJunx/XS4Xms0mwuGwOE6bzQaZTAbb7RaVSkWcJp/PJ54Y31uytxhBjUajiqTSfcX3hMI+/x1jnIyWPn/+HMFgEAcHBxiNRhIy2b7G62/2Gzx1M8YYY4wxxpjXmfj//tfw8E/V8Tf+yO/A3/gjvwOFPzFE/n/9a4YTxxhjjPme57WFHPJV+AtuuVwWQ4YxAtr+6dwhJ2e9Xgto2u12YbPZ0O12cXR0hOl0il6vh8FgALPZjFwuJx7CYDBANBpV6w+ZGwQdt9tt/aLNamY6DSgo8ak8gajD4RCf+cxncH19rUrsfr+PUCiE1WqlmtpEIiHWDBub+P8B6Kmzz+cTX4NOCfJedrsdPvjgA+RyOZhMJnFL6DDJZDKw2Wxwu92COLNVym634/z8XFXjXPQYzeCyTaHG4XAgGo2q7YnLE3AbPSJ8ly0wL168wPn5uRaleDyOer0OAIL+UuhKJpP36s15HgwGA7Xr0DXC5ZQCBZ/YHxwcKNbDJ/I8X9hmdhcYWyqVYLFYkM1mAUDnAjkqAAT8DYVCeuJOQYV8JR6n0WgkxgyjHb1eD8fHx2oo8vv9KJfLiMfjgnfzPTw6OkI6ndayGY1G77mKGPegiyQej6PRaGC9XuPo6EjiAa+X09NTRRBZ+05Rge607XYLk8kklwsBrQTRZjIZJBIJHcv5fI5yuYx0Oo1kMqn4j81mQ61Wk+vl5ORE7hOKThQL2ISUTCbltCKU2+v1olgsim81m80E/LXb7RJCEokEvv71r8Pv96Pf7yMajeLm5gZerxf5fB42m03HvF6va6En04bMpIODA1Vg73Y7ZDIZeL1enJ+f637Aa5H3J4p0rEsnU6ZQKEicuLi4QDAYFDCYYiPPEQqobANbLpf41Kc+hffee0+AcKvVqghWPB6H3W7H8+fPEQqFJCISVkyeltVqFRicjDE6SDwej0RmVoaTy3VzcyOQMwU+ssiA23p4m82GbDYLr9cr9yIB5Dwfe70eSqWSXDEUeBKJhMSQfr8v4XO73UpAIlMmFAqhXC6r8Y1Om8Vigf1+L7AxnZGNRgOhUEjHhyI3HUp08gC3QlssFkM0GkWtVpPTbTqdYjQaIZfLwel0Co7P+wlFWNaxWyyWe/fuxWKBXq8Hv98Pm82Gy8tLOJ1O/MzP/AzG47GE9FQqJYGXzCsK7MYYY4wxxhjzm51NownzP38f5n/+Pjblysf9cowxxphP+Ly2kOP1emG1WgVwZbsKGQmsPibzIRQK6XNXqxUmkwkePHig1qLpdComxt34CSGdXO7MZrMWUC42Pp8P/X5fVbN0DLRaLTkm2ALEJS+bzUrI4cIxm83kLBgOh/rnk5MTTKdTzGYzcXUIyiWngzXgsVhMleSM7/j9flxdXSEcDssh0mq11AJEPgnjFBaLBaVSSfGjuwLUbDaTmEOnDl/TYrHAG2+8gXK5jNVqpWX+0aNHGA6HYnmQjcIn8Ha7XZBlLrq9Xg8+nw+Hh4d49eqVnFAWi0XLNnkbbELiosdq+Uajge12i+PjY1V+7/d7DAYDLJdLAZdZD0weEnlHXIjpumAMhOcBK6AHgwEAwOPx4OHDh7i6uhJ42+/3q8koHA6j0Wjcg6qSFzKfz5HL5XQeJpNJnJ+fI5VKodPpyJHT6/WQzWZ17OPxOGw2GxqNBtLpNJrNpqq/GaliNC2TyaDX62nZJAyaPJh2uy2mC8HUXILPz8+RSCTkBlsulxgMBnjw4IF+tmq1KjGNCyxZIuTUjMfje8edLrC7C+p4PJaQShYWIyYUTXl98pylo6zb7eLJkycAgEqlgtFopCgeo4B0/1DY4zWaTCZ1Td+NEHm9XokQjJwRxrxarZBOpzGbzcRyoeBCMZZQc55LAFSlbTKZEI/H4XQ68fz5c3g8Hng8HnGCTCYTOp2OKqsZ9Wm324Ih12o1na8UEev1OlKpFJbLJZrNJuLxOBwOh8QXtkYRyk1YL+NWLpdLglk8HsfNzQ0ikQjS6TQ8Ho9EDbJn2MB1975CILHFYkGr1RKkd71eKwbF+57NZlOkkAIYHXQEKPNacTgcAKAmN76vFOr9fr/cfPv9Xu15bCtrtVrYbDb41Kc+JZZRuVyWWMr7yBtvvKH3pVgsijVFwHetVkO320UgEIDD4UAgEIDf75ejjQJcq9XC0dERut0uqtUqDg8PYbFYdF5QtLy+vsZut1Pr1c3NjYRlu90Om82mBwDGGGOMMcZ8/8aaSmL8Bz6H8R/4HPC5tz/ul2OMMcYY84mc1xZy+EtuJBLR8kGg6suXL+9Bg3u9Hmw2G6xWq7gUBO+u12scHx/Lhu/3+3F5eak6aC7irIhmnGs+nyOfz0sA6ff7cDgcEggIunS73aotDgQC99gzDodDFb0AxM2YTqd6wk3mDvksbrcbkUgEzWZTLA0CfAlgvftEGwBarRZisZiewjNOMRqNMB6PBdtdrVYSoMidGY1GElGi0aiO63a7lfPh6uoKm80G77zzDgaDAVwul578kw9CNwldIKxjD4fDEgXC4TAmk4mYNg6HA9fX1zg8PES73RZnJpVKKfZBQYAuKJ/Pp3/2+/2KihEOS6YSl2OCphlPAnAPqur3+9FsNlUJ/fWvfx1HR0dy+ACQ44CV2IVCQW1abrcbfr8fsVgM/X5fXCMCm6fTKR4/fqwIyGAwkKDDyAV/5mq1qiW/VCopukJujNPpVLyHkamDgwNFw66uriQgMYbHFq67zq5sNotqtSo3z/HxsSDCFBO63S6i0SiGw6F4OYweEQpcLpcVQfR6vWKLUMiyWCzIZDJ49uwZTCaT3AdWq1XV5avVSsefCzab0W5ubrBer+81D52cnOD58+dyx9CldrcxjgInIeaZTEZOG1677XYbvV5Pwh1daXSFkcdD0YwOqsViAY/HIxDvZDKRuEvBabfbqV3N4/EI1JtOp+Vq+eIXv4hwOIxkMolkMinRiND14XCIQqGAdrstt9d2u0WpVILZbMajR4+wXC7FT1osFmg0GsjlcmpNCwQC6HQ6Eih5bS+XS8xmMySTSSQSCUwmE4TDYVitVlxfX2M8HiMej6PZbGI2myGXy0lEe/jwISaTCfr9voQ4inTkTlHYZNzJYrGgXC6LOxONRlGpVDAcDgWJZk08o5K83gKBgKJoFMsYtWJzFYHYkUhEwGnGaxmTooOGzjA6AHe7HeLxOD744AMkk0k9QAAgITGRSNzjZhGAbzKZ9HodDgcmk4nu9xS1KGq73W7dM+fzuZxYwWBQAO56vX6vvdAYY4wxxpjvw5hMKP+bhxicmDE4MaPyi15YHp983K/KGGOMMeYTN68t5DidTgQCAS1Bd5/CA1CNLcGjy+VSfJlYLHYPInzXvTIej8XC2Ww2ePbsGaxWq2IVd5cgxhX8fr+WW7PZjMFgoCYYp9OJdDqt5iousmxM4pN2NvuQ27Db7eTsqVarcLvdEgHImmBsiPETLvKE2CaTSVxdXSGXy2mpI9g3Fouh2+0K7Go2m/VU3OfzIR6P4+Li4h64layG3W6HbreLQqGgpTuVSuH58+eCj1KsIuSZkFK6atgQNR6PUavVxH4hRwSAarQrlQqy2awaW8jeYWQtEokoPsdmGHJz2ObU7XbF5eGTc/J22E6zXq/lYqCbgAs3hR+Px6PmL4qCrGTfbreqVOZxY3X36ekpjo+PdazpHlutVuh2u3A6nXKVkAsTj8fl4GA7EZdWOmkI8OZ5wkiJ1WpFMplEuVxWexhdXOQeAbfuFPJRGPuYTqeK6vn9frx48QKRSATRaFQMKAoxjUZDLi4yUtbrtUQLugv6/b4WaQByXg2HQ7Uieb1eNSUxlrharRCJRMSTodi43+8VARsOhzquPO+n0yncbjc2mw263S4cDocibQQtcwl/9eqVBAeKqxTjLi8vEQwGBfteLBYC8pIlQ+4LQeZ0h9EFQj4RAEUxp9OpOEOZTEbuGEb6jo6OMB6PtdjzGDPOyeuK35tMsFgspsgUgd8U1nieMwK53W4F2w2Hw6obp8uOgOC7rVV0vTD+Zrfb0Wg0xLnhPTcWi4kJxRik2WzG9fU1stksjo6O0O/3Fc3j+32XE7Tb7e4dc4/Ho/OfwuB6vcZkMkEymZSLkRGrRCIBl8slUDgjTHR08R4bi8XQ6XTEFuL343lit9v1/gPQ3xt+v1/MJ8K5PR6PWs0oYpJf5vP50Gg05Dwj0Jo8KrrJyF+ikMm2wXg8LsHUGGOMMcaY79OYzNjd0cf3FgDm115HjDHmx36sBwVYYrGP9kX7PTb/3Xevuv9u4/xvfb/p72HMD29e+845mUxweHgIABJYxuMxDg4O1BqVSt12z89mM3S7XTx69EgASi64Pp9PER1+rtfrxdXVFQDIUn93EaAIU6lUxHQhj4YMFp/Ph3a7DbPZjMlkoigLhRe6icrlMna7HRKJBNbrNQqFAi4vLxWNCQaDcDqd2Gw2EjI6nY6WOfIj7sYTer0eDg8PMRwO7zUN+Xw+nJ2dCRwbi8VgNpvliuCSY7PZtOx3u10t29PpFBaLBcFgEIVCQW0yJpMJPp9PLh26b6bTKfb7PbxerxqrAMg1wPrkcDgsJg8X7ul0Ku7Q8fExms0mHjx4IAYPQdJsJ2q1WnJIUEwg8+f09FRxIMbtrFarFmk+yWfl83K5RLFYRK1WAwDBr3nsYrEYLBaLfhY6KXa7Ha6vr+H1euU6SCaTeqpPxgydRPP5XO6qTCaD1Wql1imn04nT01Mt0X6/H36/H4PBAJ1ORzBWRlToPLBYLDg4OEC73VbTEWG4rO7me7terzEYDCR6pFIpDIdDQYmHw6EWUEb4uNSenZ2pyY0wX0ZAer2e3C1cqgEoGsbmLwqBFBDoLmH7HEVONkYxusTzNxKJoFarIRAI4PLyUscdgJgiNptN0Z7YN/4SYmPcbDaTG4Vut4uLC5ydnSEQCIijxfp1cp2Ojo7URkZuFAWgy8tLANC9gGLter3WNeHz+cRhoqA3Go0k+sViMTQaDd2PIpGIqtk3mw1OT0/xxhtvYDAY3AMIb7dbNJtNxbn4ver1utxu8/lcFey8Zin4ENrL9j0ClMfjMUqlktxQrDhnTThFqlqthvV6jSdPnmA+n2MymUjoJQT54cOHaLfbEoUpKPMcp5ASiUTEiel0Oqr67vf7cLvdEj8potJZScGcHKB6vS6Hos1mQ7lchtPpxBtvvCHGFx00PO5sI+M9vdVq6f51FzDPFrHlcqn7x2Aw0D2IwiAFHJ4LBN3zHsTIIaORDocDx8fHePXqlQQ+nssUeI0xxhhjjPk+zX4Hy+Kb/2jaANhsP7aXY4wxPxJjtmD9P/wMWn/s8yj/jzKo/sETTP+Nn4XJ9vqu4Ow/6mKwcH3PL2GwcCH9D6vf89cb88Of1xZy0uk0vvSlL8HtdiObzaLRaMDhcNzjktAuP51OUSwWxY4gkPbm5gbT6RSZTEaigN/vV4Ux4xV0sZCHkkwmFd1hJIOWdzbMtFotvPXWW2ovIdC2Wq2qLYhCSyKR0Oex5YqwZtaFMxrAp/1comu1Gnw+H0KhkOqBB4PBPWfNarVCuVwW1+cuwNjr9eL6+hqBQECskLuuCy6Ek8lEjqXRaCTXhcfjgdvtxmAwUJyAghIZEYxWkVfCJ8t0DWw2Gy295Bj5/X4t6xS0KPSwcYeLbK/XE6dku90KPHp+fo5sNqt2HIoNy+USk8kEuVxObTsUbyqVitq+7Ha7FjJWJ1utVgwGA/T7faRSKbkb+v2+lthq9famQwcMcOtAOT8/RyQSQTKZRL/f19P77Xar95911fv9XospgbLNZlPnHGvaGVmhi2U8HqPb7SKfz+s9ms/ncj+R92O1WlX9zWW90WhIbKS7A4CgyWQHuVwuRKNRuVUWiwVsNhu8Xq+cM263W+cvRaT5fI7Dw0OcnZ3h4cOHODs703nVbDaRz+flsOO5abfbVRHO86TVasltw//u9XpRr9clTtARx1hTOBzWtc9mIgLBGcuZzWaIRqMoFouCAPN101lhtVrxD//hP8TP//zPw2q1IhqNolQqAbgV/CgebzYbuS/okGPMptfrIRaLqbZ8vV6LG0QRi1wtl8slWDgFMVZ4LxYLBAIBtNttALeCNp2Jq9VKx8NsNuPly5cIhUJwOBzo9/s4PDyUKJzNZiUURKNRXFxcIBQK4eLiQuIyRRMAur54blKspuPq61//OkKhkNxBPI/2+z0+/PBDsbrutkHd3NxIHKVgwngWm6HG4zGKxSKAWzfczc2NINiLxUIRQ8bdYrGYRBMKLLyGKVzfbSukQFosFrFerzGdTiXGU2BnK+J4PFazHa+h09NTbDYb3ePIWKNT827EjGIqnW13z3G66pxOJ/x+v4TrSqWCzWaDZDL5kf9iNcYYY4wx5jvMfo/ML58j/GKL8Istcv9kjO2r84/7VRljzMc6lpMDNH7WjmUE2HiAtR/oPbFg9Hs/DdM3zA/fbbbPXmH697/331mW/88ENlc33/PXG/PDn9cWcqbTKdLpNACIsUDGDUGrjAUEAgFUKhV4vV6JJqynphOGIOHFYqHWEPJAyISgI4CiBbkjm81GldZkttjtdpydneHw8FBPwlOpFAKBAM7OzlAoFNDr9RCJRLDf71Wje3Nzo4VxvV4L0ux0OrVQsjKXi8lgMMBoNEI2m5X4QQFhOBwiFAopolMsFhWfAaA2LrvdjkgkAovFAp/Ph1qtpugIwb3kS9yt6U0mk2rxoVDDKm7WQTudTj3RPz4+lhOE4lSr1RJPplgsKj5FMYHLFGGzFOwYh6ELY71ei20RDAbFy+DxZAsSQbWLxQJWqxWJRALNZhODwQChUAhut1viUbfbVc34crmE2+1GpVLB0dERqtWqYK08di9evFB06y7ottfrIZFIwG63699RKNtut6qF3mw2qrumMMiIDZk5drtdLJV+vy9hjvEiPvW/6xzgEsnYht1u1xJOrhDdZHR5dDodfR+CVtnORKHQbDaj1WopCsPzgwwnChjL5VIAX8KpfT6fomt8nXSiMY7CyNvh4aEEUzJD6MSjg4huN8ZgKCjSFXXXCcNoGp1cZCIBUISN5xO/B3k3jx8/xtnZGTqdDpLJpIRMusjYqGQymcTCIsuFrp67LpXFYqHoJ0VUNsE1m01st1s5VAgiJv/lLouLThIyX/x+P0qlErxer5hgZC9dXl7CYrHgrbfeUtSSQOpcLodutyuxh/yaQCCAq6srCToEtlOEvNvcRWfZYrGQQDOfzxGLxRQr4/VLtw3vgWwvozBEgZrRUjZ38Tqi66vb7SIcDiMUConjRaF4v9/rertbIU/xnADz7XaLRqOBcDis95PXA6NOvH4Yi5rP56hUKjg4OEA+n8d8Phcviq4ln8+H8XgsAYix3LvwfQq2kUgEk8kEZrNZkVs6M+kwNMYYY4wx5vs322YLnl95F55feRf7Lz/9uF+OMcb8yM7ggRlmr+e1Pz/1T1o4rcex2b1+XHGzM+P5TQqxf9b4Xl6iMR/jvPa73O/3YbfbMRgM0Ov1cHR0pKXPbrdrke71erDb7YoOxGIxuFwuhMNhXFxcqP1mu90iFAqh2+3eAxKvVivVGPP7UGQhDBW4jdjQMUNOBhtMGD1gRXAoFFLsZrVa4cWLFwiFQjg4OMBwONSf53a71STU7Xb1PbgMEmxL6C6f2LtcLuz3ey2KjUZDSxeAe1XDwK3jwuFwKD4yHo+xWCxwcHAAk8mEcrks5wojSnzK3mq14HQ6FU/icjafz9FsNhV7YvykVqspgkBejsvl0hN3siFcLpciMYFAQO1eqVRKi16j0cBbb72FdrstJxAFELfbLX7NdDpVs4zJZMLV1RWSySRGoxEikQhubm6QzWblyCIPhEsia6spUCUSCVxdXSGbzQpQzEXa5/PpzyVMGID4PIRIk2uyWCxQKpUkaiQSCS2uk8lEoiRZGXR3WCwWxWDozOB7v9vt9LMQFvz2229r6ad4Np1O0Wg0MJ/PEQgE5Lahq4lCBeNbw+FQy6Tb7RZnxGaz6Xw6ODj4FlGVDgVea41GAz6fT+II69NZS57NZjGZTCSmMIpHUO9dMHIikcB0OkUoFEK/30ez2dSxdbvdODo6wle+8hVxquikomBEsWmxWCCVSsHr9artiuJUOByWEEwByev1olqtKmZks9nEWOE/E6ZLPhAFV5vNhkAgoLgNXVlutxvRaFSCRr/fh9frldgzGo3EdAKAxWIhODk5PqxVH41GiEajEo36/T4SiYRq1aPRKBwOB05PT+VSeuedd3R+9/t9dLtduaPozLsbx+p0OgJ3F4tFtNttHB0dodfroVarIZ/P6/7Xbrd1PlPMISh+s9no9VCwJgeGgGPGohiBZd04RVCCtG02G66vr5FOp9XqFg6H0el09Hm8HikglstlRCIRAdWDwSBevXolAZhiE6NQBDVT+L/rtqnVavfqwdkyRyA6RflgMIgXL16oeYywbN7j+ToJ5mc7112R3hhjjDHGGGOMMeZHebavznH0R86x/Atp1MffnXdTHgSx+XMJPPyjL7E9v/ohvEJjvp/z2kIOf6HnE3Y2MS2XS3S7XVQqFXz2s5/FaDQSt2W1Wskxwl/yJ5OJ2A9XV1eKTLTbbXEParWaOCb7/R52ux3NZhPr9VpPeRldIWSX8RybzSaORKVSUT16v9+H3+9HNBpVBITNUKVSSSIMhQC2atFJYDabVSnu8XjU/kQHAKutWe9MIYCCFpevSqUibgyfXPPnYgSAjoPDw0NUKpV7AhCXXYo1dJSw0rrX6+Hhw4dabAlKpVtlu93KeURmTaPRkMPFbDar/cVut6NarcqNQtGDbA8KHXz6DUCtNGz3omPJZrMBgGC8bDCjKEWXh9vt1vemwBONRrHZbOT8yWQyaDQaeo8otvB483/ZnnQXouzxeJBIJHSe1Go1DIdDiVMU7axWq74nz2XClukauFspTdeD2+3GyckJGo0Glssler2eHGNcVDebzb2KaoooJycnOD09lQOB5/ndyme3261GNwK/3W634oeskr4b60okEmi327BYLAITz2Yz2O12xONxlMtlCUAE4TKutt/vEQqFxCPK5/NYLBbi6wQCAbnaKOIeHByg0+nA6/UiHo+j1WqhVCrh5OREDp7FYoFut4tOp4NAIKAFmnXjHo9Hzh1ysh4+fIjr62tBiBn1osMuEAig2Wzi4OBArqi7jjl+HkWBZrMpsaPX6+k8ZrU3XSHkqCSTSYlgvAbodHE6nYKEz+dzxeX8fj9arZYa7ehqBG6hxDzusVhMTXK9Xk9CLe+3s9lMorjJZEKtVlNkj9cu35OXL18iEong4cOHGI/Hqm8fDoew2+144403UK/X1QhG2PloNJJzb7VaKR7Ln9Xtdit25Ha7VUfO+yV5VPy7gk4rRpmazaYcm+SU3dzcqAEPuBWqKbB0u10BnsmVMpvNqNfryGazupdSmOZ7wbjUYrFAoVBQzJPvP1vm9vs9FouF4oWEe7tcLrHYeJ8yxhhjjDHGGGOM+WGP/2qH/fTWOLD//DtY/vbPYvnbP4vdb/mp7/g1u+kU9n/0ZQT+cz+u/j8H3/Hzbv7BAWJ/1QXLf/tV7L5hTjDmkzWvLeR0u130ej1sNhs4HA6k02k9yWUd8mAw0C/Jbrcb6/VavIJ0Oq2n4ow2ZLNZLWp3F6lYLKYGJPI+KKa02205Yvx+v1qSIpEIjo+P0Wg0FPOgwGK1WrXs1+t15PN5hEIhnJ6eaimhbZ9Pkf1+PyKRCHw+H168eAGfzwen04nBYIB4PC4+xH6/1wK33W7FMOFrZOyDDhibzSYXA2t7af9n65Tf70c8HlfEyWKxiJnS6XTw1ltv3XM93f2edAMAt4sZBQBWJtPlwCfvVqtVPze5L7VaTUsNuRPmbzQKnJ+fI5/PK0oymUxweXmJbDar78MoCt8DunDC4bBqg30+H8LhMBqNBjweDyKRCIbDoVg/dEGwmSYUCsnVQhDqaDTS62cDE/khdrsd8/kc/X7/nrthsVhICKKwwip5Mkz4tJ4uA0adKD6ynYy11xcXF4jH44oekVXE9iM2He33e6TTaZ3zBEmzRp3Q22QyiRcvXkgEyefzcvys12ucnJzg/Pxc9cl0Co3HYy3JdJNQmHK73WorI1uJjrpkMqm4Y7fbRTAYFK+EvCO+B9VqVUKpz+fTdUC21Gg0EnSXLWWFQkEQYDZn5XI5dDodcZMoyjCGSGcG7ysul0uCGMUpisJshGIsrdls6hxPpVISM3w+n7hD0WhUwGuKB/w5x+MxKpUK3nzzTVSrVYlP0+kUhUIB0+lUbql0Oo1arQav14t2u61rkgIljwWPGUHOdIWQaWW325FIJHB6eooHDx7cO4+9Xq9iX5FIBOFwGK1WSy4xusPIo9rv99hut2rNm8/n+pnZGkjxJBwOK2LI70Mha71e6x7C73OXeUX3JJvjGB+lUEuxhCI3Ra5arSYHEx2FqVRKPxOB37FYDJeXl7DZbPD7/WoYdDqdgigThnz3HHI6nYohVioViWonJ7f1tjc3N3A6nXIv2e12OeDuNrpNp1P0ej25M40xxhhjjDHGmB/cmGx2mD0effwkze6yhOgHG1jv6Cnu+h6hv/MBzMk4Wn/886j+Fg9an7ah9Wkbaj/vQuuPfx7WXPY7fk/bP34P+b/8Vez+qPfbfmT+ynuw/NOv/hB+OmN+UPORHDk2mw3JZBIWiwWlUgnj8VgRl1AohE6nI45LrVaTILPf7zGbzdBut5HNZtHtdhGNRgW+7Pf7mM/nCAaDEhDIslmv17i6upLYEwwGcXBwgM1mg2q1ikgkoqf3V1dXej3kOrDByeVyweFw6OmuxWKBw+EQu4IxJS7TbNhhAxLhzsBt/GAwGCAYDCIej2s5icViqNVqcjCwfphNMBRGGKshK4bOFrol6KSgI4XRCIopNzc3ehJNJ1I8Hteiy9dGdxGFGEa4GItxu91attgGQ15Fr9dDoVCQm4pcDPJiXC4XhsOhnE7r9VoRHLJ26Exi7ImCDWMbjDM5nU4BcMmtcDgcGAwGWC6XCIfDeq9ZAc7mIP5cZHiQdeHxePQ1dANdXl4inU5ju90ik8mgXC4LegvcMkUYSSIAm0yPuxXok8kEsVgMvV4Pg8EAsVhMIk48HketVhPfZj6faxlnlI3nAN9TcjtqtZrEhWg0qvf46upKcT9GbSiS8dyv1Wo6z5vNJmKxmGDCg8EAhUJBDUUAJALdrXMmwNtsNuvYkg1DgZDuBzJJKHaSZUKnxnA4RDabFZ+JDhm6oQjIpQuEHB+yd0wmk2rlKRDQ9REMBtFsNgFAIPX9fq/2MV4DZDtxKafjip9zfX0Nn8+n89Fms90TqCicZDIZtW11Oh25X8jQcjqd2G63yOVyAG6FjFAopHsCmTZ0rTE6tNlskE6nBYy/KySbzWbd77rdLg4ODjCZTHB1dSVhOxAISGj0+Xxq6AqHw+j3+2i324o30hXocDjQarXknLsbPRoOh3Ky7fd7XF5e4smTJxLG6SSz2Wy6x6XTaQnQdrtdMGnWqtN9WavVJMYdHByg1WqhWq1KKGQ0j+czIdt0ZdHpuN1uYbFYBGiuVCpIp9NwOByCkX/1q1+VaGaz2dTkZbVa0Wg0cHJygm63K7A1XVH8s/j3QbvdxnQ6FdDaGGOMMcYYY4z5wYzZ7cbqt7yFwe++/Rj+zrdgefIAlm+4hn/cZ79ewfX3fh3Z/+urb/wLIPRqgd1shsrvyWIZAvbWO59vAZYhYPZG6jf8vrvFAtsXZ9/2Y79e/QB/ImN+GPPaQg7jPBQu6JigzZ9Pjwnm5ZJhMplweHgo6Ce/B5/k88k7IySshWVcy+/3I5vNqiKc8Sy6SfgUnvXJtPLTTeD3+9FoNBCNRgWEZWtRNpuVs4OLKaNgfDLL1iTyZQhstVgsejJOYC8Xzvl8LneCy+VS9TMbtgh0ttlseirPSJnJZLoX1WHEh8eKbBc+RT86OsJms0G5XMZkMtGiy1ppLt8AEAwGUS6XMRgMkM/n1R6Ty+UkctDBkU6nMZ1O7zUPcVlst9uIRqPYbrf3xCe6s3w+373jabPZkMvl0Gq15LSii8rv96PX68Hr9UokGAwGYlywoni/38vZ43Q6Ua/XkcvlJHp0u12dd4FAQGLWcrlEp9PRuUD3BJd2Rl64SJP51Ov14HQ61cI0n88xn88RDocVrSFYmsstl2C+Drol2PBD5xf/7G63q2apeDyO6XSKL3/5y4rQ0aUVi8XkrmD8jucB+R1+vx+xWEznI1/3crlUS1EwGNS1NhgM4PP5FGuqVCqYzWYoFotqX+J5zOMOfDM6R1cPY3sE/prNZsW/EokEZrMZ3G63hKxOpwOHw4HpdIpoNIpQKITNZiO30GQykQPE5XKh0+nA5XLJ4USBiTEwCqbL5VKLPnks/PPIhOF5SzfNbrdTmxaFgHq9DrfbLXYT+Uaj0Qh2u138qGQyKVhzuVzGbDZDKpXC9fU1stnbpyMUwBKJBKxWK7xeL66urlRvzkiozWYTuJwwczLJfD6fYqLxeBy73U7uLQpxwC1kndHO7XaLdDoNq9WKer0OAEgmk2i32zq/KaoQKDwajdRGZjKZxPihI47CKuNxZIlRQKTYQpZXJBKRy41sG54LPBYWiwXL5VLCNUHyZOwwJkWHHJulKBrTudNutyVM0h06Go3g8Xiw3W4lmI5GIySTSTGV6KgcjUbw+/1wOp0YDof6O2+xWOh6MMYYY4z5sRiTCSab/bbS+Bu/ZxljzMc+ZguWX3iCedymf7U3A/1PhTH9+ROYv5Fu+EmY3XCM4t/povh3OjD/8/e/6+e337EZ1/JP8Ly2kONyuZDJZPRk9e5T4FwuB4/Hg16vJ54LbfxmsxmvXr3Sk1ECYdnqRK6NyWTSosLl6+TkBNVqVW4V/lJObkEoFJIV3+124+bmBo8ePRKL4W4Ei4sB3Q9HR0f44IMPEAwGBf1kFXcoFJJTgsyITCaD7Xarp9npdBrvvfeeXEd0l3Q6HSwWCxweHiIUCqFUKqFQKMBkMmn5mc1m8Hq9avnabrdYrVbI5XKCwVarVcVxCBZ2OBxqgyK/hTyOVCqlZZZPztfrteJl/PpkMgmXyyUXFF9zKpVSuxDrfvnesNp3MpnIkcLYXDweFwDV6XQiGo1iv99LbCNout1uKwLB84eLNAWh0WiEfD6PdruNly9fIpFIIJVKoVKpoFAooN1uo1gs6qk8HTWXl5cIBAKKEpHhRFGuUCjg/fffRywW07Jbq9X0JJ6OHUKE3W43YrEYLi4uBJJOJpP3AL9culutFo6OjtDtduH3+9FsNnF8fIyLiws8fvxYghArzOmSIujVbrcrMma1WvHGG2+Ii8QoI1t0Xr58qTgiXyuhvXRWARDjh0IhzyHyP1gbT+HR6XQKmsyI3Xg8VovRbDZDJpOB0+nEV77yFcUcp9OpoLUUPne7HUwmE548eYL33nsPqVQKTqcTDocDs9lMQiWb1wjpBSCQ7V0QOFuR6vU6drudBLtIJCIxpl6vw+PxSNRibIgRGQByXPAcXCwWEozZTEZhxefzIZPJYDaboVQqSSzhcXv06BFarZainj6fD6FQCDc3N4Kx1+t1sYro2prP53IP8Rylu4zMLr/fLyg4cOu+GwwGgjcXCgWxZwDoWqdI2O/34fP54PV69V5SxCInJxgMSswkE2i32+n+QvdZJBKREMy4IN+Hw8ND9Pt9TKdTJBIJeL1esa1WqxWurq5weHgo9xHbByeTCUajEY6PjyUmMkI7Go3wqU99ChcXF2LWABA8vdfrodfrCSqdz+dht9vx/PlzbDYb5PN5ueh4XHnuMd44m81wc3MjgZhw9Xq9LhF4t9vpPOTfLcYYY4wxn/QxWa0Y/KHP4dVfewev/to76Pzbn4P57Ucf98syxhjsfuFtzCPWb/vfVn4zYLN92//24zj79QrbZ6+wfX4KfOP3emOM+U7z2kIORQXGd4bDofgrbJ5ilXaz2ZQQwMWGT+UJDrVYLPrFnk0xdJRQAOj1enC5XGIs+Hw+wXe5fPAJs8lkUsQkEAggGo0KhkxeD38h5+fRseHxeARB9fv9ePbs2b06dACoVquCcEYiEVxcXEgIiEQiqNfrsNvtil8BULNKo9GA0+lU5Mzv9yu+Mx6PxaPodDpYr9dqryLThpW+wWAQ5+fnepLO9q/BYIB+v3/P4dBut8U+YayDx2yz2cDv94uzA9yKXdVqFfl8HrvdTtDcwWCglqdYLKaoyng8Ri6Xw/X1tRwM0+kUrVZLLilGv7g0D4dDLVQ8bwhCZUSr3W5jv99rSSPktF6v6/xjfC4SiaBcLsNqtcLj8SgCw6fvjFR0u11xRmw2myJKdKMwctfr9cQAIeSYTjTWobM1ioIWhRVyQZxOJ5bLpZxAuVwO2+1WQhlrvwHITTKZTODz+e5BXi0Wi4SDTCaDSqWCRCIBAGLbrNdrhMNhcWF4XqzXa50bw+EQ8Xhc7iJGbdg49PLlS8TjcTGiyHLyeDxqW4vFYoJlr1YrxSQZS6OQyeYyAAJo73Y7xRLJh4lGo3LPTCYTRZRYV87X2e/35TZjpJLAWzqp2Hbm9XpRLBYFr6bLi+Lw3euecTOr1ar4FcUBtusR/G2xWCSk9Xo9TCYTtFotHB4eSuBxOBxyjfG+QRGUNeQEQfN18H45n8/1vns8HiyXS6xWKwyHQx0Tu92OTqcjDtVwOBRbiUyqxWKheFI4HBafKBqN6s+iI4YuKjZWUTRjOxojrcPhEIFAAKVSCXa7Hfl8HjabTSwg3u8orKbTaTGUyM3hdUcRi6yg0Wh0D7RO5lqpVFJ0azabyfVGQdTn80mEDoVCGI1GcgDynk9GEF2T5XJZ4iMZabVaTbFAAppZy87mNkK2y+Xy6/41aYwxxhjzIzvD3/cZtP7VFUyOHUyOHfqfX+HlH/P/htBUY4z5YczOYgYMU4kxxnzkeW0hh0LIfr/HcrmE1WpFpVLBdDrF22+/jV6vhwcPHsgqT9YKYzKs9AWAZrMpN8JyucRwOEQkElFkgs0irMomDJQuBrou6AIh04atNfylnvwKm82Gfr+vqnJ+PZdONuVsNhsMh0NxIfik+O7TWXJPGOPiUkSGycOHDwX4bLfb9yqsCaXdbDZYLpcSuch4IVOGog0jYLlcDovFQo09x8fHapIhDJpL/vvvv49sNivexHg8Fren0+kgk8mItUOWjcvlwocffqj2sFAohGQyidPTU2SzWeRyOfT7fYlvLpdLTBGydQKBgFp+6LxYr9dyrJTLZYTDYYTDYXS7XdVncyGlKMamn7suBL6/FotFr5sOp1wuh6OjI1SrVVSrVVitVvT7fTx8+BBms1nnFh1abN0Zj8e4urrC22+/jXq9jvF4jIODAzmeKL5RgGu1WnA4HIhGo1gsFhIkfT6fGsTIMqIgwvPH4/Gg0WigWCyiVCrB7/ervWuz2WC1Wslds16vUSgUVJtNQYPOMS7xXGAJCPZ4PIjFYmg2m/r8aDQKl8ulJjmyqqLRqMDfhUJBzphgMCiRx2q1olQqIZfLYTabyYnn9/vlWFoul3j69Ck++9nPinMyn8/FB7JYLLi8vITValXci64mXhMUFBn3okDFemtG5Bino/OD7Bm73a5rknXvdwG+hIAzpsN7GeNK/Fl5fptMJkQiEdRqNTgcDmw2G4likUjknsuGIiNFarJ56vW6xFiKznSw8f2i2JNIJBTdJECbHDKTySTXkcvlwsuXLzEcDlEoFHQN2Ww2iXnT6VTnSDKZxGq1EoiaIh/Pfd4DfT4fHA6HWGKlUknndSKRQLValTDicDjkplmtVri5uYHFYpGTiYIhq84pGvK+v16vFbNi8x9bCKfTqThpbDRjiyFbufieUiRjTI1Mo3q9jmg0imQyqT8PuBXOxuOxYolkaJlMJommbF3j/ezg4ECvmw8sjDHGGGM+qWP2+dD5lOlbEhgm5xa9Rz85sRVjjPkkjnX2nZ05VqNs6id6PhIjx2az3Wuq4RPParUKm82GarWK7XaLx48fw2azoVarqWabQFmPxyMI6nK5hMPh0FJNDgzbTLgUM7YUj8cxm80ECmZFNpuxHA6HnD1nZ2d4/PgxJpMJbm5uEI1GxSUJBoMCIk8mk3uuDQB6Ku52uxGPx2W95zJBhwx/LlZk04WUSCTQarUUTaATiLEi1mw3Gg1FOlgnzJ8bgBYhLtR8Ys7ln86X2WwGh8OhiAlw67BhQ1Q4HBb7ot1u62k/4a39fh9erxfpdFoxNDZpLZdLcUP47wgzpUhRqVSw3W4RCATQ7/fV7MXF/fr6WpXrBFAHg0GxXvb7PS4uLuD3+9Hv97Hf75FIJDAcDjGbzWC1WhEIBPT5g8FAkbIPP/wQ+Xxe7wEAiYBctJfLpcQiLomRSETiQbFYxNe+9jU16NhsNjlY6Ii6KyayHSuZTKLb7Qr4yrp7LoqFQgGVSkWizna7RbFYhM/nw/n5ObLZrADNjIGMRiNst1tks1mMx2OJIRRl6Eh68OABGo2GIl10ZESjUcRiMUwmE1QqFQSDQbmzKJSRh0I+Ed9PMqwIDj45OUGn09HxIIeKVdnT6RSpVEqCJSM85Fet12scHR1hsVigVqthNpsp/ma329FoNOD3+3XM2eTE+0S/39exJTeFrhKKQQAUmWFrEUUQAqhZ551KpcT3YaV7s9mE3W5HJpOBx+NBvV7X62P73rNnz7DdbhXfIr/orqMqFovp2liv1/jwww9xdHSESqUioZXnWyKR0J9D9o/X60Umk8Hp6amYMKyMJ9ib3JnZbCbXksViQaVSgd1uFwicnBzGW8mkqlQqgmUTpE349V1OjcfjwWw2Q6vVEmtnPB6jWq2KjUXR1uVyodfrYbvd6ufgfWu73eLy8hJ+v1/f8y7kmu8fXxMfEjD2RRGdDB5Wr7tcLsWt2DTFn50PEij2NJtNnJycKKbIewjFOUYw+/0+PB4Put0u9vu9Ktt53zTGGGOM+SRP/3e/gW3CAJsa86M5jvoIpt3H/Sp+dCf+yx/CXf9WMcfZ3iP9y6+MCNZP8Ly2kANAv7BTqODT6Ww2e4+l8OLFCy2eZFOQa8O6bXIg4vG4mCmM85yfnwu2mUgkBAFmg9F0OkWxWMR0OkWj0dCTaIvFgmQyKU7HBx98gOPjYySTSTSbTUUlGCUIhUJaQN544w188MEH2O12iMViWi7Ozs4wGAyQy+UESuUCy9jScrnU57BFicsTI1t33UCtVgvj8VhuI0bB+v2+4g5kONAtRNDnXf4JFw22ArHNaLlc4vr6Wg4YVl2Hw2Esl0tFytgmxeOw3W613DIWwWgQHSN879n4QrGH7y3fh2q1eq8hiG4IOix2ux3K5TIcDocaZgi6pUhWr9dxfHys5Y1iAAAdgydPnuDp06fIZDLodDpqHatWqzo+z549U7NSJBKReMKo33K5xBe+8AW02209lefxZ5NSIBBQCxaX8cvLS3FvGI1yuVxagC8vLxU/slqtuLm50WKYy+XutXLx/9OhYDKZ5JyhI4l10ARJu1wuVCoVpFIpubam06mEK0ZJ+B4EAgE5fADci9FMJpN7TCYKZqlUCq9evYLP58Nms0EqlRIwmucP4zmJRAL7/V6CE4HBg8FAjhCv1yvuDBvq6Kyh2+jo6EjtTHRsUehhBIwRHwBiRe33e4TDYUwmE0ynU3GJ6Hwjq4Yi8mw2Qz6fR6vVQr/fVwtYvV4XI2cwGCCZTMp9x9jdixcvcHJyIgGO/Cjypw4PD3F5eYl4PC6xc7FYIJlMotFo6DpdLBYIBoNiwFCcZYMSXWFsV2I8ixEpNsoRYMxKe0aMyJoZDAYIBAK69huNxr1a+kQigV6vJ7GW9yGywmw2m659wqF57O9Gn/g+zGYzRZ54rfn9fgl86/UaTqcTrVZLEPHlcgm/3y/nTzqdRrPZlLjH43zXJck/Yzweo1gsSlD1+XyqF+c1lMvl5HoEgKurq3tieDqdlhhMJyBB58YYY4wxn+QxbY1dz5gf3dm+OEPwaf9bxZw94D+fYjf9ybad7MZjxP/uKxR/pY3kuyvEvra5/f9/9wLbTvfjfnnGfIzz2kIO23Z2u50ahDqdDgqFAl68eCGhodfrIZlMypbPZpHFYoHj42N0Oh0tPd1uF51OB/l8XrEdm82G9XqtWt5SqSTLPgG3NpsNw+EQiURC4gEdAmxiItPh5uZGUaLJZIJEIiEQZq/XQygUgslkQqVSwTvvvAOr1Yrr62uJIsViUYBVuhcoUCUSCUwmE6RSKVgsFjSbTfR6PcTjcbWp3G3cYdMPl2KyRfr9vpZURg+63S4ODw9hMpnQbDYFG6VrgwyK0WiEo6MjRdLS6TTK5TLsdjtSqZRAylarFeVyWXW/o9EI0+kUdrtdbor9fi+AMd+zs7MzOQ8I1M1ms2i324r/fP7zn8eHH36I5XKJXq+HVColdwu5MlyeDg8P0Wg0sNlskEgkMJ/P0Wq1MJlMtOQ5nU49ZR+NRuj1elgsFsjn87i4uMAbb7yhFq1SqaQIBaHGgUAAAFQFfnJyIqcCHSeRSESsIEaV6BZpNBo6XwHg61//ukDRFNem0ymOjo50PRCKTPdRIBCQ+EDWRzqdlqOLrBWfz4dut6vmMv4vWTp3xTA61tjYM5lMkE6nxUgaDodwuVwoFouC3nIxb7fbePDggcRCiiF0fDCSFYvFEIlE5P64uLiQ8ygSiaDb7Wqh9/l8yOVyakN69uwZksmk3Az9fh+z2QxPnjyRkFQqleS88fv9gv72+33BjS8vLyXU2mw22O12XFxcIJ1OYzabodvtwu124/Hjx4owMqZ5dXUFr9eLhw8fioHDNjxG9shfIQSXog1dbna7Xe4qCmvRaFQCLp1mPI+n06kEKwo73W4XxWJR7+Vms0EoFFLLmdPp1DW/Wq3QbDbh8/kkBDKqxMhnNBpFrVaD2+3Gdru9B2X3er24uLiQ4Mgo1nQ6lUiTzWYxn89xeXmpKCgB9cfHx4po0u1D9xSFaK/Xi81mI5GGsHo631KplOJ+q9UKnU5HwrXP54PFYkGn05FrixEqq9UqBwyv63a7jXw+j6dPn8pFB0BMKLZWMaZGNw0h5PxZea/huUZnj8ViQa1Ww8OHD7FcLhXFfPXqFcrlsgDpZEXtje3HGGOM+YSP/29+GcEvO77l3+/XZnhr24/hFRljzP3ZPnuF4Ne7cDfX+gi8HANf/hDYGefottvD9sUZbP/NV+D8B1++rRBvtj7ul2XMxzyvLeSQCcMn2fv9HvF4HPV6HcPhELlcDvV6XU+XKcAw6kTgLhkUrARnnWy73cajR4/QbrdVo8zlmFXL4/FYPA0uT4R28snqdrvVsk0uD1upGJGhCMIoBl0uvV5PLgUKHJVKBel0GpFIBLPZTAwYOnNo71+tVnqazKe5LpcL8/lcrgIKKovFQtBgupjYnuNyuVQJXC6X5fZg1KRQKMBisaBUKkkUms/nWuCAb7pVms2marKtVqt4HqPRSI6Qu04YLqKMmDGGBkCNNuPxWPGWXC6HcrmMWq0Gn88H4BYETbcVozxs2LFYLBLW6Ayg+8Zut+Pg4EBxB8a1xuMxMpkMWq0WNpuNYKeMhaRSKXi9Xrmnjo6O1OJDMWw4HIoH4na7kUqlUK1WFcepVqvw+XyCqZItdLfhjOcfo2tcMPv9vhwGfr8fyWRS0GpGTegEWy6XmE6nAiVzEd9sNnA4HKolJ9jZ7/fDZDLd4+hQUKjVanj06BEsFotqycnyoROEYuZwOMRiscBms9Gfvd/vUa1W71WxL5dLAaoZEVwsFmg0Gsjn8zq3yXGiWMDIC2vgea1ns1k12TFGycrsTqcDn88Hm80moZVwYzpC2O4UiUTkaMrn87BarWg0GqpmJxDX4/GoQt5kMqn+nXFGOrYY/WO1dr1eh81mQ6fTQTKZVDyO7pZQKCQgMwVJTjgclvul2WwiEokgm83CZrPh8vJSkaF8Po9yuYzBYCB+EGNGw+FQ9wev16vrnwwXih0ejwer1UruObJwyEQi7JgQabKhSqUSLBaLwOtvv/02+v2+znuem/v9XhBsikXL5fL2L4pv3Dvdbrcqwr1er+KhvO55j0skEnj58qXcjYQyt9ttnQculwuJRAKVSgUA9PcLfxZGKumGojOIoiyPEWOYbLQiC4vnFqOOwG0LWDAYRLFYxH6/F3eJXDOLxSLoNFllPAbGGGOMMZ/Y2W2R+C+/Av9X74s56X9shvPv//rH9KKMMeb+bF+cwfqrX9HH/mvPDCvZvzz7vXFMjNG8tpDDSMx4PMZisZArgNXEbOThUl2r1fD48WMtKIPB4J5QwsgP3QrhcFhuGsI/W60WCoWCGmbYfkTALJ8aD4dDcQzS6bSEIC4iXFAIsB2Px/cWU7qJ+CQ+kUjAZrOhVCrh4OAA4/FYy+9isUA8Hkc0GoXD4UCtVlPrCUUJLiJ3W6y4SFOESCQSMJlMaDQaCAaDarvp9Xpi+RwfH6NUKiEcDqNSqcjd0Ww2Ady6Wz788EOBjln1m06nMRqNsFwukUqlsFqt0O12kUqlcHV1Ja5JKpWSo4ACGheaxWIhjgydLawO9ng8cohQrLDb7eJsUGh6+PAhOp0Ottst0um0FiWfz4evfvWr4h8Vi0VYrVa5BRhTGo1GAshms1mJZdfX11pqyd7hMkqhkM02FNsI+aXbgCDV8XgshtHdFiJG/Xw+H5bLpdwJw+EQh4eH6HQ6OD8/FyyXx42NVnQ21et1WK1WJJNJnf+dTkdCS7VaRTweRzwex3q9FmeJ8FnGg6bTqdqjptMpQqEQms0mYrEYrq6uUCwWsdlsJHzy/4/HY3Fm6vW6WtycTqciNwRr83uTL7NcLnF4eHgvBkRhlq6sm5sbCXCBQEACCgA5NTqdjlw8mUwGl5eXiMViElDuVoWz0psMFYpDh4eHAtIyLlSr1TAajZDNZtVSxgazp0+fIhqNqvmOwt9gMIDD4UAgEFC7E3+udDqNer0u+C5ZVoyC2e129Pt9uY7oQMxms4o17fd7BAIB+Hw+OVccDgcuLi5wcnIiFwoACRKFQkH14HSk8d5019EH3PKfyDdaLBaKizFyRS4ZAEWpLBaLmDzZbFaOPafTqfvtbreTk4dRulwuh263i16vp9gYX1+v11NVO4/jXfYM6+GbzabuRbFYTILzZDKBzWbDbDbDo0ePUK/X5Thi89tdAHUymdS1wQgWnXAulwuDwQButxtWq1UCOa89NmvNZjO89dZbuLy8RKfTAQC5b/j6KW6bzWbs93u18BljjDHGfNJnv1wi9X96H4///ao+fH/3Kx/3yzLGGGM+hjH7fOj+4Z9D409+Xh+bX/xpwGz5uF+aMR9hXlvIGY1GmM/nyOVyWnwpYJDzwugVGSEXFxcAvukQcblcAKBfyqvVKgKBAFqtliCaq9VKoEp+Hpckl8uFVCqFXq8nxg2jKLT893o9Lep3XQBsxeIiz8ar7Xar5pteryeHC2GdXPJYcc56ZlZtHxwcyOLvdN6S/1mXzNfAZYpPh5vNpo4V/1wAirxcX18jEAjA6XTea/6iaEDRqN1uIx6Po1QqibXBmmyyariIMJbCKARZPRSkCA0l1Nfj8WC73cqdw8WeQpjf71f7DcHO/P5cZulqoRixXC6RTCax3W4RDAYV1ahWq0gkEhiNRnKn8Ik9AP05fr//HteEx5sAbEaAVquVgL8UcsLhsCCtrHUfj8dwOp2IRqNywVAgMZlMSKVSaDQaiMVignITXMyKZLoS4vG4YNImk0l8GAoPJpNJ/0t+TKvVQjweV+yOTV50jxF8HA6H4fF48P777+Px48daltl2RoEpkUjIUXJ6eqr42Hq9llBCgYjOOsK16ULrdrsCg8/nc33/3W6ndjKz2SyBkdXtoVAI7777Lh49eiRHls1mw9e//vV7omU0GsXh4aHcZnTPEJC82Wzgdrsl7lKkazQa4tMwOmcymSSiMaIF4F5sj64+NhFRkKIAyfsN2VY851lLHggEkEwmxUOy2WxqVFutVorJLZdLvPPOO9jv9/jSl74kdxcdHi6XC9fX19jtdliv14KQM9bGFrB+vy/Rbr1eS3ijG4hCm8lkQiwWE4S83W5jt9shmUxiv9/D5/Ph5cuXAlsTMD4ajZBKpRTn4/nGhjRGoux2u9oFs9ksbm5u1DTY7/f1v8vlUvcWcotisRhKpZIA2vv9Xt+bDkm61NgoyIiY3W5Hq9VCIpFAKBQSE+nm5kYQZUKnp9MprFYrqtWq/l6iq6zT6cgVyeY6xirX67X4QaFQCK9evUIikRDPiMwnXofkexljjDHGfNJnN5thU2/oY2/c34wx5iduLMEAOr/vTczSJqx90Ef95x3Y/Cuf+rhfnjEfYV5byOEv5GxGstvt6HQ6sq3ziXyv18NoNEIymVTr1N3acvJtaOFnXIRxlEwmowWj1+vJYRAIBLDf73F5eQmz2Yx4PC5GC+ME5MvEYjEBjtmKw1Yo1nmv12vFPHa7nXgubG26W/vt9XrRbrcVe6ATiC4MABJzGEsgk+YuY4FNWfwavm46d8j7IGT42bNniEajanmhs4ZRFr/fj+l0imw2qwjVfr9HrVYTp4WL1WKxuMfsYVSOwg6jCMFgUI6Qfr+PZrMpBwEdE2azWcvcfr9XbbXf75dD6cmTJ+LOkElB0G+/30ckElFcaTqdIhgMIhwOS9ij+Me65Wq1eo9HY7fbMR6PMZ/PVYfNn4MxFFYrVyoV+P1+lEolWK1W+Hw+MW4WiwXG47EYJBcXF0gkEorp0fFF98/x8bFYTBQGuQhTrOSySGg1HREUJDOZjGKCwG3rUrPZVNSHESEu+nSEeL1eOU8oTgGQS269Xt9zrLBxyuFwyP0xHo+RTCZ1fFerFVarFRwOBxwOB/b7PabTqeqfLRaLFtpAICDRghXYwWBQAgXrz7PZrJhXT548Qb/fx/X1NQ4ODtDtdlGr1RAOhxGJRLRQE2RLLg0bjkaj0b1oFAA5qXjNe71ehEIhtQ/N53NYLBbBa0ejEcrlMpxOpwDKACRuMUZElspgMEA+n5czsNfrYTKZYLVa4eDgAMAtr2U4HAIAXC6XHEKTyQTHx8eqp2dUjdcPRSP++QDkYiMbi64VCr3dblfnWDwex2AwQKvV0jFhnJWxst1uJ+eO2WyWwEHhkC18/N7RaBRutxvD4RCdTge5XA6xWAzPnz9XXTeFY95P2QI3Ho/x8uVLuTLJZ9putzoXYrGYOEZk5bChjudVJBJBvV5XVLXVaiGVSslRScFwsVgIbkxBhw1vFKEBIBaLSczx+/04OzsDAKxWK+RyOQniBHHzPQCg1xWLxdDv9+H3+z/K36nGGGOMMcYYY4wxP7qTimOa+faNnI3PObD7wqd+uK/HmO95XlvI4RNNtn7wqS3FiNFohP1+j+12K04Il9Jer6dfxH0+H6rVqtgedEZwEWSECLitPKdzg+wYNu4Q9EsoLp0gw+FQDgs2DAEQINXr9Uoc8vv9at7hk+pOp6NoEoHGjBYw3kW2CjkzjJuQCUOoLJd7Oo+4wDDqQ0cIIceMALBZaTAYiOFgsVgwGo0QDAYRjUaxWq3wwQcf6LWGw2GYzWZ0Oh1ks1mBct1ut1xKjx8/xm63k3OGxyWTySAajaJcLut9JIiVwgtjdT6fT0+6KcgwUkUuDXC7DLHqmVwQiin7/V58JD6Jp3NoPB4jEAjoWLKe3WQyYTweq7ac5w2XN8bebDabGq94PHO5nNgdPKesVitKpZIcOozkUIhhzbvL5cJoNILf74fX65WYwKr49XqtqN98Phe/hAIKYdWJRELHg04oRhMTiYRqsAlD5vehGDCdThGPx3F6enqvuWgymYgTwuuM3yMej2O/36NSqSAWi8Hr9aJer8vJQGAsobrT6RR+v1/A336/r+V/uVxKYGKUhcwZLup8zWygslqtislQgKDThmDn4XCI8XiMcrmMx48fa1lnNIw/JyN+k8lEVdLk0wC3Ysrl5SXC4bDuQzzXgVsXII+ry+VCo9FQ1O3JkyeKdBGuXK/X8eTJE7x48QIAJFScnp4qdsR7C8Vdvl88juFwGNVqVU4/OsEo0LApC4CEtEAgIP4PweaEE1NIm0wmqoaPx+M4OztDoVAQXNpqtUocoxOOzV6bzQaNRgOf+tSn0Gq1kEwmcXNzg1qthnQ6rWap4XCIWCwmwYzHxOv1wufz6Xo8OTlBt9tVe1alUoHH40GxWMRisZArjm4w/n+K4LvdTsJPOp3Gs2fPcHR0hFgshnK5DKvVqvgkHViTyQTJZBLX19eqgqeAncvlEA6HxVjrdrv6O4kRQbvdrgcF6XQagUAAzWZT4urh4SFGo5G4Wka0yhhjjDHGGGOM+bEYswXl3xn7jv95bwGWEeP3nk/KvLaQM5lMkMvl1EJUr9cF9STLxGazIRKJKLpBgYdunXA4jHa7rTYdLkLRaFR8FwJmt9st8vm8hAQyK8jJoajCp9hOpxO5XA4WiwWnp6eyzwO3T/D9fj98Ph/2+z36/b44Pc1mU3XlFBhYg37XPUMGTK/Xw6NHj7Db7fDy5UsUCgW5krgg9Pt9jMdjuU4ikYgWIn7MZjPE43G1CBWLRfz6r/86Dg8PYTab8erVK2SzWcViuLDcbTj61Kc+pSatcrmshh06kRhZCQaD8Pv9ePr0KZbLpSJa6/VaT+IpOhHCSiEomUzqyX8ymRTviE/EKU7MZjN0Oh0JBgTXzudzvPXWW5jNZorcWa1WNJtNLBYLuFwuiQbkHJFRM5vN9FQegOJolUoFdrtdUTICmLkokmEyHA5Vcb1cLuH1ehEIBHB9fY3VaoXRaISTkxOxe/h6KDY6HA5FiujmYsTL4XCg2+3C6/XC4/Hg+voaAFR13ev1BASn64SxILpvvF6vjh2FQpPJpEpoLuSMmtG9xveu0+lgPB7jnXfewWw2Q6PRQCAQuOfAIMw7Eolgu93KzdLr9bQkv3z5UswcRsDG4zESiQSi0ajayMi/icfjePr0KT73uc+h1WppCWdVM0W9zWaDTqeDTCYj1wNr7Mnm4TnE49LpdAQnZxtcrVZDt9sVFNvpdGK320lQYHwml8vBZDLB6XSiVCrJZUbIL69Bj8eDSCSCWCwmdwuFKoLD+f5QjCJPhowjApYZtVutVigUChJder2eRDA67gjQ3e/3aoIjkPf09BSZTEbiE4HiPIeGwyGazSZms5mESQASEjudjvhS0+lUx5CwZbbtzedz2O121Go1HB8fi4UVi8X03pVKJYlcgUBA7Bqv1yvQcb/fRzgcFguL7w8jTzzuPp9P8SfGJinsEQLPhwFk9TidTjGICN4m1HowGCCdTuuebLVa8fLlSwl9vBeQl0Te2cXFBbLZLADci49NJhPE43GYzWacn5/D5XIpTktBcDKZ/Gb+jjXGGGOMMcYYY4z5kZn9a2//xvyoz2u/lWzmYVSAbAXW9gYCATXaMKLDOmTWLtdqNTErPB6P4kk2m00MCPIfnE4nzs7OtIBbrVYt9LTs060znU71GljnzBgTAZaEnfKX9na7jVarpV/uE4kEut2ueD8UYhgpcLvdesrLZTsUCgmC3Gq19NSZLoBut6sIGUUBMi7oZioWi2i322g2m+Jb1Go15PN5xR7m87mEjlQqpRaiRqMhlwm/Frh1H1AUWS6X6HQ6YsiwcajVaglu7HA4xDwhB6XZbOo1VCoVeL1eReDYQnVyciKYLF1OAAQl9fv9SCQSuL6+hsvl0vd1Op0STNhGxWWVix75OIRWp9Np3NzcCAhLvslgMBB0lUst4yBs/bnLgWGkYjgcIp/Pi+1x13G1XC71fjPiVKvVxCWhKEGhbzgcIpvNSuyjm4guFOAWQlssFtHr9XB4eCj3B/kugUBAC2uj0dCCzxhJKpXSNUjRk+LMixcv4Ha79XOzBYnnKZ0+7XZbcSO2Rl1eXiKfz8vlEgqFFNNiPIqcFrpper0eHj9+jE6nI6Btt9vFW2+9dY/zROcU3Q53hT8KZBTg3G63mrvoXAOgGCYXe1Z5k5VFBwsjYXRcBYNBTKdTnYvFYhEulwtWqxXdblegci77dM2Ew2Hc3NwIWM1mqel0iuFwiHg8Dq/Xi16vh5OTE93/KNJMJhPF/Sj08L2gK5HxP7J/PB4PQqEQarUaYrGYvo6iD918kUgEAO7VgO/3e91/WPMOAJVKRcdrPB6jWCzi4uJCgPJHjx5hPp9LPOExpKh+t72OLphsNotut4vT01OJmbyvsXY8GAwqdrderzEYDLBerxW1pehGoZ332PF4rGgi3UsAxG2ioM9YLO/3/X5fkO1AIICrqys5iIrFIpbLJZ4/f67mu3g8LiGX1zZbsdLptJq/5vO5oqyMlhpjjDHGGGOMMcZ8ome3Re4f9n6D/w7Yxkbd+ydlXlvIYcNNpVKB1WoViJKRHrYi9ft91dRSNBmPx7BYLIrBMMJERget/1xQA4EAHA4HgsHgPUYNYyh0exBk63a7Jc5wGQqHw2LmEKLpdrtxdXUlJgrZOIz2UAjiksBf+Ali5iJarVZxdHQEAHIWhcNhuQ1msxmSySQikQhqtZqiRT6fTy4iPvXnMSFbh0/MKS6w4YVOJ8abWMXNf0+eh9frxdOnTwVBjUQiyGQy9xbyyWSin5nAXofDgfl8DpvNJhjrZDJBuVzG0dERzGYzXr58CZfLhVAoBIvFopYe1ilPp1OEw2EJABaLRQspm3S4HNKJxOpiAq7JwqGbgK+TixpbpubzOeLxuBZlHne+7lQqJbGAryWXy0lII0CWYg0Xu91uJ0GJTjKynMiYofuKHBxWu9NJsNvtkM/nMZ1OxZEKh8M6XoxxrddrPe1nFffV1RXefPNNrNdrtNttReDo4CCjiCBwvsftdlv8n9VqJcGh2WxK5LpbqU7nkNPpRLvdFgz8+vpa7ynPx+l0KmH0bmMQBd1IJILdbodut6vo44MHD/TPBAYzbud2u+Hz+STSlEolHQMKWhR0LBYLqtWq4pMENFPYsFgsYjfRqUQwLwUiOjnIM2Kkky1NdJ/5fD5cXV3BZrPh+PgYV1dXEgljsRhMJhMqlYpcbrweyayikEjI8nA4xMHBgdrDGJ/0+Xxy3jD6SGCv1WpVVJBxU7rhCOEloDkYDOr75PN5vPvuu4hGo8jn8wAgMYXOqJOTE0XHPvzwQ3i9XkHKGcWjEG+z2XBzc3Ov3Q24FfQPDg4wm83Q7/fhdrt1PtCJ0+121bhGMYkOwevrazx58kTtfBRs+H44nU6cn5/D4XAgGo1ivV4DABqNBhKJhETfdrsNp9OJUCiE6XSK6XSqa5Hgd17HsVgMw+EQfr9fYhwFVMYNGSlzuVxotVoAbhvCHj16ZMCOjTHGGGOMMeY3MWaPB5bYbZzHEgzAmkzc+zA5HDDZ7LAmE4DZArPbDUsi/jG/6h/fMdXacDW/fYV5/KtrWP+/RpvdJ2VeW8jhL9mRSERw406nIydKMBjE2dkZwuEw/H6/2m4qlYpqrNlQwvgCG2lWqxU8Ho+YLFyC+Ms9uQaMLJCNQZiv1WpFoVAQUJY1wnQ09Ho9VCoVLXc+n08xFAopZHOQ6UPhZrPZCMQaj8fFtHjx4gUKhYLaeHa7HaxWq/g1wO0iQFfSdrtFIpHAer1GKBRSxIyLGXDL2mBFOsGvFDu4rHJ5T6VSmM1mmE6nEl4sFouaZugkcLlcgvwy7kKuDTkdjM3wKfz19TUePHigxZitRWTG+Hw+uN1u3NzcyGXDJZUtPnxyPp1OJbbQRcD3iFBjVsBPp1Pk83k0m014PB7kcjlUKhX9+dvtVm4dxjzW6zUSiYTac+r1OgDg5uYGiUQCXq9X0TEyaWw2Gx48eICLiwvM53MUi0WMx2MtscAtA4ewU8ZQuHyyejkSiSAUCmE0Gun4cHm/26pFlxEdZzyXRqORXvt8PpfTqNvtIhKJIJ1Oo1wuixnUbDbVOsUGLkJ56VpbrVZy5jAeQ4cFRSROp9NRI5rL5cJqtUKlUkEul8PNzY0gwow80v3RbDYRCoUktFHEqdfrSKVScj+wdp2ONqvVCrPZLOcMYyzJZBJutxvtdhtXV1dIp9M6JwniJXMqEAgohkRXB1vAKGRut1v0ej21jN1lnLCumi4PABKmm80mDg8PdY2l02ndi/r9PgqFAqbTqVrv2Fw2n88lrLHVbbVa4cGDB2i1Wvfg5oz+NZtNbDYbvWbGgjqdDgKBAMrlMlKplCKEjUYDg8EAR0dHEqbW6zUWiwUASLD9F//iX0i4i8VicjZtNhvdL6fTKTKZDBwOB+r1OsrlspyEFF9isRiOjo5weXkptwy5ObxfkklGp5fJZFIUajqdCtB8l61ls9nQarUk7LCpjs1+jKzRrWez2XB4eIjdbqdjRscYHT0AxC0jf6lWq0n8pEus0+lgPp8jnU5LAL++vlazGO/ZFHszmYzhyDHGGGOMMcaYjzImE2C2wBIMwPLgCJYHRzAVs9gnIrAeFoF0ArtEGLt4SB/mQhbmYha7eAiWo8Lt56eit18fCcOazdx+X2O+L7PtdJH426/g6ALm1Tc/4l9dw/nffO3jfnnGfIT5SCk5NqGUy2UMh0McHR2p5YWVt4TAXl5eqgVmNBppCbXZbMjlchJx7jI72HxEp8Jut0Mmk8F+v4fJZEK5XBbMlKBdAnZvbm5gNpsRjUb1iz5/qV8sFgiHw/j617+OBw8eSFCgW6DdbqPRaCCfz6PRaCgyQCGIMRm2tjBqQHhpOByWQ6Pb7SKRSNzjORAEyif8FLvOzs7UgjOdTsXSIVA6l8uhVqtpWWP8ZLFY4NWrV/B4PBgMBigUChJ++DPf3NxgNBpJ8LLZbIKF8j0BIAdLMpkUnLRYLKLb7WI2myEWi+Hs7AzpdFoRquVyiVKphHw+j1qtpj+XrzMcDqslKBQKSciYTqdyctVqNS2/jBMlEglMJhNkMhlFQ+iGoRBQqVQkYrG9ijXQjN+YzWYJJHRX2e12VCoVsWouLi7UNlSv1++5nIBbHg+h0HQr0aXFJjG2gNFlUS6XkU6n0e12EQ6HcX5+LmcaAcRcWi8vLxUvpKARDAZV4czIIeNZ8/lcgmA0GhXo1efziYvidrsFaQUgJxvPZVZd02GQSCTkjGHbUT6fR7/fv9ckF41GcXFxoe93V/AikDeTycgZdXZ2pgrzarUqsYIC39XVlVgvdFGQixWPx9HpdBAKhVTBTgcURc7Dw0NsNhtcXFwonsbozWq1UnyJx2e9XiOXy6HRaMDlcsn1FQgEEIvF0O125XSq1+tyefBe4vV6sdls5Dby+/2Ix+PiKLGqfTKZ6NzbbreqtKdw5HK5YLFYUKlUkEgk5AKj867f76v1KZ/P4+zsTK6ag4MDtcIxXsh7UTQaxdOnTzGdTvHOO+/g+voaqVQKzWZT5xdb7eiC9Pv9ajBLJBKKebGJbT6f4+nTp8jn8wiFQrr+yMJhDIn3t6urK7muWBEeCATgdDphMpnksnG5XGqIIyybog1dRoSes0mMjql4PI7FYqF68lgshvfffx/xeFyuSULHrVYrIpEIKpWKrgWKyOv1Gv1+X612wK2YR0H+5OQE2+0W5XJZ8HRjjDHGGGOMMebbj8lmhyUUgiUUgrWYh/nth9gXM9i7HTBttjDNFsD5Nfb9IdDswDRdAOclmMpNmMpNoN3DvtHG7oOXQKcP03wJ83AK2Kww+bzYhX2wHhRgCYVg9nhgdrsNYec3OdtuD/G//kVk/sI3Pxz/4MvYG7/zfKLmI8GOF4sFHA6Hnmi+fPlSMRGyBRijSiaTgk7SrbJarfTLPhkRlUpFcRDCaqfTKQqFAsbjMSaTieDDx8fHmEwmehrLmlj+WRQaGB8YjUb3wLnb7RbdbhfZbFbOCLqLCLdkQxTjEXSAdLtdvXYCb7moRaNRdLtd1YHT+WC32/XEfjwey2FD1o/dbsd8Pkc0GhVXh04Nih2PHj2C0+mUY4M/O8WPz3zmM2ocqlQqcnuEw2G4XC65IYBbsQgA6vW6lqREIoF4PI4XL17A4/HA7XaLY8MYTrFYxG63w3q9xmg0QrvdVmsOo1a9Xg+hUEhOAbJ3+DSfLBKKbIvFQm4WLs2MR5Dn0+12MRgMEI/HtbgeHh7ivffew3q9Rj6fV1TNZrOJRzOfz9VIRoAxl3EKPplMRnBnVqCzbQqAILBsaep2u5hOp4jFYnKG9ft9LBYLiWN+v1+tQhTBCCUOhULodrsChhMqzCgio4CMC242G7x69QrhcFguL56rXCzJA6K402g05Jjjwku+i9VqRTablSPObrfr3GD0iy6Ku26R1WolF4nNZtO5TvbK9fU1crmc+DZ8v5vNpoQoCiSM/BDAy7gdIzpspRqPxxJIu92uYoeM283nc9Trdbz11luKHt6NSblcLmSzWV2fDocDZ2dnaiMi9NrlckkwHAwGSCQSmM/nYk2NRiO43W5F1xjto/vMZDIhEomg0+mopY8/H8dkMuk8Ywsb7z10NTEWudlsMBgM5DwKBoNymtC1xWuf7x2jixRa6WJpNpviTPH+CkDOGrr4KDLz3CFIneLpbDYT7JvuLIp+sVhMwmEkEpEYRxGVLWUUyVgtz/sYXXWMZ/HexCp1ivR3YeeMuM1mM1QqFYmsrEOnoOx2uyX8rdfre0wjk8mEdrutyC1juRTkWR3P840tisYYY4wxxhhjzDfHEgzAWsjBXMxiX0hhX0hhF/DA3BnCNFvCVG5gV2sAux3MqcTt5+SS2DsdMBUy+pp9IQVTPg1rIQek48Bud/vR6WPX7d+KPbvd7ecVszAVs7Bm0h/3j//Jn/3+/ocxn7h5bSEnmUyqlpbRiKOjI9VNc/nhchCJROBwOPQ09W797Ww2E/+Av/yfnJxgMploeWk0GoLKsjb27OwMfr8fmUxGy/rdWm+yI1jlzNhPKBRCMpkU46TdbiMYDIqdMJ1OVXMdj8fx1a9+VdyXTCYj99BisZDI4XA4BNtdr9cSW1h5nslksFqtcHNzg0gkIkEoHA5rMSMHZT6fI5/PYzQawefzoV6vK2bAY8rjy/iX0+lEOp3G2dkZFouFGmgWiwV6vZ7quQEoIkVuDBkpBOCuVistlZvNRgsUXU2r1UqtWOFwWM4MugkYXapWq4jFYlitVgiHw3rtXEZLpZIEN/JzDg8PMZvNUC6XkUwm1WR0c3MDq9Wq4z8cDrV0Mi5xfn6OYrEo8YnRNLo0uPyzjpzHPx6Po9VqiYfD1jI6m4LBoGrGt9utatzpSuj3+3JWUZi7uLiQyyQYDMJsNqPVaqmVq9vt4uTkBNVqVRwoOjl4/dwVa3a7HQqFAna7HdrtNgqFArxeL87Pz9WgxGhIOByWc2U0GqHT6aDZbCoKuFgs9LNmMhnY7XZFInu9npbj5XKJRCIBs/n2tkB33XK5lKNls9kgmUyi1WqJdfPixQsJcQAk4HQ6HTmQ+DqePn2qli3GiNhIx8hNKBTCzc2NWrTG47FcfJvNRuIwG/EA6Hoic4dtUYvFAkdHR3JzEXjcbDZ13rIJrNvt6nxbLBYYjUZ4+PAhXrx4gUwmg/F4LGGWoPHRaASn0yn3CcXuRCKBTqejay+RSGA4HEqE2+/3mM/n9wDod11IZJJR5KjX63qfCB6mkEjeFgBVrPN+wnue1WpFIBCA1WqV6Enx5m60jo6lQCAAv9+vVjur1YpOpyOwOt1+5GsxXhiPxwVwJliczJ3ZbIZgMIhXr17JxTcajSS2WiwWRCIR9Ho9gZFjsRg2m40A4mQHkUVEB2e325VD0e/369qiOJvP59HpdCRwPnny5Fsgy/v9XvdOxl4JwTbGGGOMMcYYY27HZLPDEgxgn09hF/IBZjOw2cHc6MI8mGDvdgI2KxAKwJRNYe90YG+3wTRfwXRdxf7lOXZnV9h/eIr9h6cwd0cwTWbYVuswzZe3n+90AEE/cJDBPpMA9nuYLitAtQFsd9hF/LBmMzB/A2NgjDE/ifPaQg6XOLZWEWJJJwNBr9vtFplMBrVaTREBslyy2Sym06niWFzgvF4vqtUqLBaLFkQKQXQxMLLEJ8rk7RAG2+129eSUyzsdOX6/H+12W2Dfer2OeDwOk8mExWKhSNdkMkGtVsOnP/1ptVGVSiVMJhPZ/d98802MRiMMh0PFEhjHKhQKODs7E5/EYrHg8ePHenq+Xq8xHA6xXC6xXC5l/0+n06qA9vl8mM1mYtyw7abT6cBkMiEej6Pb7SrmFY/HMZ1O0Wg0kEwmAdyKbpeXl3qK3uv1xP6hQ4CwYLqoWAm/2+3kDmFzUa/XUxSE7gVGVygYEZ5br9fh9XrFVBkOh5jP5wLP0u2UyWQUAep2uygWi6rTnkwmepJOFwCXs8FgoEpkOproePL5fCgUCgAgh4bX68VyuZS42G63xTfa7XZIpVKKmVitVp03ZCuxfprnLZdDOgo8Ho+iehR8AKjhbbPZKDJSLpflcqFjjO8Bm7KGw6FYKARKB4NBNJtNrNdrpNNpcYfY+jYej3VN0EnDFrD1ei1AMAVHLq6M1iyXSwFpWVnOYTSJDCD+nIT9+v1+mM1mPHv2DPF4XI1BzWZTYmQgEJD7IpFIyGVFpw4FMros2CTEYxyNRnF2diZBLJPJ3BPBLBYLLBaLwL4nJyd47733EAgEYDabBTrOZrNy/FCkWK/XaLVayOVyEjktFosiho1GA5PJRFBlRq3oKqIwwqjifr+XkyccDus9oLOL3BY267HRCYCq3CncHB4eYjqdotvtShgjC4vV9Lx38n6y3W4RCoUAQI649XotIcblcqnW3e/3o1qtCtK8Wq0wn88RDAYlAm///+19V3McV5rlyfLee4OCIUiJoqTumXmYh/n789Tj1ENJpAgQZVC+KsubLLsP2HMERGxscGNjV1L3PRGMZlMkkMi8eYHv3GOOR4XTHw4H3Nzc4PHxUQqfVqulfYjWMl4HiUeSUe12G6FQCK9fv8Z2u0Wr1YLH45Hyj6HxJLCAJxUhQ4lJ2lP5VSqV1P7GrJzT6YRmsymymrbDxWIhG2ahUND3m2w2i+PxiGaziXQ6LWKx0+mgXC6LNDQwMDAwMPh7hzudgqdYgOv6AlYkAtdwClffhjVfwtofcCqkcfZ6YC1WQLuH493D06+Pd7/+ms5wPhxe/Dq0HnFotJ5+/7muv3uqt+AaTmGdTjh7PThflWEFg7DmS7gGE5yDfuDN1dM1GULH4O8QX0zk7HY7yc0BKHOElpXz+YxKpYL5fI79fo9QKIRIJKLhgVYeWiGY3QBANd4M22UdbiqVgsfjQbvdRrlclsSep8zMsOBgyeYY1oTHYjEFv/Z6PdUw8+R7u93C5/Mhl8uh0Wjo81MBFAqFRA5RwcFBhUTGfD5XCOrnz59RKpXU4MTmFFq/xuOxqojP5zNCoRAsy8J0OoXP51PjSi6XE0E1Go00FNF2USwWEQ6H8fDwAAAi2DggMgSXQcy5XE7ZGLReJBIJOI6j03fLshCNRlWfzMDQeDyOdDotNQHJJNZ71+t1hMNhVS6/fftWmUjPG8MGg4HyjoLBoNQC0+lUChYSPuFwWNk4Dw8PL1rDOMRRicGBOpPJ6L7SeuFyuXB3d4dkMqmGH4a0TqdTBUnz4zYaDWVmZLNZETutVktV9y6XC263Wzk9tNxtNhtcXV3h559/1nWxNptqByqdmFlTKBRwf3+v+5rNZmXBoa2sWq3Ctm2pHoLBoOwtbFyjrYf/1u12o9Fo6DlTJcJA3VQqpYwpvku2bStTxO12Kz+Ez5VrP5vNSkFC1QLtlRyGT6eTVCvxeFzqKCqDotGo8mWurq5wOp3Q6XRwdXUFy7KkgmCOEK1mz0OXA4EAJpOJcoYA6J1OJBJYLpdwHAeZTAb9fl97E4mlzWYji9LxeMRkMlGu0fX1tWrYV6sVvv76a3S7XTSbTRGA/DjctwqFggK7uf9RSZJKpVCpVNBoNEReUxVDUoj7JVv3qCxxHEfZPwxrbzabUoo5joNwOKy9kNdM0o57EdUxjuMgmUxq/85kMvD7/VitVtjtdohGo8rSYu6Q4ziIx+Oq7wagvbhUKska9zxLhuqXf/iHf9CeQ2KHjXWVSkXP7b//+79xc3PzogmLbXPZbBahUAidTkeqM37fYfA983HY1kYSmeqlYDCITqejvSaRSGA4HGqviMVisG0bwWBQ36+azSai0Shs+39T02lgYGBgYPA3DlcgAPfrG5zLOZxySVjLNU6TKc7RMM6xCM7hIM6tDvBLHaeHJg7dHo7T2f/15z07Dg7dHk73DZzqLeDzI07pBE65JM6xCKyNA+uhjVM6Aet/zpQGBn9P+GIip1arqRI4EAgoOwEALi4uNFgxQ4N2Jw4GVE7wY1D5wVpyqiJo9yDRw8Yati5RXs/GpfV6LeXK+XxGOBxGuVzGaDQSYTQej/HmzRssl0vVjHPYXK/X2Gw2ePv2LZbLpU7vOYRRkVGtVhEOh9VYw7wKy7KkImK4LxVLzF9Zr9dYr9colUpIJBIih0ajkRqQisUifvzxR1SrVdmTSEjRKjSbPW2Kzy1nu91OzUUkD2j7YAgv7TgA8M0338C2bdi2jdPppLyP53Yxfo5YLIb1eq2Ka9pPaJHYbrcoFosimLxeL37++WedbHs8HmUmvXr1Cj/88INIHz43n88Hv9+v2un1eo1oNIr5fA7btqU4onWOSgE26wBQ5k6r1UKtVtO6cLlcIlNIFB0OB7WH0SLG9iLm99RqNVnBAODy8hJerxcul0sV2qy/ptqsVCopl6RYLEq1FY/H0Ww2lTFTrVax2+0wm81Qr9fxzTffKNx6MBjIcmLbNuLxOLrdrtRJJEWogvv8+TMqlYpChgOBgGx5tMBRgeU4zotQY2YQhcNhWVxoL3McRzZCkqEk3o7Ho0Kql8ulVE9s4BqPxzgej0ilUiI3BoMB6vU6gsEg4vG4KsxJQqzXa0QiEXz8+BGvXr2C2+3W0E6bHQnBeDyO9Xot5QxbyliDzvVP0olkJHOnGM5cq9WkPolGo5hMJmi327i6usJPP/0Ev9+Pw+GAbDaLZrMJn8+nFqr5fK52K2be9Ho95alks1nc39+LTB4MBmg0Gri4uNDazufzCk23LEvV3bQMhcNhfPjwAYVCQeuJOV/H41H5S7yX+/0ex+MRpVIJjuMoqJfNVfP5HJPJRDlktKsxpJmtUcPhUNZXBnzzXXG73fjxxx8RCASQyWRkm9tsNhiNRiLB+T7QxkrbLFsMC4UCcrmcFJWDwQDffvutlE+73Q6TyQTlchmHwwFerxfL5RK9Xk9B49y72KDFz9vr9dR65vF4pJYLBAJIJBI4Ho94fHxUa9ZgMEAmk1HQOgBZEJkJFjCnfAYGBgYGf4ewPB640ylY1xewdnu4FhtY2/2Tdeq2BrhdsJwdMFvitF7jtF7/PwnLPTsOzocDTosFTu8/wGoPAI8b53AQVjgENNo4/s9CCgODvyd8MZHDJhCSKfP5HDc3N5K+1+t1ANAP2iQyGo0GotEoUqmUTr+ZE0OVAe09rFkuFAoKqt3tdiJmOETTwuLxeGQRoS2C6hSqbFiZztPqw+Eg6w4HQ34un8+H2WwmUoa2oFAopPwI1tHyNHm73aqdqtFo6OTc4/Hg48ePypDo9Xr4+uuvZd+i+oIKHQZC1+t1eDweVRI7jiNLxc3NDTqdjp4FCR7ab0g2MMOI4c2JRAKTyUQWBQ4+bFdyHEen8jypPh6PIm9YiU3iitdLNQjbtkhWBQIBkXHFYlFV0MwsisfjuLq6UoA2CQMGWa/XaySTSdnomGHzPAMjGAzqxJ35Fm/evEGr1UIwGFT4ttfrxel0ksKJ6gda1Xjtq9VKOSy0KaVSKTVBzedzkYskMxk0zOp2qmRIAGazWXz+/Blv3ryRQonZH1RQrVYrzGYzRCKRF7X1oVBImTAkjLjW/X4/BoMBvvrqK7VvUdVSKpVE6rjdbtXMk4w7n884HA7KheL1UDFD+xaJUZJnz9cDW41IkLhcLllSdrudrFok+1jvzaYvflwqW5ivw/eM4dBsn2q1WiLASPywic3v9+Px8VGZLLS+XV9fYzAY4HA46H7ati2L3mw2w3a7FRHicrlEUNN+RgtbOp3Wx2VINwBV05dKJSSTSbWfRaNR9Pt9nE4nkZ0M2aaCcLlcKkeHuU98p0hMUkkVDocVsk5y6YcffkAsFkOtVsPDwwPO57Osh8zbWSwWUpKxfW02m8GyLPh8Pim4uFdst1sFaLPBKRaLKfSXZB4zu/i/JH24fthOxUBskv7pdFoKKQAiuiORCACg3+8r7JuWThI7brcbpVJJltD1ei1im8ozEmS0jvJd7PV6AKB9jxXxiUQCALT30p7GfZ/KR2Y/GRgYGBgY/D3BFQrhXC0A/RHO9hQAcA76YO32sBpdnB9aONSbOA6H/1+v6zga49zs4Nx4xDkRhSuT+v/6+Q0Mfi/4YiLHtm0NVFTFUAnD6mtmv3BwoCKBSoDnNbbAEwlCpQlPfDl05/N59Pt9hYVymKCV4ebmBuv1WvXXHBipvAiFQsoF4bUUi0XVILPhaL1eK7yVuTQAkEgkpIzgUEV1USwWk9Vjv9/j9vYWjUYD//iP/4hffvkF//Vf/4VAIICvv/5aRAQVKQzcHY1GyrrgPXj9+rUICloFWCe+WCyw2WyQz+d1DVQ+hUIhtWXFYjH4fD5ks1mpFTighMNhZVawJYj5MVQ5JJNJkXG0bpAEOhwOqNVqaLfbInOeD3UcpEejkTJ3qBDg8EySh0G6JD8eHh5QLpfx7t073N/fq86e1dbD4RDtdhvpdFoEFsnAYrGI5XKJdruthiPmH1UqFdVlP2/qqVarGA6HUld99913eP/+PdLpNDqdDjwejxpwttstCoUCHh8fRfBwzZHg4NdGZRIJplwuJ4UHSRIGMpfLZTW4MXS1Wq1iMplgv98rU2YwGLxoWbNtG1dXV3h4eFBIcSKRQDKZFPGSTCax3++xWq1EJAyHQ1lT+PyoAPF4PMjlcigWi/jLX/6ipjFaZkgMMB+Jw3csFlPw8PX1NTabDSzL0prk2m02m6rLLhQK8Pl8aLfbIlrY/DWbzVCr1WDbNnq9HqLRKCqVivKFGJo7Ho8RCARwPp/VhjUYDJTpQ+vier0WuVEsFkVEOI6D4XAosi0ajSr0eLvdSh0ym82QTCYRi8WUhbVYLGRRy+fzaDabaDQa+NOf/iS7GVuadrud7I7hcFjvCW1DmUwGy+UStm3LZsiWPjb39Xo9FItF9Hq9F0QV7zutZrPZDG63G7e3tyJQnqvzngdAk2xjQ1W320UsFlMI/WQyUY4Q955qtQqXy4V+v4/j8YharYZutys7KHPFzuez9mxmaJFgZw34er3GZDJRADszrqbTKaLRqEgrKoUYqt7tdrX++I67XC40m00Ui0Vst1u0221loF1eXqo63u/3y5LJrC6GIdNe1e/3cXt7CwBot9s6hDAwMDAwMPh7gTuThuvdV0AuDVffBk5nnK/KaqQ6NB9xnExw2m5/s2s8LRY4bbc4fXoAts5vdh0GBr8lvpjI4RDAFirWFLM5iqTHZrNBNBqV2sRxHESjUbW0jMdjheYyk8Xr9aJQKKi2+bmyhgPjZrNRIC7VI4fDQY0iHJomk4kyZeLxuEKVaT/hKX4ikdBAtVgsEAwGpUihDeVwOKjlhzXZs9kMlUpFrU081WbDVDQaxfX1tdQ1bNJar9eq6CWJQXvAZDKRXaXb7SpAt1gsYrFYaCClpYfBp7VaTRarRCKBdrutViv+PYbujsdjJJNJBXcyr4jWBCpYfD4fer2eVAGsp2Z7DMOXqULh82VuEEOC8/k81us1ut2uVD6ZTAbBYBBerxfD4VBtPh6PR7keABQ4nE6nldvC4Y2n7QypZqtTqfRUQ0jChoqnwWCge8fcF6ps1uu16r3r9ToKhQJs2xaJ4Xa70Ww2lb9D4oiD83A4RKVSwYcPH5DP56XA4H2bz+coFAoiDrjeotGoSDUqnmgvq9frsiSSzKPyhSHLJBHD4TAKhYLex0AgoPfjeRsZ29D4fKmuorqEuU/T6VT/ZrfbYblcKrychACJJK5RALKz8B2jsojvvM/nU2Au1RAMyLVtG/v9XsHVHM4ZLE1SwO/3q2Z8tVrB7/frGU+nUylrqKyjGqNarWK73Wpf4PokYUnbHglG/nvamPL5/Is9hba8VqsFt9sNl8uFTCaDWCyG1WqFn3/+GbZto1QqyQJZKBRELmUyGUwmEyQSCXS7XXg8HjUy0QbI9TAcDqWcur6+xna71XOn7YdEGpunPB4P7u7u4PV6cX19rSYmKvf8fr/yZZrNJoLBoMgSWtyojvL7/Wi1WloPfN9rtZpar0i6s6Kc7z3tS8yFovUwnU4jFouJgH6eIQVA6jESo+fzGY7jIBKJIJlMwu12K2CZOVrc854HtjObaDqdymK6Xq9F9M7nc2QyGfh8PoxGI+37rFTnfk67mYGBgYGBwd863LEYPMUCkIjBtVzDWm2eMnDK+adQ4+4Ih8f276qq+nw44NDt/daXYWDwm+CLiZzRaITr62u16jwPEabShqetu91OIZIAFEZK2w1za5j3QMsBMx3YEEXp/Waz0ZDNOmQOArS00FLynPRwHEdDGbM1qDTZ7XZYr9cakFwuF/L5PD59+oR0Oq2P73a7MZlMNGxQCcNATOZ88PSen5NDPptpeKL8PECUwbHJZBKr1QqbzUb3mNXsVDmRSGF2B78Gql6Y//P27Vvsdju1/PB0++LiQjacSqWiDJLBYKAWpXw+r0yR541QHPxisZhqyHmazwYp5nTQ/tLr9VCtVhXQSwUL7wdVKIfDAZlMRqQS691DoRAajQYqlcoLC0U8HhdZsN/vRQIwRHm9XqvBiBZAtgLxmTNkmsoZ/v1IJKK2JVZLF4tFZY6Uy+UXIbfMtuHAycyi5XIp9RazbUKhkBqTOHySJOSATVKD9egkMFwul0LCLctCuVyW9Yl/jwoF5ofkcjlkMhn89NNPIrdozWK4daFQwPv37+H1el+8NySrEokEIpEIHh4e4HK5pEzg3z2fz+h2u8jn8zifz+h0OshkMkgmkzifz3h8fEQul0MwGFQuEuvBWXNvWRbcbrfWBvOmSPKt12s1WnF/icViGrB3u50avur1Oo7Hoyq02fDEQF6+bwy7ZjYSCTgSVawS53oAntSDzNfxer0olUqqte90Onj79i222y1SqZRyng6HA9rtNjwej361Wi1cXl6qNpwBwx6PB8PhUITDbrcToZPJZPCv//qvCAaD+O6779BqtWSPYkA7w8cfHx/VGkWrU7lc1nMbjUZoNpt4+/atSJbnBC0tpABEgHMPX6/X+Oqrr9Dv9xVwT8KSYeYkxvx+v4jcXC6HzWYj1dD5fEYqlZK1j88kGAyiWq0qLJoWJ4bK813z+/04nU7KsuEeuVwuRfCEQiFZqmjHW61WqNVqsnOORiOkUikFK5O42m63L7KFnre4GRgYGBgY/M3B5Ybl8cCKx3DKJmAdjjh2+zhlE08tVM4Oh14fx/7gt75SAwODZ/hiIqdQKGA4HL6o6eXA2Gw2ZVPI5/MK1kylUqqr5mBHi8b19TXa7basQovFQrYPDnE8OSUJQ0UHB3KehgeDQXz48EG5Iy6XC6PRCJVKBV6vF81mU5khjuPI/sMhncHHp9MJgUAA4XAY19fX6Pf7qjDnMEIbFFt7vF4vfvnlF1SrVeUvdLtdOI6DXC6H8/n8oumKJ+xseOHQDED1uPz/p9NJp+Hn8xnL5RLFYlGKlXg8jnA4jPfv3yMQCKBcLuPx8VF13m63W6foVCx4vV7c398jkUjAtm3VdTOTwrIsfPXVV+h0OlIa8do3mw1KpZJyLsbjsWwgwWDwRQtPOp3WdVKFkclkZFegbYIEVyqV0vA6Ho/VNtPpdHB7e4vVaqVnRXKEuTHMD9ntdrKFtVot2VhcLhcWi4VyShjCy3wX1lh7PB68efNGTWkMv2Wt9nA4RCKRwHg8VtsVg4dnsxls25a9iBk/3W4Xm80Gq9UK2WxWqgLmpOx2OxEMJLuonqB6iyQRlT3j8Rj5fF5Eh9vtxsePH5FKpRRyfHd3B9u28ec//1nvK98xWsBIbrD2vVQqYTweA3iyFp7PZzV2cf3zmkh+fv/999hut/j5559FgDHbiNkyrJO+vLxUGDYVNplMBovFAtlsVnY0qq+GwyEGgwHS6fSLXBeSofx8JBxoz6S1jtXzVB79+OOPIthIDpBkY6iuy+USGUTSmnYvkgX7/R4XFxciRFKpFO7v70Vqp9NpBXtfXV1JRcVAYqqeDocDDocDFosFbm9v4TgOBoMBrq6uMBgMdL8ZyOzxeNBoNLSOmGPDhrlIJIJYLKYcrH6/j91uh+PxiEwmo9DmZDKpGneSOFS8/Od//qeanbj3kLyKxWLodDoK07YsC/1+X2rJ4/EoIpS2Sb/fL6JkMplgu93qnrA9ivsFa9Rp76IFzOVyYT6fo1QqybJ1f3+PYrEIy7IUHt/pdBRmzqY35loxx4xNamyjYiMa98BkMonRaITxeIxoNKpacwMDAwMDg79VeC6rsN69fqrzvm/h7HE/VYyvHZzef8Dx0+ff+hINDAz+F/hiIofqmm63i2KxKPXFcrlU+9Rms9FwzBPcZDKpiu18Pq/K21arpRyS9Xqt2mcOZxyAaVt4HpLKgM5YLAbgKXS0Uqng4eFBzVC9Xg/j8Rir1QqvXr1Cq9VCsViE1+uFZVnY7XZYrVaYz+d49eoVAGC5XCKZTKLT6agCmUNeu91WxgZ/4GdGCoc0ZoMEAgGcTicpBC4uLjAejxX4WSqV0Gw2RY6wXpuWLtoaOHTQPpFKpURQRKNRDW9utxuhUAjb7Raj0Qj5fB4ANPxMp1P0+33k83nlfFxcXODh4UFD9fF4lPXl7u5OmSG0I/H0erPZyIpGS4rL5cIPP/yAV69eyaK13++xXC5lG6Myh4HSrPt93tTDbBK2M9Eq0e/3UavVEI/HMRwO9QxDoRCGw6HIQp/Pp98zm4cWIrZHMcvjdDrh8vISd3d3yGQyGAwGsqnxZD+Xy2E4HOLu7g43NzcIBoN4eHhAIpGQ3e58PssyxzY0ZtOsVivl6bDum8M3bYPMMWKIcjabVV3z27dvZQ2jqmC73aJareo+U80VjUalIDmfz1KAUJnB+zCdTl9Y7th8xCwT2oFIRgUCAViWhfl8rn/Lj71erzEajQBAwbG5XA7z+VzV836/X5YtBu8mEgn0+30N+4lEQlYlEjp8z0qlkkhFqnkY7E1bEvcmACKIGIB9Op1wOByQy+WQSqX031hd3u/3kUwmcX9/L1KAFiafz4fb21uFQ/MaptMpfvnlF1xfXytcnEQx7zNr2Jn/QuImHo9rDyRZMRwOMRwOUavVlG11fX2twHASQVSisAmLz4zkFsk1WrhoW31exU4L2Pl8xnA4hOM4+js+nw/lchnz+RzAUxuc1+sF8KSGo9KQuUfMtLm+vsbj4yPq9TpqtZrCs71er9Y8c5i45oAn4prELdU2bJxigxStkI7jiAz2eDyIRqM4nU64u7uDZVlIJBJIp9OIRCK4v78XQUVSkBY/vrOsch8OhwgGg8ojo9KNaimqSw0MDAwMDP5WceoPYdXbODUecVqvgZENdAc4d40Cx8Dg94wvJnLm87mCh9liQzsMG2pIXlDpwvYUnubSQsWwXbfbjV6vp5NzniwHAgFst1u8e/cOi8UCnU5HuTAMk81ms2i326jX67i5ucF4PFY71Wg0ws3NjZQkjuMgFAqpojscDiMSiaDX6+H29hatVgu//PIL/vmf/xl+vx/1eh1ff/21aqK32y0SiYRCnbfbrU6pGTJL9RAtGrlcTvYRDmRUbpDc4ZBBlRAzd8bjMdxut+4jcx8A6HT5fD4jl8thOp1is9kgm82qSYn2qEgkgvfv32voKRaLAIB4PK72l3//938XKXM4HDAejxV62+l0pDo6Ho+o1+uqG3a5XNhut8o7qVQqCkk9n88Krr65ucGHDx9QrVbRaDQQCoWUkzOdTl+EvvIZcnilyiKTySijiDXTjUYDHo9HIcr82qbTKdLpNI7HI2zbRiaTwXq9Rr/fx8XFhXKYSOb80z/9E9rtNt6+fYv7+3tVMW82G/j9fmw2GwXdZjIZXFxc4O7uTkMzyZ9MJoNOp4O7uzvUajWs12uFKtPGs1qtlMviOA76/T7+9Kc/Yb1eo16vy1YHAKlUCu12G4VCQQqeQqGAbDaL6XSK7XaLh4cHeDwevH79GsfjEY1GQyqlcDisim+q5SKRCCKRCDqdDlKplBQSl5eXGAwGGI/H+P7779VEx/tJWwzwZDGiWiWXy6lZiRYxNqDxebKlrFqt4vHxERcXF8o9YQB0r9eDz+dDLpdDKBRSdXYul8P9/b0awO7v71/UVjNfiLac4XAIl8slCx7fk2AwiL/+9a+4vr4WicBMlGg0ikajgXK5rKB1Zv5wj5vNZlKLrddrVKtVBAIBHA4H9Pt9tUEFAgHte/l8XqTK+XzG/f291Fcej0dkaDweRyKRQLPZRDgcRrfbVXg2w8tJ8DG3idXatImSNCN5Mp1OMR6P8erVK3S7XRGj0+kU8/lc5C9zwmhXo/0IgBq8aNvivvX69Wu9I71eT+1qJPd6vZ5CuWOxmNRRbPoieUTLFivguf/QGsZ6da/Xq3D0dDoN27bR7/f1LMLhsD4HiRvgyU7FvYH2sslkImXc83BkKjapDry6upItjS1xBgYGBgYGf6s4rVY4Tmc4Ow5wPuM4neE4neFkWhsNDH7X+D9qraLi4Xg8KpB1Op0im82i1+spI6Lb7SKbzcLlcuHz58/w+/1qBqlWqwrTdBxHJ+8A1A7Eph1WSTOvhAQQm2CYhTAcDlVJzcrxQCAgwoEVtqVSSVYBnr5PJhNMJhN8++23+Omnn+B2u3F5ealsCw7frC5mDksqlcJ8PleGA4eBQCCgoYiVxLSLeDweHA4H7HY7qWAYYEtlAltv+OfT6VREwWAw0LBIG1axWFQmSCKRwGg00lDH3ItCoYDpdIrPnz+LxLEsS806zLmxLAvX19f4+PEjZrMZqtWqQlHz+TwikQgeHx+VSePxeHB5eYnpdIrRaCTSI5VKSUXBU3mqj6gsmU6nqFQqWK1WyvdgkxFDa1mZvVgsZKc6Ho+YTqeqL+cw7zgO9vu9FBCZTEYWj81mg1QqhfF4jHA4DMdxtAbb7bYCXtPptNp6qJ6gYsXn88G2bQCQ9YZDOof9169fIxqN4vPnz4jFYojFYgp7vry8xP39vfI3aBthAGw6ndbvgV/VECRQWa9Ma16hUIBlWZjNZhiNRqhWq/q62XLFAONAICBLyul0UrU2yUlmXAFAq9WCz+fDmzdvMBwO4ff7tVY3mw32+z0Gg4GakHi9bBJj01yhUEA4HEY2m8VkMkG325Wqo9lsilAKhUKoVqsIh8OyBV5cXGAymajKm3ZGksbP24c4cE8mE1xeXmKz2ciOw+YjqgHZQEUrFomRw+Egex+VZ1RlOY6j4OJeryd1BqvVAci+l0qllMvCfYdqoWQyiUgkglarJQsb7xUJYX7NbDX7/vvvsdlsVHNOJRrbu0hSk9ikjalSqWA6napKfbVaiVze7XayFJFAprptu91iNpuhVCopzJoNbmwi5DvHLKx4PK5g9Vgsht1uh36/j2g0qnVIG+R8Pkcul0O9Xtf3B4ZIk2yfz+eYzWZaW1Q1RSIRkV38XsI2KypDuZ/zexNDimmPJTFMiyTvdyKRQCKR0P74/BmS3DYwMDAwMDAwMDD4PeGLiRzaXILBoNQtDLtkTTAzZnhKS4k9AzQjkYhOdvnnVOJwUM3lcjoZZggtAGWojMdjZLNZAE/KElpmGo0GSqUSotGoTpPZfEWZfbfbFRESj8eRzWZ18n04HF7k+bBGvVwuYzAYIBwOqx6Y1crJZBKO4+h0vtvtIhKJyE5EiwoAhfrS0sAMGf63ZDKJyWSi03eeDvPUfTKZyPZhWZbyOJg1sd/vMRwO8e2336pCud/vYzqdIhwO41/+5V/QaDReNCs5joOrqytVATMYmc+JRMPl5aWUUByEC4UCms2mckSe/znVSgwD5v2kKouDFtfMeDxGo9HAxcWF7A+sUWf4sc/nw2AwUMsOALUPMUODxMdisVD472AwENlFEoAZH7RpTSYTABDxxCGd1i8OgFR7MUOECotGoyGSk1XnvAa2KpHEYEgzQ2FJ8IRCISQSCXz69EnhyRxmd7udlBrMnfrw4YMCd9mwFgqF4PF4FHI9Ho/VBkfr2vF41HMOh8NIpVL48ccflTfV7/dFcAFAuVyWFSqdTuvaOIRPJhPd+4uLC71rtO19/PhRz+95i5XH41FOVjgcxnK5RD6flyKG9zKRSMDv92M8Hmv/4fOgTYh7k9/vRzgclhWK+VAkM2nZ2u12L67D6/WiVqvhw4cPcLlculbgqdJ8u90qI8ftdmO1Wkn5ATwRBe/evUOr1cJ+v0ehUMDpdML9/T2SyaQUTVR6UY3ISnLmf9XrdakXqRijKqhSqahRi4o9Ni9tNhsUi0W02234fD40Gg1VkNPWycD2VColOxjJMZLl3DMty8J4PJaFMJ1Oa/8j8UMihdYjvmts7aPlcTgcqokrlUohGo3KUsf/DvwarOzz+ZDJZDAajVSVzhwgEsSFQgHr9Vp7yng8ljLM6/UqiPn59yqGGZO0AqA9k+Hu/L4UjUZF5nJPMTAwMDAwMDAwMPg94YuJHOa4TCYT5aJQpk9lDcNC+UPz8x/YASjskwNlIBBAtVrFYDDAaDSSwocnsdPpFACUP9Fut5HNZqXssW0bi8UC1WoV6XRaTUzMZ4nFYsqs4Gn26XRSmChPYK+urrDZbDTE0uZEtQi/XpIMgUBAuQ+0MIVCIWSzWYzHY6RSKQwGAwUeL5dL5TRwMGPmCPNswuEwAEjhRAURq5VZx81B5fnpMk+tOaTx/rN+ebPZyMLGoFcqC87nM9rtNm5vb9UUwyBp1phTFXJxcYHRaKTMo+vrawUwPyeT2MSz2Ww0rDKT5Xg8au1sNhvl5rCKnsQFA6tJqrDpLJ1Oy36WyWREhnFYdhwHiURCKgTa0khEkfTg/aGtgzlNLpcL9XpdhAiHb2aJhMNhWfbYnMTmnNFohP1+r1rjdrutr+/u7k5rhe9MNBpFt9tVoxXVFK1WS81YzJQhuULFAYPBqVKjne1wOMgCaVkWms2mVFNcx7xPJLCYe/P4+Ch712KxkK2FSrNgMIi7uzuk02m8ffsWnz9/Vq06c7MOhwNs29b7SHUHvz4Oy1RKnc9nhRdTZeLxeJDJZPD4+AjHcQA8Nd4Fg0EFdzPTico+kqEkfp83R5EQZjMY27cYiEvl3cXFBbbbrSyVg8EAFxcXSKfT+PDhA2KxmOw93AfP57Mq6NPptGxntIYxK4lECAO8mQ/28PCAZDKJ9Xqt95uZNlQUkrhkRfpz6x+Jr1gspj2a7yyD5Km+o7KELXQMvh4MBlK/5fN5TKdTpFIpNBoN7Zmz2Uzh6yTCqbAjmR+JRBAMBtHr9ZBIJERYUtVFIoWV38PhEPl8Xiof2nDZCsiMH34/eHx8hMvlQigU0nobjUYolUpq/crlcnC73VIo8Vlzj6Qtz+VyiaAajUYiiEiMk+w6Ho8KUzYwMDAwMDAwMDD4veCLiZzHx0cFT7Ltxefzwev1KpyVRA4HVeZK0DKRzWbVEsPgVTYNMXtmu92qUtfr9SojJhaLSbUwHA6V4VGpVNBut2U5YP4IrR4AlPPy+fNntf9waFyv17IEkeRgK0+tVtMpM0/5WT+dy+UU3Gvbtk7Waf9wuVw6wacCIBAIwOPxSNHD/ItgMCjSiC0utBOR+Hrz5g2Wy6VUI/F4HPf39zqR58DE2nOeNLPa2uPxvLgGVlJzsOLnB4BisQjbtqWMiUajGtJ5/bQMUc0Ti8VwcXGBwWCA+XwOx3GksBiPx3C5XHh8fNSfsco5HA5r+Lu4uMCnT590b7kmAMCyLJEJrVYLx+MRhUIBfr8fHz58wO3trbJgaGehEmS5XIo8TKVSUghxoIxGo1Junc9nBAIBpFIpWduYg8MskEgkovVHIgGA7Fi2beNwOEidxMGRJBxbpBhSy0F9Pp+/yFyh3Q6AMkgsy8JoNJJSi9YoKhS8Xq9sf8+bhRji+jyMmNdNMiWXy+Hx8RGlUknkqcfjUUsXCYl+vw/btvH69Wv0ej1ks1ksFgsF6lJpFwqFsFgsZDccjUZqaiIBQMKUZB/JvV6vp9wj3lsq/RhgvN1uFbyby+Xw6dMn2c6oaKNah7Yzql241geDgQgQKrBI/FIBE4lEZCfabDZqM6J1bTqdIpPJYDab6Z6RYCXBSzsQCWoGrtO+lUwmEY1GRR5w3+BeSkIyl8uh3+/jeDwin8+j0+mgVqupJnu73WqvZE043+HZbIbZbIZgMCjrYrlc1npmBthiscByucTl5aVUcVSIMfyY6+l0OmE4HCKTySCRSGC322E6ncLlcskSxb2QtijmaPH5MUSeZHo2m5V1LhgMwrZt9Ho9VCoVEa60o7KFazqdivyhze10OinnC4DeJ94rEoFsPrRtG6FQSNdIcp1ku4GBgYGBgYGBgcHvBV9M5FxdXSEcDqsZitYFZshQpcHTSwYC0yrFU9VGoyGbyfX1NVarlYY9Vr7GYjHl2ABPP4DHYjG4XC6FIR+PR1WfP2/Hoa2KeTO0VxwOB2SzWdk7OATwB3gGBvM0NhAIoNFoIJfL6ZScqpjhcKiBDYBsC+FwWF+nbdtqRGE+BhtfgF+JiVAohPF4rI/tcrl0Eh6NRmUL6na7WK1WUqdQHXJ3d4dsNovr62v8x3/8B5LJpFQyDEudTCY6hafFhvkStCtR7XA4HNBqtXBzc/Ni8BoMBlJTsDHouZWOGUk8VedgS6uZ2+1GoVB4UYkcCAQUSsu2IhI3zwmw3W6ncOjpdKosjmAwiPl8rsBaVmmzsYdqEVYMc20wIJVkH0Ny4/E4LMvCfr+XOom5TbSz8JoymQxOpxMeHh7gcrmUo8JcFCoXRqMR0um0GslIiFDRwCaicrmsHJRMJqPK+n6/ryGezUcM0/3pp59QKBQAPDWuUSHCNd1oNPDNN9/gfD6r+nuxWOh+MhtnMHhqJdhsNlonfJeZL0LSAgAKhYKIFKozFosFisWivt7RaIRYLIZ4PK4GNpJPDN4lGTEYDLTuvv76aymMqNghkUKlWTqdRrfbVVMTW96q1Srq9bratU6nk9RNgUBAIbl8byuVCk6nk+rXQ6GQLFOXl5eIRCJoNBpSPlFdRyKBXyNzm8rlMgDIzkWi1+v1yvpJ4tSyLFWlr9drLBYLBWKTmOGzZqYWSbV0Og2fz4dWq4VyuYzRaKSQYhLsfA60BJJU4fWRLOI7R4K1Xq8ru4rvezweF7l9Pp9FpnEPZYsWQ/DfvHmDbreLXC4nWyiJ2/V6jXw+LyXbdrvFZDJRW2A+n8fpdIJlWSL0aXN6eHjAu3fvlNNEZRcJslgspjBwy7L08aiuW6/X+t5Ay+t+v8f5fEa1WlWuF/egzWaDdDqtdW9gYGBgYGBgYGDwe8EXEzms2A4Gg7Ka0C7EcN18Pq/KbQBqKGJYMU/pB4OBMlZIrlABMJlM1FLCUOV4PI5Pnz4hlUrhcDjgeDyq/YeBx9PpVLkrDCv98ccf9YM4P85oNHqRlUB1kWVZ+uHesiwsFgtZLniSy1NutsqwApqEUTAYVOsKPyYHNg7xDNBkJTFzTVwulzJ8eKK8WCyU2TOZTNSGxJpjt9utYZtZFsRgMEC1WtWQyMYpn8+HVCqlU32esLOBjEqSRqOhRi5moFDVworn5XKpCma24zC4mjk7VDUwb4J2Ig5s7XZb17Db7ZBKpVQPTeUGyZxisaiMmdPphPP5rCH7cDhoqAR+JTao8tput6qTH41GUk2xqpnDLYfI6XSKUCgk1cvz9h3mf0QiEdUrD4dDRCIREQFU8eTzeZEhtPbwhJ9qhucV3Vzjbrcb0WhU7xLVUAzX/vjxI7799lscj0eFNPOdbLVauLy81MAbj8fRarVQKBRwPp+ldloul6quJykCPNn7isUiBoOBCASu6WQyicFgoOrsi4sLdDodEVgM0GY1OUk3Zo3wvu33e9Wpx2IxJJNJKVlItJEQpRLquZqKyjW3261nx8aui4sLtNttzOdzXFxciNBjRhQzVqigsywLwWAQgUAA3W4Xf/7zn9HtdqVg435EUmkymWC/36tJihYtWi232632RebJkLhbLBYAoHBy2gOpyovH4+h2uygWiy/slySLQ6HQi/tM0oi2MmbLRKNR2LatFj3uK89JuVKphMFggHQ6LZXW9fW19ktmzxwOB9knmdE1nU6lPqQVksozKol8Pt8LKxPJer/fLzUfSXtm7XDfZ+6P4zgiz6jkXCwWaklMpVJqfOt0OrL1OY6DbDarLCSqnmhTpS0tkUhgPB6j2WyqeY9h616vV02JBgYGBgYGBgYGBr8nfDGRQyVHsVhUhSwrcDncUzJPOT8JlmAwCACyBDH/grYlVl3TRgL8Ogjv93sNgmyUYbiwbds6Ic5mswql5EBKlQSbR6gOYAsLT5WptGDgJYcJKnR46s9wZ57QcqD96quvADwFdgYCAXz+/FlZKAweZjvL8XhUsGu32xVhQJUSLQy73Q4ANCjRKgBA1c/j8Rgej0cn4vyYtDEwINflciEWi6FWq0lNxLweqlRokeHJfTKZRL1eV6hqOBzWyT0VVLTRvX//Hq9evdLXxrwb2pqy2Sxs21Z4c7lcxk8//YRUKiVbEHM8XC6XlDa73Q7xeByxWAyNRkODJVU/JDcCgQCy2Szq9TrS6fSLU/83b96o1abX64loInHAwdmyLA2zkUgEi8UCV1dXIjD5tbMenNYo5hARtDTRouL3+9HpdFCtVjUc0jJHAoFWpEqlAtu2sVwuRRQcj0c4jqP6ZD6PXC4Hv9+vkG0SglSlAZA6gcoCtjSRiKBNikQO7YEkW6geo0WHliZmD/Fd7Ha7ACD1y2azwXfffQfHcaTMq1arytyhhZFWqd1uJyKOCo5wOCwLHkkfhjFz2KZqh5Y4tp+RIKzVarqPPp9PNqF+vy/F2na7VRBxp9NBJBKRBSyTyWgddDodqadIBC8WC4Wrk3Sl3ZTri5k8fKb8Wmm3IhmyXC5lM2Xd+dXVFWazmYhDkqEkcGgzZWgz1VRutxutVgv5fB53d3faFyqVitYbnxcJRoaQ8znz+VKlNBgMFGhORdNzCx/wlKMGAHd3d7i6utK65PtEoobvGO1R3Fui0ahUgfF4XHsVv27WgpOwYfPU1dUVVqsVxuOxVD68R4fDQUQ3VZyLxULh6VyD3BeY7ZRMJpFIJET+GxgYGBgYGBgYGPye8MVEDhuePB6PlA2bzUaEAMkJtsYAT+RPu91GPB5XlgUHt1gspuGJqgKeVtOaQptIOByGbduyCTiOo+wDDnz9fl/hysy2oZqmXC7jeDxK4TEcDjU4ZLNZOI6jk/7JZAKXy4VCoaAf9oEn+1Qmk5G6iAofr9eLu7s75WRQOTOdTtUEQ2UGsyNisRiGw6HIIhIc8/lc7VVUCzE7g3kt6XRa+ThUGyUSCZ2+k8hgls1kMtHgTSKMCot0Oq12mvP5LIUGMzGYnVEqlZT3wQBSgrW/VItwIGJQMfMrWGe8XC4xmUzw7t07hbLyVNxxHFnOKpUKXC4XbNtWnkkoFFKtvNfrRa/XQygUUmApCZdoNKpTdwYvk8Rgc9R6vVaFPfORqMpYLBYaJLlWGZhLC9pzCweJh/P5jGg0KnUMG4tisZhsPVxnbPOKx+Nq7OGQz4Yiqrji8bgUVQxP5oBJNQztTalUSrlFDF3muiYxk0qlpBhhKDNtKm63W9lRfr9fOVMcjgGoOWw8HqPX6+Hm5gbb7VbPe7vdKnPpdDrpPR+Px3j9+jVs24bP55O9ijkqo9EItm3j+++/h23baoQjAfa8sW44HEphkcvlZMM5HA4YDoeybXKdsOGoWq0iGAzKCsSvj0QG1USxWEwkUa/XQ7VaVSgv31Va6QCo3pvkFQnON2/eSIWyXC4RDAZRLBYRi8Xw888/i1QiycC/y3VYKpXU1sXr4R65Wq1e1L8z0JdNYH6/H2/fvpVNrt/vYzwew7Is+P1+hamvViuRLHwfPB4Pms2m7g2tV6w893q9auBiwDSfUzabxYcPH0TCsuqeezgVTrvdTu8Bv6/w3u/3e3g8HinIvF4vut2uiG621TEPi3vSarXSx+THoBLJsixEIhFkMhm0220kk0l8/PgRt7e38Hq9WK1WsG0bABTQ3ev1RFAZGBgYGBgYGBgY/F7wf6TIYfXs8XhUFgsHNf4gTasLSQuGn3IgpqKBNbPD4VCtSMFgEC6XSwoOKmMeHx/VIMR/yx/UgV9DLCmXf3h4QC6XU0sSyZThcKj6W55c85R+vV4jm83KBvBcws+gVOZV0IZVLpdfNN8wi4YKC1qbqFhiowyDobfbLfb7vWxYDGZl/kwul1OlcCQSQafTUf0yCYVAICCrEO0QV1dXGhrZ2ESry/O8GL/fryplDpGXl5c4nU5Sr9A69rx9hzaTbreL+XwutQgHLw5fjuMogDSVSilQlkoX5u9MJhMN3m63WyTdarWSnYQKnWKxqBYdhq/SesXTeFr+GOY8HA4VFDyfz1GpVLRWZrOZCDWGrQYCAT1bx3FUKc0Wnnw+j36/L6sPG4GYnZJKpdDtdrV+Gew7n8+1RqhCoPXFtm3E43G1uDE0Np/Py2LI5iASLmwW471nfs9qtcL19TVs21YbkOM4UqDxPuRyOfz1r3/F1dWVnhetdaxMp+KDa4HqEloDSVYyUJyNTIvFQgqfYDCI/X6PdDqNdruN7777Dg8PD1L4JBIJkSWxWAx/+ctfUKvVRCiQiGLdO+2VJO14LQxFH4/HUs6QzGFoNVuJttstyuUy/H4//u3f/g2vX7/Wu9XtdkUSRKNREba8tyQ5+XwZBE6iKxwOo16vv2hkeh5ETAJpNBrh7du36Pf7anmLRqMi+0gCkgSqVqtS3LExjmuYexuzr6LRqILUgV9Dg5n/FIvF0O12pVqaTCawLAuz2QyWZSnr7Hg8ytLU7/e1zxaLRbVq9ft9zOdzBINBkZMMl3/z5o32YNpfE4mEGt1oI2XoPAOyuSaYlcbnSyUS68in06nI/VKphFarBcuyRD5HIhG9i6lUCsPhUDk5AEQUjkYjkXq1Wk0ETigUUkW6gYGBgYGBgYGBwe8F1pk/vRoYGBgYGBgYGBgYGBgYGBgY/K7xxYocAwMDAwMDAwMDAwMDAwMDA4PfFobIMTAwMDAwMDAwMDAwMDAwMPiDwBA5BgYGBgYGBgYGBgYGBgYGBn8QGCLHwMDAwMDAwMDAwMDAwMDA4A8CQ+QYGBgYGBgYGBgYGBgYGBgY/EFgiBwDAwMDAwMDAwMDAwMDAwODPwgMkWNgYGBgYGBgYGBgYGBgYGDwB4EhcgwMDAwMDAwMDAwMDAwMDAz+IDBEjoGBgYGBgYGBgYGBgYGBgcEfBP8Dte+o3+CzgHYAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 1500x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"# Plot the images\n",
"plt.figure(figsize=(15, 5))\n",
"\n",
"plt.subplot(1, 2, 1)\n",
"plt.title('Tomogram')\n",
"plt.imshow(data_dicts[0]['image'][100],cmap='gray')\n",
"plt.axis('off')\n",
"\n",
"plt.subplot(1, 2, 2)\n",
"plt.title('Painted Segmentation from Picks')\n",
"plt.imshow(data_dicts[0]['label'][100], cmap='viridis')\n",
"plt.axis('off')\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "89f14aa2-87d6-4237-b41c-d07e3e31d0f2",
"metadata": {},
"source": [
"### 5. Prepare dataloaders"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "44234e02",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of training samples: 5\n",
"Number of validation samples: 2\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading dataset: 100%|██████████| 5/5 [00:00<00:00, 15.14it/s]\n",
"Loading dataset: 100%|██████████| 2/2 [00:00<00:00, 10.21it/s]\n"
]
}
],
"source": [
"my_num_samples = 16\n",
"train_batch_size = 1\n",
"val_batch_size = 1\n",
"\n",
"train_files, val_files = data_dicts[:5], data_dicts[5:7]\n",
"print(f\"Number of training samples: {len(train_files)}\")\n",
"print(f\"Number of validation samples: {len(val_files)}\")\n",
"\n",
"# Non-random transforms to be cached\n",
"non_random_transforms = Compose([\n",
" EnsureChannelFirstd(keys=[\"image\", \"label\"], channel_dim=\"no_channel\"),\n",
" NormalizeIntensityd(keys=\"image\"),\n",
" Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\")\n",
"])\n",
"\n",
"# Random transforms to be applied during training\n",
"random_transforms = Compose([\n",
" RandCropByLabelClassesd(\n",
" keys=[\"image\", \"label\"],\n",
" label_key=\"label\",\n",
" spatial_size=[96, 96, 96],\n",
" num_classes=8,\n",
" num_samples=my_num_samples\n",
" ),\n",
" RandRotate90d(keys=[\"image\", \"label\"], prob=0.5, spatial_axes=[0, 2]),\n",
" RandFlipd(keys=[\"image\", \"label\"], prob=0.5, spatial_axis=0), \n",
"])\n",
"\n",
"# Create the cached dataset with non-random transforms\n",
"train_ds = CacheDataset(data=train_files, transform=non_random_transforms, cache_rate=1.0)\n",
"\n",
"# Wrap the cached dataset to apply random transforms during iteration\n",
"train_ds = Dataset(data=train_ds, transform=random_transforms)\n",
"\n",
"# DataLoader remains the same\n",
"train_loader = DataLoader(\n",
" train_ds,\n",
" batch_size=train_batch_size,\n",
" shuffle=True,\n",
" num_workers=4,\n",
" pin_memory=torch.cuda.is_available()\n",
")\n",
"\n",
"# Validation transforms\n",
"val_transforms = Compose([\n",
" EnsureChannelFirstd(keys=[\"image\", \"label\"], channel_dim=\"no_channel\"),\n",
" NormalizeIntensityd(keys=\"image\"),\n",
" RandCropByLabelClassesd(\n",
" keys=[\"image\", \"label\"],\n",
" label_key=\"label\",\n",
" spatial_size=[96, 96, 96],\n",
" num_classes=8,\n",
" num_samples=my_num_samples, # Use 1 to get a single, consistent crop per image\n",
" ),\n",
"])\n",
"\n",
"# Create validation dataset\n",
"val_ds = CacheDataset(data=val_files, transform=non_random_transforms, cache_rate=1.0)\n",
"\n",
"# Wrap the cached dataset to apply random transforms during iteration\n",
"val_ds = Dataset(data=val_ds, transform=random_transforms)\n",
"\n",
"# Create validation DataLoader\n",
"val_loader = DataLoader(\n",
" val_ds,\n",
" batch_size=val_batch_size,\n",
" num_workers=4,\n",
" pin_memory=torch.cuda.is_available(),\n",
" shuffle=False, # Ensure the data order remains consistent\n",
")"
]
},
{
"cell_type": "markdown",
"id": "c7222751-50e7-443b-ab8f-4a380aecb021",
"metadata": {},
"source": [
"## Model setup"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "6cd8eb5b-200c-49a5-939f-5221e0a023e6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cuda\n"
]
}
],
"source": [
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"print(device)\n",
"# Create UNet, DiceLoss and Adam optimizer\n",
"model = UNet(\n",
" spatial_dims=3,\n",
" in_channels=1,\n",
" out_channels=len(root.pickable_objects)+1,\n",
" channels=(48, 64, 80, 80),\n",
" strides=(2, 2, 1),\n",
" num_res_units=1,\n",
").to(device)\n",
"\n",
"lr = 1e-3\n",
"optimizer = torch.optim.Adam(model.parameters(), lr)\n",
"#loss_function = DiceLoss(include_background=True, to_onehot_y=True, softmax=True) # softmax=True for multiclass\n",
"loss_function = TverskyLoss(include_background=True, to_onehot_y=True, softmax=True) # softmax=True for multiclass\n",
"dice_metric = DiceMetric(include_background=False, reduction=\"mean\", ignore_empty=True) # must use onehot for multiclass\n",
"recall_metric = ConfusionMatrixMetric(include_background=False, metric_name=\"recall\", reduction=\"None\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "70284f0c-6e29-42d3-87d7-d7f49209dcba",
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"post_pred = AsDiscrete(argmax=True, to_onehot=len(root.pickable_objects)+1)\n",
"post_label = AsDiscrete(to_onehot=len(root.pickable_objects)+1)\n",
"\n",
"def train(train_loader, model, loss_function, metrics_function, optimizer, max_epochs=100):\n",
" val_interval = 2\n",
" best_metric = -1\n",
" best_metric_epoch = -1\n",
" epoch_loss_values = []\n",
" metric_values = []\n",
" for epoch in range(max_epochs):\n",
" print(\"-\" * 10)\n",
" print(f\"epoch {epoch + 1}/{max_epochs}\")\n",
" model.train()\n",
" epoch_loss = 0\n",
" step = 0\n",
" for batch_data in train_loader:\n",
" step += 1\n",
" inputs = batch_data[\"image\"].to(device)\n",
" labels = batch_data[\"label\"].to(device)\n",
" optimizer.zero_grad()\n",
" outputs = model(inputs)\n",
" loss = loss_function(outputs, labels)\n",
" loss.backward()\n",
" optimizer.step()\n",
" epoch_loss += loss.item()\n",
" print(f\"batch {step}/{len(train_ds) // train_loader.batch_size}, \" f\"train_loss: {loss.item():.4f}\")\n",
" epoch_loss /= step\n",
" epoch_loss_values.append(epoch_loss)\n",
" print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n",
" mlflow.log_metric(\"train_loss\", epoch_loss, step=epoch+1)\n",
"\n",
" if (epoch + 1) % val_interval == 0:\n",
" model.eval()\n",
" with torch.no_grad():\n",
" for val_data in val_loader:\n",
" val_inputs = val_data[\"image\"].to(device)\n",
" val_labels = val_data[\"label\"].to(device)\n",
" val_outputs = model(val_inputs)\n",
" metric_val_outputs = [post_pred(i) for i in decollate_batch(val_outputs)]\n",
" metric_val_labels = [post_label(i) for i in decollate_batch(val_labels)]\n",
" \n",
" \n",
" # compute metric for current iteration\n",
" metrics_function(y_pred=metric_val_outputs, y=metric_val_labels)\n",
"\n",
" metrics = metrics_function.aggregate(reduction=\"mean_batch\")\n",
" metric_per_class = [\"{:.4g}\".format(x) for x in metrics]\n",
" metric = torch.mean(metrics).numpy(force=True)\n",
" mlflow.log_metric(\"validation metric\", metric, step=epoch+1)\n",
" for i,m in enumerate(metrics):\n",
" mlflow.log_metric(f\"validation metric class {i+1}\", m, step=epoch+1)\n",
" metrics_function.reset()\n",
"\n",
" metric_values.append(metric)\n",
" if metric > best_metric:\n",
" best_metric = metric\n",
" best_metric_epoch = epoch + 1\n",
" torch.save(model.state_dict(), os.path.join('./', \"best_metric_model.pth\"))\n",
" \n",
" print(\"saved new best metric model\")\n",
" print(\n",
" f\"current epoch: {epoch + 1} current mean recall per class: {', '.join(metric_per_class)}\"\n",
" f\"\\nbest mean recall: {best_metric:.4f} \"\n",
" f\"at epoch: {best_metric_epoch}\"\n",
" )"
]
},
{
"cell_type": "markdown",
"id": "b8f55d1f-4601-47dd-a666-b3c519c4632c",
"metadata": {},
"source": [
"## Training and tracking"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "0b8e2a2c-29dc-4615-9ee6-830f693d0c58",
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------\n",
"epoch 1/200\n",
"batch 1/5, train_loss: 0.9563\n",
"batch 2/5, train_loss: 0.9532\n",
"batch 3/5, train_loss: 0.9472\n",
"batch 4/5, train_loss: 0.9418\n",
"batch 5/5, train_loss: 0.9325\n",
"epoch 1 average loss: 0.9462\n",
"----------\n",
"epoch 2/200\n",
"batch 1/5, train_loss: 0.9285\n",
"batch 2/5, train_loss: 0.9273\n",
"batch 3/5, train_loss: 0.9196\n",
"batch 4/5, train_loss: 0.9198\n",
"batch 5/5, train_loss: 0.9161\n",
"epoch 2 average loss: 0.9222\n",
"saved new best metric model\n",
"current epoch: 2 current mean recall per class: 0.03788, 0.01056, 0.02347, 0.003582, 0.3222, 0.01952, 0.008017\n",
"best mean recall: 0.0607 at epoch: 2\n",
"----------\n",
"epoch 3/200\n",
"batch 1/5, train_loss: 0.9038\n",
"batch 2/5, train_loss: 0.8937\n",
"batch 3/5, train_loss: 0.9015\n",
"batch 4/5, train_loss: 0.8887\n",
"batch 5/5, train_loss: 0.8954\n",
"epoch 3 average loss: 0.8966\n",
"----------\n",
"epoch 4/200\n",
"batch 1/5, train_loss: 0.8856\n",
"batch 2/5, train_loss: 0.8819\n",
"batch 3/5, train_loss: 0.8863\n",
"batch 4/5, train_loss: 0.8716\n",
"batch 5/5, train_loss: 0.8680\n",
"epoch 4 average loss: 0.8787\n",
"current epoch: 4 current mean recall per class: 0.01587, 0.002457, 0.0362, 0.000348, 0.2891, 0.01627, 0.004173\n",
"best mean recall: 0.0607 at epoch: 2\n",
"----------\n",
"epoch 5/200\n",
"batch 1/5, train_loss: 0.8765\n",
"batch 2/5, train_loss: 0.8596\n",
"batch 3/5, train_loss: 0.8657\n",
"batch 4/5, train_loss: 0.8546\n",
"batch 5/5, train_loss: 0.8524\n",
"epoch 5 average loss: 0.8618\n",
"----------\n",
"epoch 6/200\n",
"batch 1/5, train_loss: 0.8652\n",
"batch 2/5, train_loss: 0.8553\n",
"batch 3/5, train_loss: 0.8497\n",
"batch 4/5, train_loss: 0.8292\n",
"batch 5/5, train_loss: 0.8628\n",
"epoch 6 average loss: 0.8524\n",
"current epoch: 6 current mean recall per class: 0.009953, 0.009403, 0.0225, 1.372e-05, 0.337, 0.0004583, 0.0007762\n",
"best mean recall: 0.0607 at epoch: 2\n",
"----------\n",
"epoch 7/200\n",
"batch 1/5, train_loss: 0.8390\n",
"batch 2/5, train_loss: 0.8286\n",
"batch 3/5, train_loss: 0.8505\n",
"batch 4/5, train_loss: 0.8376\n",
"batch 5/5, train_loss: 0.8239\n",
"epoch 7 average loss: 0.8359\n",
"----------\n",
"epoch 8/200\n",
"batch 1/5, train_loss: 0.8359\n",
"batch 2/5, train_loss: 0.8416\n",
"batch 3/5, train_loss: 0.8356\n",
"batch 4/5, train_loss: 0.8166\n",
"batch 5/5, train_loss: 0.8465\n",
"epoch 8 average loss: 0.8352\n",
"saved new best metric model\n",
"current epoch: 8 current mean recall per class: 0.001088, 0.04174, 0.04748, 0.004388, 0.5121, 0, 0\n",
"best mean recall: 0.0867 at epoch: 8\n",
"----------\n",
"epoch 9/200\n",
"batch 1/5, train_loss: 0.8348\n",
"batch 2/5, train_loss: 0.8477\n",
"batch 3/5, train_loss: 0.8322\n",
"batch 4/5, train_loss: 0.8267\n",
"batch 5/5, train_loss: 0.8141\n",
"epoch 9 average loss: 0.8311\n",
"----------\n",
"epoch 10/200\n",
"batch 1/5, train_loss: 0.8286\n",
"batch 2/5, train_loss: 0.8204\n",
"batch 3/5, train_loss: 0.8136\n",
"batch 4/5, train_loss: 0.8086\n",
"batch 5/5, train_loss: 0.8123\n",
"epoch 10 average loss: 0.8167\n",
"saved new best metric model\n",
"current epoch: 10 current mean recall per class: 0.0008377, 0.1311, 0.07802, 0.01412, 0.4434, 0, 0\n",
"best mean recall: 0.0953 at epoch: 10\n",
"----------\n",
"epoch 11/200\n",
"batch 1/5, train_loss: 0.8184\n",
"batch 2/5, train_loss: 0.8031\n",
"batch 3/5, train_loss: 0.8086\n",
"batch 4/5, train_loss: 0.8392\n",
"batch 5/5, train_loss: 0.8043\n",
"epoch 11 average loss: 0.8147\n",
"----------\n",
"epoch 12/200\n",
"batch 1/5, train_loss: 0.8087\n",
"batch 2/5, train_loss: 0.8324\n",
"batch 3/5, train_loss: 0.8388\n",
"batch 4/5, train_loss: 0.8308\n",
"batch 5/5, train_loss: 0.8195\n",
"epoch 12 average loss: 0.8260\n",
"saved new best metric model\n",
"current epoch: 12 current mean recall per class: 0.0007773, 0.1026, 0.07875, 0.05783, 0.5366, 0, 0\n",
"best mean recall: 0.1109 at epoch: 12\n",
"----------\n",
"epoch 13/200\n",
"batch 1/5, train_loss: 0.8169\n",
"batch 2/5, train_loss: 0.7823\n",
"batch 3/5, train_loss: 0.8170\n",
"batch 4/5, train_loss: 0.8280\n",
"batch 5/5, train_loss: 0.8023\n",
"epoch 13 average loss: 0.8093\n",
"----------\n",
"epoch 14/200\n",
"batch 1/5, train_loss: 0.8106\n",
"batch 2/5, train_loss: 0.7956\n",
"batch 3/5, train_loss: 0.8052\n",
"batch 4/5, train_loss: 0.8113\n",
"batch 5/5, train_loss: 0.7977\n",
"epoch 14 average loss: 0.8041\n",
"current epoch: 14 current mean recall per class: 0.0002548, 0.08705, 0.1202, 0.07057, 0.2911, 0, 0\n",
"best mean recall: 0.1109 at epoch: 12\n",
"----------\n",
"epoch 15/200\n",
"batch 1/5, train_loss: 0.8072\n",
"batch 2/5, train_loss: 0.8317\n",
"batch 3/5, train_loss: 0.7806\n",
"batch 4/5, train_loss: 0.7874\n",
"batch 5/5, train_loss: 0.7992\n",
"epoch 15 average loss: 0.8012\n",
"----------\n",
"epoch 16/200\n",
"batch 1/5, train_loss: 0.7988\n",
"batch 2/5, train_loss: 0.8164\n",
"batch 3/5, train_loss: 0.7992\n",
"batch 4/5, train_loss: 0.7804\n",
"batch 5/5, train_loss: 0.7886\n",
"epoch 16 average loss: 0.7967\n",
"current epoch: 16 current mean recall per class: 0.001154, 0.03475, 0.1546, 0.1119, 0.2166, 0, 0\n",
"best mean recall: 0.1109 at epoch: 12\n",
"----------\n",
"epoch 17/200\n",
"batch 1/5, train_loss: 0.7927\n",
"batch 2/5, train_loss: 0.8342\n",
"batch 3/5, train_loss: 0.8057\n",
"batch 4/5, train_loss: 0.7740\n",
"batch 5/5, train_loss: 0.7892\n",
"epoch 17 average loss: 0.7992\n",
"----------\n",
"epoch 18/200\n",
"batch 1/5, train_loss: 0.7948\n",
"batch 2/5, train_loss: 0.7667\n",
"batch 3/5, train_loss: 0.8141\n",
"batch 4/5, train_loss: 0.7895\n",
"batch 5/5, train_loss: 0.7862\n",
"epoch 18 average loss: 0.7903\n",
"current epoch: 18 current mean recall per class: 0.0004407, 0.03009, 0.1646, 0.1414, 0.3861, 0, 0\n",
"best mean recall: 0.1109 at epoch: 12\n",
"----------\n",
"epoch 19/200\n",
"batch 1/5, train_loss: 0.7797\n",
"batch 2/5, train_loss: 0.7824\n",
"batch 3/5, train_loss: 0.8100\n",
"batch 4/5, train_loss: 0.7700\n",
"batch 5/5, train_loss: 0.8089\n",
"epoch 19 average loss: 0.7902\n",
"----------\n",
"epoch 20/200\n",
"batch 1/5, train_loss: 0.8190\n",
"batch 2/5, train_loss: 0.8038\n",
"batch 3/5, train_loss: 0.7806\n",
"batch 4/5, train_loss: 0.7982\n",
"batch 5/5, train_loss: 0.7884\n",
"epoch 20 average loss: 0.7980\n",
"current epoch: 20 current mean recall per class: 0.002856, 0.02608, 0.1848, 0.05934, 0.464, 0, 0\n",
"best mean recall: 0.1109 at epoch: 12\n",
"----------\n",
"epoch 21/200\n",
"batch 1/5, train_loss: 0.7804\n",
"batch 2/5, train_loss: 0.7839\n",
"batch 3/5, train_loss: 0.7719\n",
"batch 4/5, train_loss: 0.7915\n",
"batch 5/5, train_loss: 0.7958\n",
"epoch 21 average loss: 0.7847\n",
"----------\n",
"epoch 22/200\n",
"batch 1/5, train_loss: 0.7575\n",
"batch 2/5, train_loss: 0.7720\n",
"batch 3/5, train_loss: 0.8154\n",
"batch 4/5, train_loss: 0.7646\n",
"batch 5/5, train_loss: 0.7777\n",
"epoch 22 average loss: 0.7775\n",
"saved new best metric model\n",
"current epoch: 22 current mean recall per class: 0.000393, 0.05365, 0.2128, 0.1571, 0.5161, 0, 0\n",
"best mean recall: 0.1343 at epoch: 22\n",
"----------\n",
"epoch 23/200\n",
"batch 1/5, train_loss: 0.7537\n",
"batch 2/5, train_loss: 0.7625\n",
"batch 3/5, train_loss: 0.7651\n",
"batch 4/5, train_loss: 0.8017\n",
"batch 5/5, train_loss: 0.7809\n",
"epoch 23 average loss: 0.7728\n",
"----------\n",
"epoch 24/200\n",
"batch 1/5, train_loss: 0.7576\n",
"batch 2/5, train_loss: 0.7812\n",
"batch 3/5, train_loss: 0.7528\n",
"batch 4/5, train_loss: 0.7943\n",
"batch 5/5, train_loss: 0.7782\n",
"epoch 24 average loss: 0.7728\n",
"current epoch: 24 current mean recall per class: 0.006077, 0.04232, 0.2099, 0.1068, 0.442, 0, 0\n",
"best mean recall: 0.1343 at epoch: 22\n",
"----------\n",
"epoch 25/200\n",
"batch 1/5, train_loss: 0.7614\n",
"batch 2/5, train_loss: 0.7570\n",
"batch 3/5, train_loss: 0.7643\n",
"batch 4/5, train_loss: 0.7963\n",
"batch 5/5, train_loss: 0.7859\n",
"epoch 25 average loss: 0.7730\n",
"----------\n",
"epoch 26/200\n",
"batch 1/5, train_loss: 0.7670\n",
"batch 2/5, train_loss: 0.7739\n",
"batch 3/5, train_loss: 0.7364\n",
"batch 4/5, train_loss: 0.7609\n",
"batch 5/5, train_loss: 0.8102\n",
"epoch 26 average loss: 0.7697\n",
"current epoch: 26 current mean recall per class: 0.0004247, 0.07503, 0.1958, 0.0875, 0.4244, 0, 0\n",
"best mean recall: 0.1343 at epoch: 22\n",
"----------\n",
"epoch 27/200\n",
"batch 1/5, train_loss: 0.7634\n",
"batch 2/5, train_loss: 0.7619\n",
"batch 3/5, train_loss: 0.7441\n",
"batch 4/5, train_loss: 0.7690\n",
"batch 5/5, train_loss: 0.7768\n",
"epoch 27 average loss: 0.7630\n",
"----------\n",
"epoch 28/200\n",
"batch 1/5, train_loss: 0.7833\n",
"batch 2/5, train_loss: 0.7645\n",
"batch 3/5, train_loss: 0.7401\n",
"batch 4/5, train_loss: 0.7555\n",
"batch 5/5, train_loss: 0.7772\n",
"epoch 28 average loss: 0.7641\n",
"saved new best metric model\n",
"current epoch: 28 current mean recall per class: 0.004878, 0.09144, 0.2084, 0.1895, 0.585, 0, 0\n",
"best mean recall: 0.1542 at epoch: 28\n",
"----------\n",
"epoch 29/200\n",
"batch 1/5, train_loss: 0.7622\n",
"batch 2/5, train_loss: 0.7461\n",
"batch 3/5, train_loss: 0.7427\n",
"batch 4/5, train_loss: 0.7907\n",
"batch 5/5, train_loss: 0.7655\n",
"epoch 29 average loss: 0.7614\n",
"----------\n",
"epoch 30/200\n",
"batch 1/5, train_loss: 0.7555\n",
"batch 2/5, train_loss: 0.7517\n",
"batch 3/5, train_loss: 0.7516\n",
"batch 4/5, train_loss: 0.7829\n",
"batch 5/5, train_loss: 0.7592\n",
"epoch 30 average loss: 0.7602\n",
"current epoch: 30 current mean recall per class: 0.003908, 0.08625, 0.2439, 0.1567, 0.4272, 0, 0\n",
"best mean recall: 0.1542 at epoch: 28\n",
"----------\n",
"epoch 31/200\n",
"batch 1/5, train_loss: 0.7691\n",
"batch 2/5, train_loss: 0.7383\n",
"batch 3/5, train_loss: 0.7476\n",
"batch 4/5, train_loss: 0.7999\n",
"batch 5/5, train_loss: 0.7402\n",
"epoch 31 average loss: 0.7590\n",
"----------\n",
"epoch 32/200\n",
"batch 1/5, train_loss: 0.7507\n",
"batch 2/5, train_loss: 0.7443\n",
"batch 3/5, train_loss: 0.7714\n",
"batch 4/5, train_loss: 0.7725\n",
"batch 5/5, train_loss: 0.7692\n",
"epoch 32 average loss: 0.7616\n",
"current epoch: 32 current mean recall per class: 0.01514, 0.1372, 0.2125, 0.1438, 0.511, 0, 0\n",
"best mean recall: 0.1542 at epoch: 28\n",
"----------\n",
"epoch 33/200\n",
"batch 1/5, train_loss: 0.7702\n",
"batch 2/5, train_loss: 0.7552\n",
"batch 3/5, train_loss: 0.7805\n",
"batch 4/5, train_loss: 0.7249\n",
"batch 5/5, train_loss: 0.7539\n",
"epoch 33 average loss: 0.7569\n",
"----------\n",
"epoch 34/200\n",
"batch 1/5, train_loss: 0.7347\n",
"batch 2/5, train_loss: 0.7345\n",
"batch 3/5, train_loss: 0.7933\n",
"batch 4/5, train_loss: 0.7623\n",
"batch 5/5, train_loss: 0.7649\n",
"epoch 34 average loss: 0.7579\n",
"current epoch: 34 current mean recall per class: 0.007701, 0.1506, 0.2504, 0.162, 0.4547, 0, 0\n",
"best mean recall: 0.1542 at epoch: 28\n",
"----------\n",
"epoch 35/200\n",
"batch 1/5, train_loss: 0.7450\n",
"batch 2/5, train_loss: 0.7374\n",
"batch 3/5, train_loss: 0.7404\n",
"batch 4/5, train_loss: 0.7468\n",
"batch 5/5, train_loss: 0.7819\n",
"epoch 35 average loss: 0.7503\n",
"----------\n",
"epoch 36/200\n",
"batch 1/5, train_loss: 0.7559\n",
"batch 2/5, train_loss: 0.7536\n",
"batch 3/5, train_loss: 0.7658\n",
"batch 4/5, train_loss: 0.7775\n",
"batch 5/5, train_loss: 0.7392\n",
"epoch 36 average loss: 0.7584\n",
"current epoch: 36 current mean recall per class: 0.008777, 0.1589, 0.2273, 0.1552, 0.4931, 1.363e-05, 3.054e-05\n",
"best mean recall: 0.1542 at epoch: 28\n",
"----------\n",
"epoch 37/200\n",
"batch 1/5, train_loss: 0.7550\n",
"batch 2/5, train_loss: 0.7792\n",
"batch 3/5, train_loss: 0.7035\n",
"batch 4/5, train_loss: 0.7087\n",
"batch 5/5, train_loss: 0.7162\n",
"epoch 37 average loss: 0.7325\n",
"----------\n",
"epoch 38/200\n",
"batch 1/5, train_loss: 0.7933\n",
"batch 2/5, train_loss: 0.7117\n",
"batch 3/5, train_loss: 0.7210\n",
"batch 4/5, train_loss: 0.7566\n",
"batch 5/5, train_loss: 0.7213\n",
"epoch 38 average loss: 0.7408\n",
"saved new best metric model\n",
"current epoch: 38 current mean recall per class: 0.008157, 0.2048, 0.2539, 0.1441, 0.5733, 0.01294, 0.01277\n",
"best mean recall: 0.1728 at epoch: 38\n",
"----------\n",
"epoch 39/200\n",
"batch 1/5, train_loss: 0.7677\n",
"batch 2/5, train_loss: 0.7429\n",
"batch 3/5, train_loss: 0.7243\n",
"batch 4/5, train_loss: 0.7003\n",
"batch 5/5, train_loss: 0.7143\n",
"epoch 39 average loss: 0.7299\n",
"----------\n",
"epoch 40/200\n",
"batch 1/5, train_loss: 0.6936\n",
"batch 2/5, train_loss: 0.7361\n",
"batch 3/5, train_loss: 0.7196\n",
"batch 4/5, train_loss: 0.7367\n",
"batch 5/5, train_loss: 0.7745\n",
"epoch 40 average loss: 0.7321\n",
"current epoch: 40 current mean recall per class: 0.01636, 0.1996, 0.2386, 0.1818, 0.4502, 0.009808, 0.04731\n",
"best mean recall: 0.1728 at epoch: 38\n",
"----------\n",
"epoch 41/200\n",
"batch 1/5, train_loss: 0.7315\n",
"batch 2/5, train_loss: 0.7325\n",
"batch 3/5, train_loss: 0.7355\n",
"batch 4/5, train_loss: 0.7141\n",
"batch 5/5, train_loss: 0.7345\n",
"epoch 41 average loss: 0.7296\n",
"----------\n",
"epoch 42/200\n",
"batch 1/5, train_loss: 0.7374\n",
"batch 2/5, train_loss: 0.6936\n",
"batch 3/5, train_loss: 0.7011\n",
"batch 4/5, train_loss: 0.7408\n",
"batch 5/5, train_loss: 0.7053\n",
"epoch 42 average loss: 0.7156\n",
"saved new best metric model\n",
"current epoch: 42 current mean recall per class: 0.0005693, 0.2954, 0.2787, 0.1356, 0.469, 0.1473, 0.1208\n",
"best mean recall: 0.2068 at epoch: 42\n",
"----------\n",
"epoch 43/200\n",
"batch 1/5, train_loss: 0.6923\n",
"batch 2/5, train_loss: 0.7366\n",
"batch 3/5, train_loss: 0.6750\n",
"batch 4/5, train_loss: 0.7054\n",
"batch 5/5, train_loss: 0.7546\n",
"epoch 43 average loss: 0.7128\n",
"----------\n",
"epoch 44/200\n",
"batch 1/5, train_loss: 0.6816\n",
"batch 2/5, train_loss: 0.7143\n",
"batch 3/5, train_loss: 0.7087\n",
"batch 4/5, train_loss: 0.7283\n",
"batch 5/5, train_loss: 0.6945\n",
"epoch 44 average loss: 0.7055\n",
"saved new best metric model\n",
"current epoch: 44 current mean recall per class: 3.024e-05, 0.3381, 0.2306, 0.1592, 0.3851, 0.1907, 0.2848\n",
"best mean recall: 0.2269 at epoch: 44\n",
"----------\n",
"epoch 45/200\n",
"batch 1/5, train_loss: 0.7558\n",
"batch 2/5, train_loss: 0.7098\n",
"batch 3/5, train_loss: 0.6796\n",
"batch 4/5, train_loss: 0.6718\n",
"batch 5/5, train_loss: 0.7355\n",
"epoch 45 average loss: 0.7105\n",
"----------\n",
"epoch 46/200\n",
"batch 1/5, train_loss: 0.7047\n",
"batch 2/5, train_loss: 0.7060\n",
"batch 3/5, train_loss: 0.7084\n",
"batch 4/5, train_loss: 0.6907\n",
"batch 5/5, train_loss: 0.7363\n",
"epoch 46 average loss: 0.7092\n",
"current epoch: 46 current mean recall per class: 0, 0.1685, 0.3246, 0.1348, 0.4879, 0.1783, 0.1965\n",
"best mean recall: 0.2269 at epoch: 44\n",
"----------\n",
"epoch 47/200\n",
"batch 1/5, train_loss: 0.6642\n",
"batch 2/5, train_loss: 0.7189\n",
"batch 3/5, train_loss: 0.6991\n",
"batch 4/5, train_loss: 0.6934\n",
"batch 5/5, train_loss: 0.7083\n",
"epoch 47 average loss: 0.6968\n",
"----------\n",
"epoch 48/200\n",
"batch 1/5, train_loss: 0.7000\n",
"batch 2/5, train_loss: 0.6997\n",
"batch 3/5, train_loss: 0.7084\n",
"batch 4/5, train_loss: 0.7230\n",
"batch 5/5, train_loss: 0.7099\n",
"epoch 48 average loss: 0.7082\n",
"saved new best metric model\n",
"current epoch: 48 current mean recall per class: 0.0002188, 0.3376, 0.2592, 0.1604, 0.4819, 0.2779, 0.1553\n",
"best mean recall: 0.2389 at epoch: 48\n",
"----------\n",
"epoch 49/200\n",
"batch 1/5, train_loss: 0.6701\n",
"batch 2/5, train_loss: 0.7467\n",
"batch 3/5, train_loss: 0.6860\n",
"batch 4/5, train_loss: 0.7077\n",
"batch 5/5, train_loss: 0.6912\n",
"epoch 49 average loss: 0.7004\n",
"----------\n",
"epoch 50/200\n",
"batch 1/5, train_loss: 0.6682\n",
"batch 2/5, train_loss: 0.6846\n",
"batch 3/5, train_loss: 0.7177\n",
"batch 4/5, train_loss: 0.7120\n",
"batch 5/5, train_loss: 0.6535\n",
"epoch 50 average loss: 0.6872\n",
"saved new best metric model\n",
"current epoch: 50 current mean recall per class: 0.0004473, 0.2363, 0.2911, 0.2392, 0.381, 0.3509, 0.363\n",
"best mean recall: 0.2660 at epoch: 50\n",
"----------\n",
"epoch 51/200\n",
"batch 1/5, train_loss: 0.6992\n",
"batch 2/5, train_loss: 0.6661\n",
"batch 3/5, train_loss: 0.6962\n",
"batch 4/5, train_loss: 0.6884\n",
"batch 5/5, train_loss: 0.6915\n",
"epoch 51 average loss: 0.6883\n",
"----------\n",
"epoch 52/200\n",
"batch 1/5, train_loss: 0.7193\n",
"batch 2/5, train_loss: 0.6511\n",
"batch 3/5, train_loss: 0.6452\n",
"batch 4/5, train_loss: 0.6829\n",
"batch 5/5, train_loss: 0.7056\n",
"epoch 52 average loss: 0.6808\n",
"current epoch: 52 current mean recall per class: 0.000115, 0.2228, 0.2364, 0.262, 0.4065, 0.2696, 0.2173\n",
"best mean recall: 0.2660 at epoch: 50\n",
"----------\n",
"epoch 53/200\n",
"batch 1/5, train_loss: 0.6894\n",
"batch 2/5, train_loss: 0.6541\n",
"batch 3/5, train_loss: 0.6503\n",
"batch 4/5, train_loss: 0.7217\n",
"batch 5/5, train_loss: 0.6602\n",
"epoch 53 average loss: 0.6751\n",
"----------\n",
"epoch 54/200\n",
"batch 1/5, train_loss: 0.6713\n",
"batch 2/5, train_loss: 0.6929\n",
"batch 3/5, train_loss: 0.6724\n",
"batch 4/5, train_loss: 0.6520\n",
"batch 5/5, train_loss: 0.7057\n",
"epoch 54 average loss: 0.6789\n",
"current epoch: 54 current mean recall per class: 0, 0.3563, 0.2943, 0.09173, 0.4517, 0.2635, 0.2236\n",
"best mean recall: 0.2660 at epoch: 50\n",
"----------\n",
"epoch 55/200\n",
"batch 1/5, train_loss: 0.6473\n",
"batch 2/5, train_loss: 0.6335\n",
"batch 3/5, train_loss: 0.6470\n",
"batch 4/5, train_loss: 0.7205\n",
"batch 5/5, train_loss: 0.6928\n",
"epoch 55 average loss: 0.6682\n",
"----------\n",
"epoch 56/200\n",
"batch 1/5, train_loss: 0.6716\n",
"batch 2/5, train_loss: 0.6545\n",
"batch 3/5, train_loss: 0.6769\n",
"batch 4/5, train_loss: 0.7253\n",
"batch 5/5, train_loss: 0.6608\n",
"epoch 56 average loss: 0.6778\n",
"current epoch: 56 current mean recall per class: 8.574e-06, 0.3118, 0.2547, 0.2974, 0.3674, 0.2162, 0.1524\n",
"best mean recall: 0.2660 at epoch: 50\n",
"----------\n",
"epoch 57/200\n",
"batch 1/5, train_loss: 0.6521\n",
"batch 2/5, train_loss: 0.6774\n",
"batch 3/5, train_loss: 0.7280\n",
"batch 4/5, train_loss: 0.6990\n",
"batch 5/5, train_loss: 0.6600\n",
"epoch 57 average loss: 0.6833\n",
"----------\n",
"epoch 58/200\n",
"batch 1/5, train_loss: 0.6860\n",
"batch 2/5, train_loss: 0.6641\n",
"batch 3/5, train_loss: 0.6813\n",
"batch 4/5, train_loss: 0.6785\n",
"batch 5/5, train_loss: 0.7004\n",
"epoch 58 average loss: 0.6821\n",
"current epoch: 58 current mean recall per class: 4.424e-05, 0.2988, 0.2912, 0.2541, 0.4526, 0.3317, 0.09739\n",
"best mean recall: 0.2660 at epoch: 50\n",
"----------\n",
"epoch 59/200\n",
"batch 1/5, train_loss: 0.7178\n",
"batch 2/5, train_loss: 0.6595\n",
"batch 3/5, train_loss: 0.6515\n",
"batch 4/5, train_loss: 0.6557\n",
"batch 5/5, train_loss: 0.7006\n",
"epoch 59 average loss: 0.6770\n",
"----------\n",
"epoch 60/200\n",
"batch 1/5, train_loss: 0.6695\n",
"batch 2/5, train_loss: 0.6849\n",
"batch 3/5, train_loss: 0.6456\n",
"batch 4/5, train_loss: 0.6601\n",
"batch 5/5, train_loss: 0.6413\n",
"epoch 60 average loss: 0.6603\n",
"saved new best metric model\n",
"current epoch: 60 current mean recall per class: 6.278e-06, 0.2826, 0.2694, 0.292, 0.4893, 0.3045, 0.2534\n",
"best mean recall: 0.2702 at epoch: 60\n",
"----------\n",
"epoch 61/200\n",
"batch 1/5, train_loss: 0.6708\n",
"batch 2/5, train_loss: 0.6856\n",
"batch 3/5, train_loss: 0.6658\n",
"batch 4/5, train_loss: 0.6730\n",
"batch 5/5, train_loss: 0.6501\n",
"epoch 61 average loss: 0.6691\n",
"----------\n",
"epoch 62/200\n",
"batch 1/5, train_loss: 0.6776\n",
"batch 2/5, train_loss: 0.6583\n",
"batch 3/5, train_loss: 0.6307\n",
"batch 4/5, train_loss: 0.6451\n",
"batch 5/5, train_loss: 0.7093\n",
"epoch 62 average loss: 0.6642\n",
"saved new best metric model\n",
"current epoch: 62 current mean recall per class: 0, 0.3622, 0.2893, 0.2502, 0.4832, 0.3797, 0.3468\n",
"best mean recall: 0.3016 at epoch: 62\n",
"----------\n",
"epoch 63/200\n",
"batch 1/5, train_loss: 0.6724\n",
"batch 2/5, train_loss: 0.6230\n",
"batch 3/5, train_loss: 0.6345\n",
"batch 4/5, train_loss: 0.6660\n",
"batch 5/5, train_loss: 0.6961\n",
"epoch 63 average loss: 0.6584\n",
"----------\n",
"epoch 64/200\n",
"batch 1/5, train_loss: 0.6904\n",
"batch 2/5, train_loss: 0.6519\n",
"batch 3/5, train_loss: 0.6615\n",
"batch 4/5, train_loss: 0.6749\n",
"batch 5/5, train_loss: 0.6710\n",
"epoch 64 average loss: 0.6700\n",
"current epoch: 64 current mean recall per class: 1.077e-05, 0.2704, 0.2622, 0.1943, 0.4585, 0.4113, 0.1999\n",
"best mean recall: 0.3016 at epoch: 62\n",
"----------\n",
"epoch 65/200\n",
"batch 1/5, train_loss: 0.6654\n",
"batch 2/5, train_loss: 0.7003\n",
"batch 3/5, train_loss: 0.6304\n",
"batch 4/5, train_loss: 0.6502\n",
"batch 5/5, train_loss: 0.6910\n",
"epoch 65 average loss: 0.6675\n",
"----------\n",
"epoch 66/200\n",
"batch 1/5, train_loss: 0.7013\n",
"batch 2/5, train_loss: 0.6398\n",
"batch 3/5, train_loss: 0.6466\n",
"batch 4/5, train_loss: 0.6182\n",
"batch 5/5, train_loss: 0.6914\n",
"epoch 66 average loss: 0.6595\n",
"current epoch: 66 current mean recall per class: 8.846e-06, 0.3218, 0.3026, 0.1903, 0.3533, 0.4496, 0.09209\n",
"best mean recall: 0.3016 at epoch: 62\n",
"----------\n",
"epoch 67/200\n",
"batch 1/5, train_loss: 0.6408\n",
"batch 2/5, train_loss: 0.6755\n",
"batch 3/5, train_loss: 0.6647\n",
"batch 4/5, train_loss: 0.6918\n",
"batch 5/5, train_loss: 0.6242\n",
"epoch 67 average loss: 0.6594\n",
"----------\n",
"epoch 68/200\n",
"batch 1/5, train_loss: 0.6689\n",
"batch 2/5, train_loss: 0.6875\n",
"batch 3/5, train_loss: 0.6482\n",
"batch 4/5, train_loss: 0.6339\n",
"batch 5/5, train_loss: 0.6314\n",
"epoch 68 average loss: 0.6540\n",
"current epoch: 68 current mean recall per class: 0, 0.3019, 0.3089, 0.2263, 0.332, 0.2726, 0.1412\n",
"best mean recall: 0.3016 at epoch: 62\n",
"----------\n",
"epoch 69/200\n",
"batch 1/5, train_loss: 0.7058\n",
"batch 2/5, train_loss: 0.6588\n",
"batch 3/5, train_loss: 0.6214\n",
"batch 4/5, train_loss: 0.6372\n",
"batch 5/5, train_loss: 0.6740\n",
"epoch 69 average loss: 0.6595\n",
"----------\n",
"epoch 70/200\n",
"batch 1/5, train_loss: 0.6561\n",
"batch 2/5, train_loss: 0.6795\n",
"batch 3/5, train_loss: 0.6847\n",
"batch 4/5, train_loss: 0.6093\n",
"batch 5/5, train_loss: 0.6278\n",
"epoch 70 average loss: 0.6515\n",
"current epoch: 70 current mean recall per class: 0, 0.3849, 0.314, 0.2401, 0.3535, 0.2176, 0.1902\n",
"best mean recall: 0.3016 at epoch: 62\n",
"----------\n",
"epoch 71/200\n",
"batch 1/5, train_loss: 0.6518\n",
"batch 2/5, train_loss: 0.6625\n",
"batch 3/5, train_loss: 0.6767\n",
"batch 4/5, train_loss: 0.6674\n",
"batch 5/5, train_loss: 0.6327\n",
"epoch 71 average loss: 0.6582\n",
"----------\n",
"epoch 72/200\n",
"batch 1/5, train_loss: 0.6581\n",
"batch 2/5, train_loss: 0.6544\n",
"batch 3/5, train_loss: 0.6512\n",
"batch 4/5, train_loss: 0.6924\n",
"batch 5/5, train_loss: 0.6201\n",
"epoch 72 average loss: 0.6552\n",
"current epoch: 72 current mean recall per class: 0, 0.3823, 0.2882, 0.1844, 0.2402, 0.26, 0.2185\n",
"best mean recall: 0.3016 at epoch: 62\n",
"----------\n",
"epoch 73/200\n",
"batch 1/5, train_loss: 0.6144\n",
"batch 2/5, train_loss: 0.7004\n",
"batch 3/5, train_loss: 0.6414\n",
"batch 4/5, train_loss: 0.6353\n",
"batch 5/5, train_loss: 0.6294\n",
"epoch 73 average loss: 0.6442\n",
"----------\n",
"epoch 74/200\n",
"batch 1/5, train_loss: 0.6800\n",
"batch 2/5, train_loss: 0.6666\n",
"batch 3/5, train_loss: 0.6674\n",
"batch 4/5, train_loss: 0.6416\n",
"batch 5/5, train_loss: 0.6525\n",
"epoch 74 average loss: 0.6616\n",
"current epoch: 74 current mean recall per class: 0, 0.3514, 0.3168, 0.2317, 0.4099, 0.4495, 0.2569\n",
"best mean recall: 0.3016 at epoch: 62\n",
"----------\n",
"epoch 75/200\n",
"batch 1/5, train_loss: 0.6516\n",
"batch 2/5, train_loss: 0.6831\n",
"batch 3/5, train_loss: 0.6884\n",
"batch 4/5, train_loss: 0.6075\n",
"batch 5/5, train_loss: 0.6529\n",
"epoch 75 average loss: 0.6567\n",
"----------\n",
"epoch 76/200\n",
"batch 1/5, train_loss: 0.6787\n",
"batch 2/5, train_loss: 0.6677\n",
"batch 3/5, train_loss: 0.6423\n",
"batch 4/5, train_loss: 0.6104\n",
"batch 5/5, train_loss: 0.6164\n",
"epoch 76 average loss: 0.6431\n",
"current epoch: 76 current mean recall per class: 0, 0.3657, 0.3175, 0.3, 0.3766, 0.3784, 0.2266\n",
"best mean recall: 0.3016 at epoch: 62\n",
"----------\n",
"epoch 77/200\n",
"batch 1/5, train_loss: 0.6333\n",
"batch 2/5, train_loss: 0.6285\n",
"batch 3/5, train_loss: 0.6864\n",
"batch 4/5, train_loss: 0.5926\n",
"batch 5/5, train_loss: 0.6545\n",
"epoch 77 average loss: 0.6391\n",
"----------\n",
"epoch 78/200\n",
"batch 1/5, train_loss: 0.6544\n",
"batch 2/5, train_loss: 0.6592\n",
"batch 3/5, train_loss: 0.6323\n",
"batch 4/5, train_loss: 0.6359\n",
"batch 5/5, train_loss: 0.6131\n",
"epoch 78 average loss: 0.6390\n",
"current epoch: 78 current mean recall per class: 0, 0.3111, 0.3292, 0.2056, 0.4229, 0.3804, 0.06854\n",
"best mean recall: 0.3016 at epoch: 62\n",
"----------\n",
"epoch 79/200\n",
"batch 1/5, train_loss: 0.6513\n",
"batch 2/5, train_loss: 0.6489\n",
"batch 3/5, train_loss: 0.7048\n",
"batch 4/5, train_loss: 0.6935\n",
"batch 5/5, train_loss: 0.6048\n",
"epoch 79 average loss: 0.6607\n",
"----------\n",
"epoch 80/200\n",
"batch 1/5, train_loss: 0.6485\n",
"batch 2/5, train_loss: 0.6360\n",
"batch 3/5, train_loss: 0.7053\n",
"batch 4/5, train_loss: 0.6684\n",
"batch 5/5, train_loss: 0.6254\n",
"epoch 80 average loss: 0.6567\n",
"saved new best metric model\n",
"current epoch: 80 current mean recall per class: 4.963e-06, 0.3418, 0.2905, 0.3487, 0.4683, 0.3642, 0.3127\n",
"best mean recall: 0.3038 at epoch: 80\n",
"----------\n",
"epoch 81/200\n",
"batch 1/5, train_loss: 0.6028\n",
"batch 2/5, train_loss: 0.6367\n",
"batch 3/5, train_loss: 0.5899\n",
"batch 4/5, train_loss: 0.6576\n",
"batch 5/5, train_loss: 0.6852\n",
"epoch 81 average loss: 0.6345\n",
"----------\n",
"epoch 82/200\n",
"batch 1/5, train_loss: 0.6436\n",
"batch 2/5, train_loss: 0.5947\n",
"batch 3/5, train_loss: 0.6870\n",
"batch 4/5, train_loss: 0.6156\n",
"batch 5/5, train_loss: 0.6312\n",
"epoch 82 average loss: 0.6344\n",
"current epoch: 82 current mean recall per class: 0, 0.3983, 0.3009, 0.2765, 0.4413, 0.3979, 0.2621\n",
"best mean recall: 0.3038 at epoch: 80\n",
"----------\n",
"epoch 83/200\n",
"batch 1/5, train_loss: 0.6332\n",
"batch 2/5, train_loss: 0.6171\n",
"batch 3/5, train_loss: 0.6491\n",
"batch 4/5, train_loss: 0.6565\n",
"batch 5/5, train_loss: 0.6585\n",
"epoch 83 average loss: 0.6429\n",
"----------\n",
"epoch 84/200\n",
"batch 1/5, train_loss: 0.6518\n",
"batch 2/5, train_loss: 0.6773\n",
"batch 3/5, train_loss: 0.6689\n",
"batch 4/5, train_loss: 0.6438\n",
"batch 5/5, train_loss: 0.6026\n",
"epoch 84 average loss: 0.6489\n",
"current epoch: 84 current mean recall per class: 0, 0.3907, 0.2661, 0.2209, 0.3423, 0.3244, 0.3258\n",
"best mean recall: 0.3038 at epoch: 80\n",
"----------\n",
"epoch 85/200\n",
"batch 1/5, train_loss: 0.6746\n",
"batch 2/5, train_loss: 0.6590\n",
"batch 3/5, train_loss: 0.6108\n",
"batch 4/5, train_loss: 0.6034\n",
"batch 5/5, train_loss: 0.6223\n",
"epoch 85 average loss: 0.6340\n",
"----------\n",
"epoch 86/200\n",
"batch 1/5, train_loss: 0.7054\n",
"batch 2/5, train_loss: 0.6183\n",
"batch 3/5, train_loss: 0.6381\n",
"batch 4/5, train_loss: 0.6123\n",
"batch 5/5, train_loss: 0.6199\n",
"epoch 86 average loss: 0.6388\n",
"current epoch: 86 current mean recall per class: 0, 0.2623, 0.2968, 0.2285, 0.4533, 0.3993, 0.2957\n",
"best mean recall: 0.3038 at epoch: 80\n",
"----------\n",
"epoch 87/200\n",
"batch 1/5, train_loss: 0.6780\n",
"batch 2/5, train_loss: 0.6196\n",
"batch 3/5, train_loss: 0.5594\n",
"batch 4/5, train_loss: 0.6608\n",
"batch 5/5, train_loss: 0.6112\n",
"epoch 87 average loss: 0.6258\n",
"----------\n",
"epoch 88/200\n",
"batch 1/5, train_loss: 0.6553\n",
"batch 2/5, train_loss: 0.6316\n",
"batch 3/5, train_loss: 0.6289\n",
"batch 4/5, train_loss: 0.6288\n",
"batch 5/5, train_loss: 0.6621\n",
"epoch 88 average loss: 0.6413\n",
"current epoch: 88 current mean recall per class: 0, 0.3382, 0.2853, 0.1588, 0.5014, 0.3074, 0.2861\n",
"best mean recall: 0.3038 at epoch: 80\n",
"----------\n",
"epoch 89/200\n",
"batch 1/5, train_loss: 0.6021\n",
"batch 2/5, train_loss: 0.6796\n",
"batch 3/5, train_loss: 0.6117\n",
"batch 4/5, train_loss: 0.6938\n",
"batch 5/5, train_loss: 0.6628\n",
"epoch 89 average loss: 0.6500\n",
"----------\n",
"epoch 90/200\n",
"batch 1/5, train_loss: 0.6356\n",
"batch 2/5, train_loss: 0.5882\n",
"batch 3/5, train_loss: 0.5992\n",
"batch 4/5, train_loss: 0.6643\n",
"batch 5/5, train_loss: 0.6479\n",
"epoch 90 average loss: 0.6270\n",
"saved new best metric model\n",
"current epoch: 90 current mean recall per class: 0, 0.3159, 0.327, 0.3393, 0.4971, 0.4685, 0.262\n",
"best mean recall: 0.3157 at epoch: 90\n",
"----------\n",
"epoch 91/200\n",
"batch 1/5, train_loss: 0.6470\n",
"batch 2/5, train_loss: 0.6566\n",
"batch 3/5, train_loss: 0.6395\n",
"batch 4/5, train_loss: 0.6966\n",
"batch 5/5, train_loss: 0.6263\n",
"epoch 91 average loss: 0.6532\n",
"----------\n",
"epoch 92/200\n",
"batch 1/5, train_loss: 0.5979\n",
"batch 2/5, train_loss: 0.6103\n",
"batch 3/5, train_loss: 0.5663\n",
"batch 4/5, train_loss: 0.6656\n",
"batch 5/5, train_loss: 0.6748\n",
"epoch 92 average loss: 0.6230\n",
"current epoch: 92 current mean recall per class: 0, 0.3967, 0.2765, 0.2737, 0.548, 0.415, 0.2779\n",
"best mean recall: 0.3157 at epoch: 90\n",
"----------\n",
"epoch 93/200\n",
"batch 1/5, train_loss: 0.6861\n",
"batch 2/5, train_loss: 0.6139\n",
"batch 3/5, train_loss: 0.6567\n",
"batch 4/5, train_loss: 0.5885\n",
"batch 5/5, train_loss: 0.6082\n",
"epoch 93 average loss: 0.6307\n",
"----------\n",
"epoch 94/200\n",
"batch 1/5, train_loss: 0.6917\n",
"batch 2/5, train_loss: 0.6724\n",
"batch 3/5, train_loss: 0.5952\n",
"batch 4/5, train_loss: 0.6314\n",
"batch 5/5, train_loss: 0.6130\n",
"epoch 94 average loss: 0.6407\n",
"current epoch: 94 current mean recall per class: 0, 0.3639, 0.249, 0.2323, 0.39, 0.476, 0.3637\n",
"best mean recall: 0.3157 at epoch: 90\n",
"----------\n",
"epoch 95/200\n",
"batch 1/5, train_loss: 0.6251\n",
"batch 2/5, train_loss: 0.6412\n",
"batch 3/5, train_loss: 0.6277\n",
"batch 4/5, train_loss: 0.6337\n",
"batch 5/5, train_loss: 0.5918\n",
"epoch 95 average loss: 0.6239\n",
"----------\n",
"epoch 96/200\n",
"batch 1/5, train_loss: 0.6270\n",
"batch 2/5, train_loss: 0.6076\n",
"batch 3/5, train_loss: 0.6356\n",
"batch 4/5, train_loss: 0.6911\n",
"batch 5/5, train_loss: 0.6762\n",
"epoch 96 average loss: 0.6475\n",
"current epoch: 96 current mean recall per class: 0, 0.2094, 0.3064, 0.2475, 0.5958, 0.4154, 0.4203\n",
"best mean recall: 0.3157 at epoch: 90\n",
"----------\n",
"epoch 97/200\n",
"batch 1/5, train_loss: 0.5998\n",
"batch 2/5, train_loss: 0.6135\n",
"batch 3/5, train_loss: 0.7041\n",
"batch 4/5, train_loss: 0.5868\n",
"batch 5/5, train_loss: 0.6470\n",
"epoch 97 average loss: 0.6302\n",
"----------\n",
"epoch 98/200\n",
"batch 1/5, train_loss: 0.6627\n",
"batch 2/5, train_loss: 0.6073\n",
"batch 3/5, train_loss: 0.6108\n",
"batch 4/5, train_loss: 0.6607\n",
"batch 5/5, train_loss: 0.6145\n",
"epoch 98 average loss: 0.6312\n",
"current epoch: 98 current mean recall per class: 0, 0.3278, 0.3262, 0.3325, 0.456, 0.3861, 0.3221\n",
"best mean recall: 0.3157 at epoch: 90\n",
"----------\n",
"epoch 99/200\n",
"batch 1/5, train_loss: 0.6212\n",
"batch 2/5, train_loss: 0.6678\n",
"batch 3/5, train_loss: 0.6529\n",
"batch 4/5, train_loss: 0.6485\n",
"batch 5/5, train_loss: 0.6432\n",
"epoch 99 average loss: 0.6467\n",
"----------\n",
"epoch 100/200\n",
"batch 1/5, train_loss: 0.6696\n",
"batch 2/5, train_loss: 0.6789\n",
"batch 3/5, train_loss: 0.6216\n",
"batch 4/5, train_loss: 0.6029\n",
"batch 5/5, train_loss: 0.5742\n",
"epoch 100 average loss: 0.6294\n",
"current epoch: 100 current mean recall per class: 0, 0.3709, 0.3502, 0.2583, 0.3975, 0.445, 0.3263\n",
"best mean recall: 0.3157 at epoch: 90\n",
"----------\n",
"epoch 101/200\n",
"batch 1/5, train_loss: 0.6269\n",
"batch 2/5, train_loss: 0.6499\n",
"batch 3/5, train_loss: 0.6299\n",
"batch 4/5, train_loss: 0.6339\n",
"batch 5/5, train_loss: 0.6789\n",
"epoch 101 average loss: 0.6439\n",
"----------\n",
"epoch 102/200\n",
"batch 1/5, train_loss: 0.6065\n",
"batch 2/5, train_loss: 0.5879\n",
"batch 3/5, train_loss: 0.6648\n",
"batch 4/5, train_loss: 0.6112\n",
"batch 5/5, train_loss: 0.6582\n",
"epoch 102 average loss: 0.6257\n",
"current epoch: 102 current mean recall per class: 0, 0.336, 0.3066, 0.2584, 0.5292, 0.388, 0.3661\n",
"best mean recall: 0.3157 at epoch: 90\n",
"----------\n",
"epoch 103/200\n",
"batch 1/5, train_loss: 0.6653\n",
"batch 2/5, train_loss: 0.5915\n",
"batch 3/5, train_loss: 0.6374\n",
"batch 4/5, train_loss: 0.5875\n",
"batch 5/5, train_loss: 0.6324\n",
"epoch 103 average loss: 0.6228\n",
"----------\n",
"epoch 104/200\n",
"batch 1/5, train_loss: 0.6420\n",
"batch 2/5, train_loss: 0.6348\n",
"batch 3/5, train_loss: 0.6234\n",
"batch 4/5, train_loss: 0.5786\n",
"batch 5/5, train_loss: 0.6596\n",
"epoch 104 average loss: 0.6277\n",
"current epoch: 104 current mean recall per class: 0, 0.3601, 0.2541, 0.2493, 0.398, 0.351, 0.3607\n",
"best mean recall: 0.3157 at epoch: 90\n",
"----------\n",
"epoch 105/200\n",
"batch 1/5, train_loss: 0.5962\n",
"batch 2/5, train_loss: 0.6750\n",
"batch 3/5, train_loss: 0.6078\n",
"batch 4/5, train_loss: 0.6660\n",
"batch 5/5, train_loss: 0.6280\n",
"epoch 105 average loss: 0.6346\n",
"----------\n",
"epoch 106/200\n",
"batch 1/5, train_loss: 0.6806\n",
"batch 2/5, train_loss: 0.6059\n",
"batch 3/5, train_loss: 0.6637\n",
"batch 4/5, train_loss: 0.6011\n",
"batch 5/5, train_loss: 0.6062\n",
"epoch 106 average loss: 0.6315\n",
"saved new best metric model\n",
"current epoch: 106 current mean recall per class: 0, 0.3328, 0.2817, 0.258, 0.5415, 0.3886, 0.5459\n",
"best mean recall: 0.3355 at epoch: 106\n",
"----------\n",
"epoch 107/200\n",
"batch 1/5, train_loss: 0.6111\n",
"batch 2/5, train_loss: 0.5778\n",
"batch 3/5, train_loss: 0.6731\n",
"batch 4/5, train_loss: 0.5984\n",
"batch 5/5, train_loss: 0.6627\n",
"epoch 107 average loss: 0.6246\n",
"----------\n",
"epoch 108/200\n",
"batch 1/5, train_loss: 0.6037\n",
"batch 2/5, train_loss: 0.6593\n",
"batch 3/5, train_loss: 0.6464\n",
"batch 4/5, train_loss: 0.6711\n",
"batch 5/5, train_loss: 0.5811\n",
"epoch 108 average loss: 0.6323\n",
"current epoch: 108 current mean recall per class: 0, 0.2945, 0.302, 0.3039, 0.5016, 0.4251, 0.118\n",
"best mean recall: 0.3355 at epoch: 106\n",
"----------\n",
"epoch 109/200\n",
"batch 1/5, train_loss: 0.6082\n",
"batch 2/5, train_loss: 0.6604\n",
"batch 3/5, train_loss: 0.5660\n",
"batch 4/5, train_loss: 0.6556\n",
"batch 5/5, train_loss: 0.6184\n",
"epoch 109 average loss: 0.6217\n",
"----------\n",
"epoch 110/200\n",
"batch 1/5, train_loss: 0.6145\n",
"batch 2/5, train_loss: 0.6269\n",
"batch 3/5, train_loss: 0.6463\n",
"batch 4/5, train_loss: 0.6020\n",
"batch 5/5, train_loss: 0.6604\n",
"epoch 110 average loss: 0.6300\n",
"current epoch: 110 current mean recall per class: 0, 0.4078, 0.2926, 0.2217, 0.4972, 0.4222, 0.2669\n",
"best mean recall: 0.3355 at epoch: 106\n",
"----------\n",
"epoch 111/200\n",
"batch 1/5, train_loss: 0.5758\n",
"batch 2/5, train_loss: 0.6017\n",
"batch 3/5, train_loss: 0.6434\n",
"batch 4/5, train_loss: 0.6160\n",
"batch 5/5, train_loss: 0.6747\n",
"epoch 111 average loss: 0.6223\n",
"----------\n",
"epoch 112/200\n",
"batch 1/5, train_loss: 0.5856\n",
"batch 2/5, train_loss: 0.5914\n",
"batch 3/5, train_loss: 0.5775\n",
"batch 4/5, train_loss: 0.6777\n",
"batch 5/5, train_loss: 0.6364\n",
"epoch 112 average loss: 0.6137\n",
"current epoch: 112 current mean recall per class: 0, 0.3357, 0.3223, 0.2835, 0.513, 0.3687, 0.298\n",
"best mean recall: 0.3355 at epoch: 106\n",
"----------\n",
"epoch 113/200\n",
"batch 1/5, train_loss: 0.6374\n",
"batch 2/5, train_loss: 0.6193\n",
"batch 3/5, train_loss: 0.6183\n",
"batch 4/5, train_loss: 0.5985\n",
"batch 5/5, train_loss: 0.5920\n",
"epoch 113 average loss: 0.6131\n",
"----------\n",
"epoch 114/200\n",
"batch 1/5, train_loss: 0.6529\n",
"batch 2/5, train_loss: 0.5846\n",
"batch 3/5, train_loss: 0.6571\n",
"batch 4/5, train_loss: 0.6388\n",
"batch 5/5, train_loss: 0.5986\n",
"epoch 114 average loss: 0.6264\n",
"current epoch: 114 current mean recall per class: 0, 0.3385, 0.3388, 0.2084, 0.3526, 0.4251, 0.2709\n",
"best mean recall: 0.3355 at epoch: 106\n",
"----------\n",
"epoch 115/200\n",
"batch 1/5, train_loss: 0.6582\n",
"batch 2/5, train_loss: 0.6372\n",
"batch 3/5, train_loss: 0.6328\n",
"batch 4/5, train_loss: 0.6257\n",
"batch 5/5, train_loss: 0.6125\n",
"epoch 115 average loss: 0.6333\n",
"----------\n",
"epoch 116/200\n",
"batch 1/5, train_loss: 0.6124\n",
"batch 2/5, train_loss: 0.5965\n",
"batch 3/5, train_loss: 0.6510\n",
"batch 4/5, train_loss: 0.6500\n",
"batch 5/5, train_loss: 0.5429\n",
"epoch 116 average loss: 0.6106\n",
"saved new best metric model\n",
"current epoch: 116 current mean recall per class: 0, 0.3716, 0.3465, 0.219, 0.5732, 0.4681, 0.4108\n",
"best mean recall: 0.3413 at epoch: 116\n",
"----------\n",
"epoch 117/200\n",
"batch 1/5, train_loss: 0.6442\n",
"batch 2/5, train_loss: 0.6095\n",
"batch 3/5, train_loss: 0.6336\n",
"batch 4/5, train_loss: 0.5780\n",
"batch 5/5, train_loss: 0.6177\n",
"epoch 117 average loss: 0.6166\n",
"----------\n",
"epoch 118/200\n",
"batch 1/5, train_loss: 0.5833\n",
"batch 2/5, train_loss: 0.6374\n",
"batch 3/5, train_loss: 0.6225\n",
"batch 4/5, train_loss: 0.6523\n",
"batch 5/5, train_loss: 0.6202\n",
"epoch 118 average loss: 0.6231\n",
"saved new best metric model\n",
"current epoch: 118 current mean recall per class: 0, 0.3192, 0.3113, 0.3163, 0.5694, 0.4986, 0.4555\n",
"best mean recall: 0.3529 at epoch: 118\n",
"----------\n",
"epoch 119/200\n",
"batch 1/5, train_loss: 0.6494\n",
"batch 2/5, train_loss: 0.6184\n",
"batch 3/5, train_loss: 0.6406\n",
"batch 4/5, train_loss: 0.5873\n",
"batch 5/5, train_loss: 0.6684\n",
"epoch 119 average loss: 0.6328\n",
"----------\n",
"epoch 120/200\n",
"batch 1/5, train_loss: 0.6233\n",
"batch 2/5, train_loss: 0.6198\n",
"batch 3/5, train_loss: 0.6495\n",
"batch 4/5, train_loss: 0.6166\n",
"batch 5/5, train_loss: 0.6383\n",
"epoch 120 average loss: 0.6295\n",
"current epoch: 120 current mean recall per class: 0, 0.3419, 0.2946, 0.2272, 0.5694, 0.4486, 0.4487\n",
"best mean recall: 0.3529 at epoch: 118\n",
"----------\n",
"epoch 121/200\n",
"batch 1/5, train_loss: 0.5958\n",
"batch 2/5, train_loss: 0.6351\n",
"batch 3/5, train_loss: 0.6260\n",
"batch 4/5, train_loss: 0.6505\n",
"batch 5/5, train_loss: 0.5753\n",
"epoch 121 average loss: 0.6166\n",
"----------\n",
"epoch 122/200\n",
"batch 1/5, train_loss: 0.6614\n",
"batch 2/5, train_loss: 0.5991\n",
"batch 3/5, train_loss: 0.6044\n",
"batch 4/5, train_loss: 0.5881\n",
"batch 5/5, train_loss: 0.6483\n",
"epoch 122 average loss: 0.6203\n",
"current epoch: 122 current mean recall per class: 0, 0.3518, 0.3312, 0.2941, 0.4974, 0.3931, 0.3866\n",
"best mean recall: 0.3529 at epoch: 118\n",
"----------\n",
"epoch 123/200\n",
"batch 1/5, train_loss: 0.6607\n",
"batch 2/5, train_loss: 0.6498\n",
"batch 3/5, train_loss: 0.6736\n",
"batch 4/5, train_loss: 0.6030\n",
"batch 5/5, train_loss: 0.6059\n",
"epoch 123 average loss: 0.6386\n",
"----------\n",
"epoch 124/200\n",
"batch 1/5, train_loss: 0.6270\n",
"batch 2/5, train_loss: 0.6372\n",
"batch 3/5, train_loss: 0.6149\n",
"batch 4/5, train_loss: 0.6501\n",
"batch 5/5, train_loss: 0.5466\n",
"epoch 124 average loss: 0.6152\n",
"current epoch: 124 current mean recall per class: 0, 0.4062, 0.324, 0.2323, 0.6421, 0.4249, 0.3341\n",
"best mean recall: 0.3529 at epoch: 118\n",
"----------\n",
"epoch 125/200\n",
"batch 1/5, train_loss: 0.6045\n",
"batch 2/5, train_loss: 0.6575\n",
"batch 3/5, train_loss: 0.6341\n",
"batch 4/5, train_loss: 0.5958\n",
"batch 5/5, train_loss: 0.5896\n",
"epoch 125 average loss: 0.6163\n",
"----------\n",
"epoch 126/200\n",
"batch 1/5, train_loss: 0.6088\n",
"batch 2/5, train_loss: 0.6095\n",
"batch 3/5, train_loss: 0.6239\n",
"batch 4/5, train_loss: 0.6348\n",
"batch 5/5, train_loss: 0.6877\n",
"epoch 126 average loss: 0.6329\n",
"current epoch: 126 current mean recall per class: 0, 0.4057, 0.3082, 0.2261, 0.5045, 0.4756, 0.3705\n",
"best mean recall: 0.3529 at epoch: 118\n",
"----------\n",
"epoch 127/200\n",
"batch 1/5, train_loss: 0.6751\n",
"batch 2/5, train_loss: 0.6662\n",
"batch 3/5, train_loss: 0.5867\n",
"batch 4/5, train_loss: 0.6122\n",
"batch 5/5, train_loss: 0.5718\n",
"epoch 127 average loss: 0.6224\n",
"----------\n",
"epoch 128/200\n",
"batch 1/5, train_loss: 0.5848\n",
"batch 2/5, train_loss: 0.6226\n",
"batch 3/5, train_loss: 0.6579\n",
"batch 4/5, train_loss: 0.5885\n",
"batch 5/5, train_loss: 0.6194\n",
"epoch 128 average loss: 0.6146\n",
"current epoch: 128 current mean recall per class: 0, 0.3855, 0.3065, 0.264, 0.4208, 0.3141, 0.29\n",
"best mean recall: 0.3529 at epoch: 118\n",
"----------\n",
"epoch 129/200\n",
"batch 1/5, train_loss: 0.5819\n",
"batch 2/5, train_loss: 0.6279\n",
"batch 3/5, train_loss: 0.6494\n",
"batch 4/5, train_loss: 0.6561\n",
"batch 5/5, train_loss: 0.5927\n",
"epoch 129 average loss: 0.6216\n",
"----------\n",
"epoch 130/200\n",
"batch 1/5, train_loss: 0.6214\n",
"batch 2/5, train_loss: 0.6183\n",
"batch 3/5, train_loss: 0.6884\n",
"batch 4/5, train_loss: 0.6411\n",
"batch 5/5, train_loss: 0.5760\n",
"epoch 130 average loss: 0.6290\n",
"saved new best metric model\n",
"current epoch: 130 current mean recall per class: 8.131e-06, 0.3998, 0.2964, 0.2903, 0.5448, 0.5067, 0.4806\n",
"best mean recall: 0.3598 at epoch: 130\n",
"----------\n",
"epoch 131/200\n",
"batch 1/5, train_loss: 0.6310\n",
"batch 2/5, train_loss: 0.6324\n",
"batch 3/5, train_loss: 0.5805\n",
"batch 4/5, train_loss: 0.5952\n",
"batch 5/5, train_loss: 0.6323\n",
"epoch 131 average loss: 0.6143\n",
"----------\n",
"epoch 132/200\n",
"batch 1/5, train_loss: 0.5985\n",
"batch 2/5, train_loss: 0.6489\n",
"batch 3/5, train_loss: 0.6487\n",
"batch 4/5, train_loss: 0.5909\n",
"batch 5/5, train_loss: 0.6079\n",
"epoch 132 average loss: 0.6190\n",
"current epoch: 132 current mean recall per class: 0, 0.3311, 0.3414, 0.323, 0.5439, 0.4248, 0.4232\n",
"best mean recall: 0.3598 at epoch: 130\n",
"----------\n",
"epoch 133/200\n",
"batch 1/5, train_loss: 0.6928\n",
"batch 2/5, train_loss: 0.6366\n",
"batch 3/5, train_loss: 0.6128\n",
"batch 4/5, train_loss: 0.6286\n",
"batch 5/5, train_loss: 0.6071\n",
"epoch 133 average loss: 0.6356\n",
"----------\n",
"epoch 134/200\n",
"batch 1/5, train_loss: 0.6230\n",
"batch 2/5, train_loss: 0.6645\n",
"batch 3/5, train_loss: 0.6203\n",
"batch 4/5, train_loss: 0.6043\n",
"batch 5/5, train_loss: 0.5903\n",
"epoch 134 average loss: 0.6204\n",
"current epoch: 134 current mean recall per class: 0, 0.3962, 0.3395, 0.2725, 0.3838, 0.4432, 0.3746\n",
"best mean recall: 0.3598 at epoch: 130\n",
"----------\n",
"epoch 135/200\n",
"batch 1/5, train_loss: 0.5880\n",
"batch 2/5, train_loss: 0.6623\n",
"batch 3/5, train_loss: 0.5724\n",
"batch 4/5, train_loss: 0.5813\n",
"batch 5/5, train_loss: 0.6726\n",
"epoch 135 average loss: 0.6153\n",
"----------\n",
"epoch 136/200\n",
"batch 1/5, train_loss: 0.6280\n",
"batch 2/5, train_loss: 0.5910\n",
"batch 3/5, train_loss: 0.5756\n",
"batch 4/5, train_loss: 0.6623\n",
"batch 5/5, train_loss: 0.5713\n",
"epoch 136 average loss: 0.6056\n",
"saved new best metric model\n",
"current epoch: 136 current mean recall per class: 0, 0.4146, 0.3255, 0.3313, 0.4184, 0.4983, 0.5514\n",
"best mean recall: 0.3628 at epoch: 136\n",
"----------\n",
"epoch 137/200\n",
"batch 1/5, train_loss: 0.5857\n",
"batch 2/5, train_loss: 0.6865\n",
"batch 3/5, train_loss: 0.5867\n",
"batch 4/5, train_loss: 0.6536\n",
"batch 5/5, train_loss: 0.6040\n",
"epoch 137 average loss: 0.6233\n",
"----------\n",
"epoch 138/200\n",
"batch 1/5, train_loss: 0.6463\n",
"batch 2/5, train_loss: 0.6007\n",
"batch 3/5, train_loss: 0.6735\n",
"batch 4/5, train_loss: 0.5835\n",
"batch 5/5, train_loss: 0.5911\n",
"epoch 138 average loss: 0.6190\n",
"current epoch: 138 current mean recall per class: 0, 0.4428, 0.3236, 0.2985, 0.5312, 0.4273, 0.3741\n",
"best mean recall: 0.3628 at epoch: 136\n",
"----------\n",
"epoch 139/200\n",
"batch 1/5, train_loss: 0.5943\n",
"batch 2/5, train_loss: 0.5748\n",
"batch 3/5, train_loss: 0.5970\n",
"batch 4/5, train_loss: 0.6175\n",
"batch 5/5, train_loss: 0.6657\n",
"epoch 139 average loss: 0.6099\n",
"----------\n",
"epoch 140/200\n",
"batch 1/5, train_loss: 0.6546\n",
"batch 2/5, train_loss: 0.5815\n",
"batch 3/5, train_loss: 0.6015\n",
"batch 4/5, train_loss: 0.6472\n",
"batch 5/5, train_loss: 0.5934\n",
"epoch 140 average loss: 0.6156\n",
"current epoch: 140 current mean recall per class: 0, 0.4253, 0.3535, 0.314, 0.3549, 0.3589, 0.4672\n",
"best mean recall: 0.3628 at epoch: 136\n",
"----------\n",
"epoch 141/200\n",
"batch 1/5, train_loss: 0.6594\n",
"batch 2/5, train_loss: 0.5875\n",
"batch 3/5, train_loss: 0.6057\n",
"batch 4/5, train_loss: 0.5878\n",
"batch 5/5, train_loss: 0.6473\n",
"epoch 141 average loss: 0.6175\n",
"----------\n",
"epoch 142/200\n",
"batch 1/5, train_loss: 0.6042\n",
"batch 2/5, train_loss: 0.6049\n",
"batch 3/5, train_loss: 0.6275\n",
"batch 4/5, train_loss: 0.6137\n",
"batch 5/5, train_loss: 0.5995\n",
"epoch 142 average loss: 0.6100\n",
"current epoch: 142 current mean recall per class: 0, 0.4303, 0.3198, 0.2795, 0.5028, 0.3904, 0.4839\n",
"best mean recall: 0.3628 at epoch: 136\n",
"----------\n",
"epoch 143/200\n",
"batch 1/5, train_loss: 0.6518\n",
"batch 2/5, train_loss: 0.5939\n",
"batch 3/5, train_loss: 0.6114\n",
"batch 4/5, train_loss: 0.5561\n",
"batch 5/5, train_loss: 0.6211\n",
"epoch 143 average loss: 0.6069\n",
"----------\n",
"epoch 144/200\n",
"batch 1/5, train_loss: 0.6058\n",
"batch 2/5, train_loss: 0.5960\n",
"batch 3/5, train_loss: 0.6395\n",
"batch 4/5, train_loss: 0.6367\n",
"batch 5/5, train_loss: 0.5322\n",
"epoch 144 average loss: 0.6020\n",
"current epoch: 144 current mean recall per class: 0, 0.3671, 0.3325, 0.2734, 0.5621, 0.5197, 0.3205\n",
"best mean recall: 0.3628 at epoch: 136\n",
"----------\n",
"epoch 145/200\n",
"batch 1/5, train_loss: 0.6801\n",
"batch 2/5, train_loss: 0.5586\n",
"batch 3/5, train_loss: 0.5807\n",
"batch 4/5, train_loss: 0.6072\n",
"batch 5/5, train_loss: 0.5915\n",
"epoch 145 average loss: 0.6036\n",
"----------\n",
"epoch 146/200\n",
"batch 1/5, train_loss: 0.5416\n",
"batch 2/5, train_loss: 0.5950\n",
"batch 3/5, train_loss: 0.6639\n",
"batch 4/5, train_loss: 0.6853\n",
"batch 5/5, train_loss: 0.5783\n",
"epoch 146 average loss: 0.6128\n",
"current epoch: 146 current mean recall per class: 0, 0.3385, 0.3149, 0.2878, 0.508, 0.5016, 0.2995\n",
"best mean recall: 0.3628 at epoch: 136\n",
"----------\n",
"epoch 147/200\n",
"batch 1/5, train_loss: 0.6078\n",
"batch 2/5, train_loss: 0.6486\n",
"batch 3/5, train_loss: 0.6112\n",
"batch 4/5, train_loss: 0.5784\n",
"batch 5/5, train_loss: 0.6473\n",
"epoch 147 average loss: 0.6187\n",
"----------\n",
"epoch 148/200\n",
"batch 1/5, train_loss: 0.6642\n",
"batch 2/5, train_loss: 0.5770\n",
"batch 3/5, train_loss: 0.6223\n",
"batch 4/5, train_loss: 0.5984\n",
"batch 5/5, train_loss: 0.5782\n",
"epoch 148 average loss: 0.6080\n",
"current epoch: 148 current mean recall per class: 0, 0.4215, 0.3018, 0.2686, 0.5095, 0.4629, 0.4036\n",
"best mean recall: 0.3628 at epoch: 136\n",
"----------\n",
"epoch 149/200\n",
"batch 1/5, train_loss: 0.5832\n",
"batch 2/5, train_loss: 0.5774\n",
"batch 3/5, train_loss: 0.5862\n",
"batch 4/5, train_loss: 0.6813\n",
"batch 5/5, train_loss: 0.6456\n",
"epoch 149 average loss: 0.6147\n",
"----------\n",
"epoch 150/200\n",
"batch 1/5, train_loss: 0.6587\n",
"batch 2/5, train_loss: 0.6224\n",
"batch 3/5, train_loss: 0.6289\n",
"batch 4/5, train_loss: 0.5961\n",
"batch 5/5, train_loss: 0.6169\n",
"epoch 150 average loss: 0.6246\n",
"current epoch: 150 current mean recall per class: 4.432e-06, 0.3687, 0.3699, 0.3122, 0.6161, 0.533, 0.3091\n",
"best mean recall: 0.3628 at epoch: 136\n",
"----------\n",
"epoch 151/200\n",
"batch 1/5, train_loss: 0.6426\n",
"batch 2/5, train_loss: 0.5962\n",
"batch 3/5, train_loss: 0.6132\n",
"batch 4/5, train_loss: 0.5534\n",
"batch 5/5, train_loss: 0.6745\n",
"epoch 151 average loss: 0.6160\n",
"----------\n",
"epoch 152/200\n",
"batch 1/5, train_loss: 0.6050\n",
"batch 2/5, train_loss: 0.6161\n",
"batch 3/5, train_loss: 0.6691\n",
"batch 4/5, train_loss: 0.6251\n",
"batch 5/5, train_loss: 0.5797\n",
"epoch 152 average loss: 0.6190\n",
"current epoch: 152 current mean recall per class: 0, 0.3619, 0.3195, 0.3147, 0.4635, 0.5423, 0.3826\n",
"best mean recall: 0.3628 at epoch: 136\n",
"----------\n",
"epoch 153/200\n",
"batch 1/5, train_loss: 0.6641\n",
"batch 2/5, train_loss: 0.6090\n",
"batch 3/5, train_loss: 0.5949\n",
"batch 4/5, train_loss: 0.5767\n",
"batch 5/5, train_loss: 0.6018\n",
"epoch 153 average loss: 0.6093\n",
"----------\n",
"epoch 154/200\n",
"batch 1/5, train_loss: 0.6460\n",
"batch 2/5, train_loss: 0.5608\n",
"batch 3/5, train_loss: 0.5974\n",
"batch 4/5, train_loss: 0.5829\n",
"batch 5/5, train_loss: 0.6345\n",
"epoch 154 average loss: 0.6043\n",
"current epoch: 154 current mean recall per class: 0, 0.3676, 0.3011, 0.3433, 0.3644, 0.5891, 0.3228\n",
"best mean recall: 0.3628 at epoch: 136\n",
"----------\n",
"epoch 155/200\n",
"batch 1/5, train_loss: 0.5596\n",
"batch 2/5, train_loss: 0.6173\n",
"batch 3/5, train_loss: 0.5704\n",
"batch 4/5, train_loss: 0.5717\n",
"batch 5/5, train_loss: 0.6701\n",
"epoch 155 average loss: 0.5978\n",
"----------\n",
"epoch 156/200\n",
"batch 1/5, train_loss: 0.6042\n",
"batch 2/5, train_loss: 0.6590\n",
"batch 3/5, train_loss: 0.6007\n",
"batch 4/5, train_loss: 0.5792\n",
"batch 5/5, train_loss: 0.5649\n",
"epoch 156 average loss: 0.6016\n",
"saved new best metric model\n",
"current epoch: 156 current mean recall per class: 0, 0.3926, 0.3306, 0.302, 0.3807, 0.6596, 0.4832\n",
"best mean recall: 0.3641 at epoch: 156\n",
"----------\n",
"epoch 157/200\n",
"batch 1/5, train_loss: 0.6200\n",
"batch 2/5, train_loss: 0.6460\n",
"batch 3/5, train_loss: 0.6188\n",
"batch 4/5, train_loss: 0.5883\n",
"batch 5/5, train_loss: 0.5854\n",
"epoch 157 average loss: 0.6117\n",
"----------\n",
"epoch 158/200\n",
"batch 1/5, train_loss: 0.6297\n",
"batch 2/5, train_loss: 0.5530\n",
"batch 3/5, train_loss: 0.6265\n",
"batch 4/5, train_loss: 0.6311\n",
"batch 5/5, train_loss: 0.5933\n",
"epoch 158 average loss: 0.6067\n",
"current epoch: 158 current mean recall per class: 0, 0.3995, 0.3349, 0.3325, 0.4389, 0.5179, 0.4511\n",
"best mean recall: 0.3641 at epoch: 156\n",
"----------\n",
"epoch 159/200\n",
"batch 1/5, train_loss: 0.5851\n",
"batch 2/5, train_loss: 0.6224\n",
"batch 3/5, train_loss: 0.5772\n",
"batch 4/5, train_loss: 0.6567\n",
"batch 5/5, train_loss: 0.5735\n",
"epoch 159 average loss: 0.6030\n",
"----------\n",
"epoch 160/200\n",
"batch 1/5, train_loss: 0.5562\n",
"batch 2/5, train_loss: 0.6274\n",
"batch 3/5, train_loss: 0.5700\n",
"batch 4/5, train_loss: 0.6340\n",
"batch 5/5, train_loss: 0.6048\n",
"epoch 160 average loss: 0.5985\n",
"current epoch: 160 current mean recall per class: 0, 0.4415, 0.3775, 0.2806, 0.5817, 0.4348, 0.3765\n",
"best mean recall: 0.3641 at epoch: 156\n",
"----------\n",
"epoch 161/200\n",
"batch 1/5, train_loss: 0.6231\n",
"batch 2/5, train_loss: 0.5850\n",
"batch 3/5, train_loss: 0.5691\n",
"batch 4/5, train_loss: 0.5944\n",
"batch 5/5, train_loss: 0.6407\n",
"epoch 161 average loss: 0.6025\n",
"----------\n",
"epoch 162/200\n",
"batch 1/5, train_loss: 0.6328\n",
"batch 2/5, train_loss: 0.5837\n",
"batch 3/5, train_loss: 0.5690\n",
"batch 4/5, train_loss: 0.5545\n",
"batch 5/5, train_loss: 0.6557\n",
"epoch 162 average loss: 0.5991\n",
"current epoch: 162 current mean recall per class: 0, 0.3933, 0.3635, 0.2649, 0.4187, 0.4472, 0.3558\n",
"best mean recall: 0.3641 at epoch: 156\n",
"----------\n",
"epoch 163/200\n",
"batch 1/5, train_loss: 0.5783\n",
"batch 2/5, train_loss: 0.6320\n",
"batch 3/5, train_loss: 0.5895\n",
"batch 4/5, train_loss: 0.6368\n",
"batch 5/5, train_loss: 0.6210\n",
"epoch 163 average loss: 0.6115\n",
"----------\n",
"epoch 164/200\n",
"batch 1/5, train_loss: 0.5770\n",
"batch 2/5, train_loss: 0.6283\n",
"batch 3/5, train_loss: 0.5851\n",
"batch 4/5, train_loss: 0.5722\n",
"batch 5/5, train_loss: 0.6527\n",
"epoch 164 average loss: 0.6031\n",
"current epoch: 164 current mean recall per class: 0, 0.3421, 0.3579, 0.258, 0.5276, 0.532, 0.465\n",
"best mean recall: 0.3641 at epoch: 156\n",
"----------\n",
"epoch 165/200\n",
"batch 1/5, train_loss: 0.5867\n",
"batch 2/5, train_loss: 0.6207\n",
"batch 3/5, train_loss: 0.6151\n",
"batch 4/5, train_loss: 0.6370\n",
"batch 5/5, train_loss: 0.6395\n",
"epoch 165 average loss: 0.6198\n",
"----------\n",
"epoch 166/200\n",
"batch 1/5, train_loss: 0.5789\n",
"batch 2/5, train_loss: 0.6042\n",
"batch 3/5, train_loss: 0.6313\n",
"batch 4/5, train_loss: 0.6306\n",
"batch 5/5, train_loss: 0.5781\n",
"epoch 166 average loss: 0.6046\n",
"current epoch: 166 current mean recall per class: 0, 0.3792, 0.3522, 0.2393, 0.5681, 0.5986, 0.3816\n",
"best mean recall: 0.3641 at epoch: 156\n",
"----------\n",
"epoch 167/200\n",
"batch 1/5, train_loss: 0.5698\n",
"batch 2/5, train_loss: 0.5830\n",
"batch 3/5, train_loss: 0.6036\n",
"batch 4/5, train_loss: 0.6404\n",
"batch 5/5, train_loss: 0.5966\n",
"epoch 167 average loss: 0.5987\n",
"----------\n",
"epoch 168/200\n",
"batch 1/5, train_loss: 0.5924\n",
"batch 2/5, train_loss: 0.6421\n",
"batch 3/5, train_loss: 0.5444\n",
"batch 4/5, train_loss: 0.5992\n",
"batch 5/5, train_loss: 0.5475\n",
"epoch 168 average loss: 0.5851\n",
"saved new best metric model\n",
"current epoch: 168 current mean recall per class: 0, 0.427, 0.3024, 0.233, 0.6329, 0.5499, 0.4753\n",
"best mean recall: 0.3744 at epoch: 168\n",
"----------\n",
"epoch 169/200\n",
"batch 1/5, train_loss: 0.5867\n",
"batch 2/5, train_loss: 0.6524\n",
"batch 3/5, train_loss: 0.6045\n",
"batch 4/5, train_loss: 0.6322\n",
"batch 5/5, train_loss: 0.6065\n",
"epoch 169 average loss: 0.6164\n",
"----------\n",
"epoch 170/200\n",
"batch 1/5, train_loss: 0.6437\n",
"batch 2/5, train_loss: 0.5848\n",
"batch 3/5, train_loss: 0.5717\n",
"batch 4/5, train_loss: 0.6288\n",
"batch 5/5, train_loss: 0.5785\n",
"epoch 170 average loss: 0.6015\n",
"current epoch: 170 current mean recall per class: 0, 0.3336, 0.3269, 0.2793, 0.5618, 0.4072, 0.3525\n",
"best mean recall: 0.3744 at epoch: 168\n",
"----------\n",
"epoch 171/200\n",
"batch 1/5, train_loss: 0.6052\n",
"batch 2/5, train_loss: 0.6306\n",
"batch 3/5, train_loss: 0.5914\n",
"batch 4/5, train_loss: 0.5451\n",
"batch 5/5, train_loss: 0.6572\n",
"epoch 171 average loss: 0.6059\n",
"----------\n",
"epoch 172/200\n",
"batch 1/5, train_loss: 0.5886\n",
"batch 2/5, train_loss: 0.5788\n",
"batch 3/5, train_loss: 0.6622\n",
"batch 4/5, train_loss: 0.6086\n",
"batch 5/5, train_loss: 0.6360\n",
"epoch 172 average loss: 0.6148\n",
"current epoch: 172 current mean recall per class: 0, 0.3779, 0.3328, 0.2053, 0.531, 0.5649, 0.3993\n",
"best mean recall: 0.3744 at epoch: 168\n",
"----------\n",
"epoch 173/200\n",
"batch 1/5, train_loss: 0.6201\n",
"batch 2/5, train_loss: 0.5522\n",
"batch 3/5, train_loss: 0.6046\n",
"batch 4/5, train_loss: 0.6497\n",
"batch 5/5, train_loss: 0.6163\n",
"epoch 173 average loss: 0.6086\n",
"----------\n",
"epoch 174/200\n",
"batch 1/5, train_loss: 0.6061\n",
"batch 2/5, train_loss: 0.6449\n",
"batch 3/5, train_loss: 0.5657\n",
"batch 4/5, train_loss: 0.5973\n",
"batch 5/5, train_loss: 0.6584\n",
"epoch 174 average loss: 0.6145\n",
"current epoch: 174 current mean recall per class: 0, 0.4315, 0.3353, 0.2712, 0.4708, 0.4089, 0.2196\n",
"best mean recall: 0.3744 at epoch: 168\n",
"----------\n",
"epoch 175/200\n",
"batch 1/5, train_loss: 0.6143\n",
"batch 2/5, train_loss: 0.6581\n",
"batch 3/5, train_loss: 0.6160\n",
"batch 4/5, train_loss: 0.6123\n",
"batch 5/5, train_loss: 0.6227\n",
"epoch 175 average loss: 0.6247\n",
"----------\n",
"epoch 176/200\n",
"batch 1/5, train_loss: 0.6018\n",
"batch 2/5, train_loss: 0.5947\n",
"batch 3/5, train_loss: 0.6447\n",
"batch 4/5, train_loss: 0.6068\n",
"batch 5/5, train_loss: 0.6058\n",
"epoch 176 average loss: 0.6107\n",
"current epoch: 176 current mean recall per class: 0, 0.4777, 0.3325, 0.2822, 0.4592, 0.4672, 0.3941\n",
"best mean recall: 0.3744 at epoch: 168\n",
"----------\n",
"epoch 177/200\n",
"batch 1/5, train_loss: 0.6033\n",
"batch 2/5, train_loss: 0.6523\n",
"batch 3/5, train_loss: 0.6052\n",
"batch 4/5, train_loss: 0.5900\n",
"batch 5/5, train_loss: 0.5955\n",
"epoch 177 average loss: 0.6092\n",
"----------\n",
"epoch 178/200\n",
"batch 1/5, train_loss: 0.6177\n",
"batch 2/5, train_loss: 0.6132\n",
"batch 3/5, train_loss: 0.5805\n",
"batch 4/5, train_loss: 0.6485\n",
"batch 5/5, train_loss: 0.5888\n",
"epoch 178 average loss: 0.6097\n",
"current epoch: 178 current mean recall per class: 0, 0.3876, 0.3191, 0.3565, 0.5149, 0.5141, 0.4546\n",
"best mean recall: 0.3744 at epoch: 168\n",
"----------\n",
"epoch 179/200\n",
"batch 1/5, train_loss: 0.6277\n",
"batch 2/5, train_loss: 0.5374\n",
"batch 3/5, train_loss: 0.6001\n",
"batch 4/5, train_loss: 0.6288\n",
"batch 5/5, train_loss: 0.6445\n",
"epoch 179 average loss: 0.6077\n",
"----------\n",
"epoch 180/200\n",
"batch 1/5, train_loss: 0.6232\n",
"batch 2/5, train_loss: 0.6412\n",
"batch 3/5, train_loss: 0.6221\n",
"batch 4/5, train_loss: 0.6102\n",
"batch 5/5, train_loss: 0.6477\n",
"epoch 180 average loss: 0.6289\n",
"current epoch: 180 current mean recall per class: 0, 0.4039, 0.3208, 0.28, 0.3763, 0.4919, 0.3705\n",
"best mean recall: 0.3744 at epoch: 168\n",
"----------\n",
"epoch 181/200\n",
"batch 1/5, train_loss: 0.6056\n",
"batch 2/5, train_loss: 0.6553\n",
"batch 3/5, train_loss: 0.5901\n",
"batch 4/5, train_loss: 0.5650\n",
"batch 5/5, train_loss: 0.6370\n",
"epoch 181 average loss: 0.6106\n",
"----------\n",
"epoch 182/200\n",
"batch 1/5, train_loss: 0.5693\n",
"batch 2/5, train_loss: 0.5649\n",
"batch 3/5, train_loss: 0.5731\n",
"batch 4/5, train_loss: 0.6380\n",
"batch 5/5, train_loss: 0.5806\n",
"epoch 182 average loss: 0.5852\n",
"current epoch: 182 current mean recall per class: 0, 0.3878, 0.3308, 0.2914, 0.3586, 0.5259, 0.352\n",
"best mean recall: 0.3744 at epoch: 168\n",
"----------\n",
"epoch 183/200\n",
"batch 1/5, train_loss: 0.5795\n",
"batch 2/5, train_loss: 0.5880\n",
"batch 3/5, train_loss: 0.6492\n",
"batch 4/5, train_loss: 0.6607\n",
"batch 5/5, train_loss: 0.5500\n",
"epoch 183 average loss: 0.6055\n",
"----------\n",
"epoch 184/200\n",
"batch 1/5, train_loss: 0.6151\n",
"batch 2/5, train_loss: 0.5818\n",
"batch 3/5, train_loss: 0.6768\n",
"batch 4/5, train_loss: 0.5167\n",
"batch 5/5, train_loss: 0.6418\n",
"epoch 184 average loss: 0.6064\n",
"current epoch: 184 current mean recall per class: 0, 0.4211, 0.2991, 0.2771, 0.4521, 0.4612, 0.4351\n",
"best mean recall: 0.3744 at epoch: 168\n",
"----------\n",
"epoch 185/200\n",
"batch 1/5, train_loss: 0.5974\n",
"batch 2/5, train_loss: 0.6349\n",
"batch 3/5, train_loss: 0.6424\n",
"batch 4/5, train_loss: 0.5781\n",
"batch 5/5, train_loss: 0.6494\n",
"epoch 185 average loss: 0.6204\n",
"----------\n",
"epoch 186/200\n",
"batch 1/5, train_loss: 0.6391\n",
"batch 2/5, train_loss: 0.5997\n",
"batch 3/5, train_loss: 0.5414\n",
"batch 4/5, train_loss: 0.6172\n",
"batch 5/5, train_loss: 0.5833\n",
"epoch 186 average loss: 0.5961\n",
"current epoch: 186 current mean recall per class: 0, 0.4647, 0.3811, 0.1821, 0.4514, 0.496, 0.4758\n",
"best mean recall: 0.3744 at epoch: 168\n",
"----------\n",
"epoch 187/200\n",
"batch 1/5, train_loss: 0.5996\n",
"batch 2/5, train_loss: 0.6608\n",
"batch 3/5, train_loss: 0.6084\n",
"batch 4/5, train_loss: 0.5887\n",
"batch 5/5, train_loss: 0.6398\n",
"epoch 187 average loss: 0.6195\n",
"----------\n",
"epoch 188/200\n",
"batch 1/5, train_loss: 0.5597\n",
"batch 2/5, train_loss: 0.5837\n",
"batch 3/5, train_loss: 0.5583\n",
"batch 4/5, train_loss: 0.6334\n",
"batch 5/5, train_loss: 0.6209\n",
"epoch 188 average loss: 0.5912\n",
"current epoch: 188 current mean recall per class: 0, 0.4213, 0.3308, 0.2326, 0.4255, 0.5068, 0.3163\n",
"best mean recall: 0.3744 at epoch: 168\n",
"----------\n",
"epoch 189/200\n",
"batch 1/5, train_loss: 0.6014\n",
"batch 2/5, train_loss: 0.6320\n",
"batch 3/5, train_loss: 0.6638\n",
"batch 4/5, train_loss: 0.5570\n",
"batch 5/5, train_loss: 0.5853\n",
"epoch 189 average loss: 0.6079\n",
"----------\n",
"epoch 190/200\n",
"batch 1/5, train_loss: 0.6464\n",
"batch 2/5, train_loss: 0.6175\n",
"batch 3/5, train_loss: 0.5603\n",
"batch 4/5, train_loss: 0.5754\n",
"batch 5/5, train_loss: 0.6328\n",
"epoch 190 average loss: 0.6065\n",
"current epoch: 190 current mean recall per class: 0, 0.383, 0.3641, 0.3043, 0.5037, 0.4928, 0.4459\n",
"best mean recall: 0.3744 at epoch: 168\n",
"----------\n",
"epoch 191/200\n",
"batch 1/5, train_loss: 0.6296\n",
"batch 2/5, train_loss: 0.6027\n",
"batch 3/5, train_loss: 0.6276\n",
"batch 4/5, train_loss: 0.5800\n",
"batch 5/5, train_loss: 0.6387\n",
"epoch 191 average loss: 0.6157\n",
"----------\n",
"epoch 192/200\n",
"batch 1/5, train_loss: 0.6474\n",
"batch 2/5, train_loss: 0.6333\n",
"batch 3/5, train_loss: 0.6195\n",
"batch 4/5, train_loss: 0.5779\n",
"batch 5/5, train_loss: 0.5779\n",
"epoch 192 average loss: 0.6112\n",
"saved new best metric model\n",
"current epoch: 192 current mean recall per class: 0, 0.4241, 0.3459, 0.3157, 0.5804, 0.5758, 0.3976\n",
"best mean recall: 0.3771 at epoch: 192\n",
"----------\n",
"epoch 193/200\n",
"batch 1/5, train_loss: 0.5451\n",
"batch 2/5, train_loss: 0.5879\n",
"batch 3/5, train_loss: 0.6029\n",
"batch 4/5, train_loss: 0.5754\n",
"batch 5/5, train_loss: 0.6054\n",
"epoch 193 average loss: 0.5833\n",
"----------\n",
"epoch 194/200\n",
"batch 1/5, train_loss: 0.5611\n",
"batch 2/5, train_loss: 0.5669\n",
"batch 3/5, train_loss: 0.6597\n",
"batch 4/5, train_loss: 0.6281\n",
"batch 5/5, train_loss: 0.5840\n",
"epoch 194 average loss: 0.6000\n",
"current epoch: 194 current mean recall per class: 0, 0.4343, 0.3403, 0.3411, 0.5967, 0.4002, 0.3217\n",
"best mean recall: 0.3771 at epoch: 192\n",
"----------\n",
"epoch 195/200\n",
"batch 1/5, train_loss: 0.5989\n",
"batch 2/5, train_loss: 0.6013\n",
"batch 3/5, train_loss: 0.5603\n",
"batch 4/5, train_loss: 0.5965\n",
"batch 5/5, train_loss: 0.6466\n",
"epoch 195 average loss: 0.6007\n",
"----------\n",
"epoch 196/200\n",
"batch 1/5, train_loss: 0.6294\n",
"batch 2/5, train_loss: 0.5948\n",
"batch 3/5, train_loss: 0.5625\n",
"batch 4/5, train_loss: 0.5832\n",
"batch 5/5, train_loss: 0.5586\n",
"epoch 196 average loss: 0.5857\n",
"current epoch: 196 current mean recall per class: 1.326e-05, 0.5234, 0.3215, 0.367, 0.5597, 0.4415, 0.3703\n",
"best mean recall: 0.3771 at epoch: 192\n",
"----------\n",
"epoch 197/200\n",
"batch 1/5, train_loss: 0.5646\n",
"batch 2/5, train_loss: 0.5963\n",
"batch 3/5, train_loss: 0.6608\n",
"batch 4/5, train_loss: 0.6053\n",
"batch 5/5, train_loss: 0.5719\n",
"epoch 197 average loss: 0.5998\n",
"----------\n",
"epoch 198/200\n",
"batch 1/5, train_loss: 0.5583\n",
"batch 2/5, train_loss: 0.5586\n",
"batch 3/5, train_loss: 0.6236\n",
"batch 4/5, train_loss: 0.5782\n",
"batch 5/5, train_loss: 0.6139\n",
"epoch 198 average loss: 0.5865\n",
"current epoch: 198 current mean recall per class: 0, 0.402, 0.3045, 0.31, 0.5168, 0.4688, 0.3588\n",
"best mean recall: 0.3771 at epoch: 192\n",
"----------\n",
"epoch 199/200\n",
"batch 1/5, train_loss: 0.6193\n",
"batch 2/5, train_loss: 0.5808\n",
"batch 3/5, train_loss: 0.5365\n",
"batch 4/5, train_loss: 0.5934\n",
"batch 5/5, train_loss: 0.6445\n",
"epoch 199 average loss: 0.5949\n",
"----------\n",
"epoch 200/200\n",
"batch 1/5, train_loss: 0.6020\n",
"batch 2/5, train_loss: 0.5568\n",
"batch 3/5, train_loss: 0.6023\n",
"batch 4/5, train_loss: 0.6117\n",
"batch 5/5, train_loss: 0.6146\n",
"epoch 200 average loss: 0.5975\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024/10/07 20:07:12 WARNING mlflow.utils.requirements_utils: Found torch version (2.4.0+cu124) contains a local version label (+cu124). MLflow logged a pip requirement for this package as 'torch==2.4.0' without the local version label to make it installable from PyPI. To specify pip requirements containing local version labels, please use `conda_env` or `pip_requirements`.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"current epoch: 200 current mean recall per class: 0, 0.3304, 0.3373, 0.3223, 0.4577, 0.5082, 0.4501\n",
"best mean recall: 0.3771 at epoch: 192\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024/10/07 20:07:29 WARNING mlflow.utils.requirements_utils: Found torchvision version (0.19.0+cu124) contains a local version label (+cu124). MLflow logged a pip requirement for this package as 'torchvision==0.19.0' without the local version label to make it installable from PyPI. To specify pip requirements containing local version labels, please use `conda_env` or `pip_requirements`.\n",
"2024/10/07 20:07:29 WARNING mlflow.utils.requirements_utils: Found torchaudio version (2.4.0+cu124) contains a local version label (+cu124). MLflow logged a pip requirement for this package as 'torchaudio==2.4.0' without the local version label to make it installable from PyPI. To specify pip requirements containing local version labels, please use `conda_env` or `pip_requirements`.\n",
"/hpc/mydata/kevin.zhao/miniconda3/envs/mlchallenge/lib/python3.9/site-packages/_distutils_hack/__init__.py:32: UserWarning: Setuptools is replacing distutils. Support for replacing an already imported distutils is deprecated. In the future, this condition will fail. Register concerns at https://github.com/pypa/setuptools/issues/new?template=distutils-deprecation.yml\n",
" warnings.warn(\n",
"2024/10/07 20:07:29 WARNING mlflow.models.model: Input example should be provided to infer model signature if the model signature is not provided when logging the model.\n"
]
}
],
"source": [
"from torchinfo import summary\n",
"\n",
"mlflow.end_run()\n",
"mlflow.set_experiment('training 3D U-Net model for the cryoET ML Challenge')\n",
"epochs = 200\n",
"with mlflow.start_run():\n",
" params = {\n",
" \"epochs\": epochs,\n",
" \"learning_rate\": lr,\n",
" \"loss_function\": loss_function.__class__.__name__,\n",
" \"metric_function\": recall_metric.__class__.__name__,\n",
" \"optimizer\": \"Adam\",\n",
" }\n",
" # Log training parameters.\n",
" mlflow.log_params(params)\n",
"\n",
" # Log model summary.\n",
" with open(\"model_summary.txt\", \"w\") as f:\n",
" f.write(str(summary(model)))\n",
" mlflow.log_artifact(\"model_summary.txt\")\n",
"\n",
" train(train_loader, model, loss_function, dice_metric, optimizer, max_epochs=epochs)\n",
"\n",
" # Save the trained model to MLflow.\n",
" mlflow.pytorch.log_model(model, \"model\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6b72796c-6468-4211-aaf5-52d7a1b6fd4b",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "mlchallenge",
"language": "python",
"name": "mlchallenge"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.19"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | czimaginginstitute/2024_czii_mlchallenge_notebooks | 3d_unet_monai/inference.ipynb | .ipynb | 633,135 | 508 | {
"cells": [
{
"cell_type": "code",
"execution_count": 11,
"id": "60fecd04-06e4-4544-8329-7393e2817ea8",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from monai.networks.nets import UNet\n",
"from monai.inferers import sliding_window_inference"
]
},
{
"cell_type": "markdown",
"id": "406d1f83-dfd5-4e7b-9532-9d4670642009",
"metadata": {},
"source": [
"## Load tomograms and picks"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "638e8776-2ac4-4cb3-bf19-195545daff24",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from tqdm import tqdm\n",
"import copick\n",
"\n",
"copick_config_path = \"/hpc/projects/group.czii/kevin.zhao/ml_challenge/example_notebooks/synthetic_data_10439.json\" \n",
"root = copick.from_file(copick_config_path)"
]
},
{
"cell_type": "markdown",
"id": "5507dc69-83a3-4d4e-ac35-3c49400e69ff",
"metadata": {},
"source": [
"### Use the rest of 20 tomograms for inference"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "c900898b-2a97-4b98-899a-be1a861053e2",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 20/20 [03:38<00:00, 10.91s/it]\n"
]
}
],
"source": [
"test_dataset = []\n",
"for run in tqdm(root.runs[7:]):\n",
" tomogram = run.get_voxel_spacing(10).get_tomogram('wbp').numpy()\n",
" segmentation = run.get_segmentations(name='paintedPicks', user_id='user0', voxel_size=10, is_multilabel=True)[0].numpy()\n",
" membrane_seg = run.get_segmentations(name='membrane', user_id=\"data-portal\")[0].numpy()\n",
" segmentation[membrane_seg==1]=1 \n",
" test_dataset.append({\"image\": tomogram, \"label\": segmentation})"
]
},
{
"cell_type": "markdown",
"id": "94b2f6ce-edfe-4239-a2e6-158ef9eeab6c",
"metadata": {},
"source": [
"### Create dataloader for the test dataset"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "b7b2a461-851e-48b8-8417-1e1cd25ceb31",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading dataset: 100%|██████████| 20/20 [00:03<00:00, 5.37it/s]\n"
]
}
],
"source": [
"from monai.data import DataLoader, CacheDataset\n",
"from monai.transforms import (\n",
" Compose, \n",
" NormalizeIntensityd,\n",
" EnsureChannelFirstd, \n",
" Activationsd,\n",
" AsDiscreted\n",
")\n",
"\n",
"# define pre transforms\n",
"pre_transforms = Compose(\n",
" [ EnsureChannelFirstd(keys=[\"image\"], channel_dim=\"no_channel\"),\n",
" NormalizeIntensityd(keys=[\"image\"]),\n",
"])\n",
"\n",
"\n",
"test_ds = CacheDataset(data=test_dataset, transform=pre_transforms)\n",
"test_loader = DataLoader(test_ds, batch_size=4, shuffle=False, num_workers=4, pin_memory=torch.cuda.is_available())"
]
},
{
"cell_type": "markdown",
"id": "a5fb15d5-65ff-4aff-be39-522d8e8bf6d4",
"metadata": {},
"source": [
"## Load model and weights"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "746c77a1-8c48-49ed-89d8-b5c48f5baa24",
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cuda\n"
]
},
{
"data": {
"text/plain": [
"UNet(\n",
" (model): Sequential(\n",
" (0): ResidualUnit(\n",
" (conv): Sequential(\n",
" (unit0): Convolution(\n",
" (conv): Conv3d(1, 48, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
" (adn): ADN(\n",
" (N): InstanceNorm3d(48, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
" (D): Dropout(p=0.0, inplace=False)\n",
" (A): PReLU(num_parameters=1)\n",
" )\n",
" )\n",
" )\n",
" (residual): Conv3d(1, 48, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
" )\n",
" (1): SkipConnection(\n",
" (submodule): Sequential(\n",
" (0): ResidualUnit(\n",
" (conv): Sequential(\n",
" (unit0): Convolution(\n",
" (conv): Conv3d(48, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
" (adn): ADN(\n",
" (N): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
" (D): Dropout(p=0.0, inplace=False)\n",
" (A): PReLU(num_parameters=1)\n",
" )\n",
" )\n",
" )\n",
" (residual): Conv3d(48, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1))\n",
" )\n",
" (1): SkipConnection(\n",
" (submodule): Sequential(\n",
" (0): ResidualUnit(\n",
" (conv): Sequential(\n",
" (unit0): Convolution(\n",
" (conv): Conv3d(64, 80, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
" (adn): ADN(\n",
" (N): InstanceNorm3d(80, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
" (D): Dropout(p=0.0, inplace=False)\n",
" (A): PReLU(num_parameters=1)\n",
" )\n",
" )\n",
" )\n",
" (residual): Conv3d(64, 80, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n",
" )\n",
" (1): SkipConnection(\n",
" (submodule): ResidualUnit(\n",
" (conv): Sequential(\n",
" (unit0): Convolution(\n",
" (conv): Conv3d(80, 80, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
" (adn): ADN(\n",
" (N): InstanceNorm3d(80, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
" (D): Dropout(p=0.0, inplace=False)\n",
" (A): PReLU(num_parameters=1)\n",
" )\n",
" )\n",
" )\n",
" (residual): Identity()\n",
" )\n",
" )\n",
" (2): Sequential(\n",
" (0): Convolution(\n",
" (conv): ConvTranspose3d(160, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
" (adn): ADN(\n",
" (N): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
" (D): Dropout(p=0.0, inplace=False)\n",
" (A): PReLU(num_parameters=1)\n",
" )\n",
" )\n",
" (1): ResidualUnit(\n",
" (conv): Sequential(\n",
" (unit0): Convolution(\n",
" (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
" (adn): ADN(\n",
" (N): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
" (D): Dropout(p=0.0, inplace=False)\n",
" (A): PReLU(num_parameters=1)\n",
" )\n",
" )\n",
" )\n",
" (residual): Identity()\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (2): Sequential(\n",
" (0): Convolution(\n",
" (conv): ConvTranspose3d(128, 48, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), output_padding=(1, 1, 1))\n",
" (adn): ADN(\n",
" (N): InstanceNorm3d(48, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
" (D): Dropout(p=0.0, inplace=False)\n",
" (A): PReLU(num_parameters=1)\n",
" )\n",
" )\n",
" (1): ResidualUnit(\n",
" (conv): Sequential(\n",
" (unit0): Convolution(\n",
" (conv): Conv3d(48, 48, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
" (adn): ADN(\n",
" (N): InstanceNorm3d(48, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
" (D): Dropout(p=0.0, inplace=False)\n",
" (A): PReLU(num_parameters=1)\n",
" )\n",
" )\n",
" )\n",
" (residual): Identity()\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (2): Sequential(\n",
" (0): Convolution(\n",
" (conv): ConvTranspose3d(96, 8, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), output_padding=(1, 1, 1))\n",
" (adn): ADN(\n",
" (N): InstanceNorm3d(8, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
" (D): Dropout(p=0.0, inplace=False)\n",
" (A): PReLU(num_parameters=1)\n",
" )\n",
" )\n",
" (1): ResidualUnit(\n",
" (conv): Sequential(\n",
" (unit0): Convolution(\n",
" (conv): Conv3d(8, 8, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
" )\n",
" )\n",
" (residual): Identity()\n",
" )\n",
" )\n",
" )\n",
")"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"print(device)\n",
"\n",
"model = UNet(\n",
" spatial_dims=3,\n",
" in_channels=1,\n",
" out_channels=len(root.pickable_objects)+1,\n",
" channels=(48, 64, 80, 80),\n",
" strides=(2, 2, 1),\n",
" num_res_units=1,\n",
").to(device)\n",
"\n",
"model.load_state_dict(torch.load(\"best_metric_model.pth\", weights_only=True))\n",
"model.eval()"
]
},
{
"cell_type": "markdown",
"id": "b91114e9-9484-4907-90d2-6389bf51490a",
"metadata": {},
"source": [
"## Inference"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "44e0165e-7cac-49e5-a0f7-2d72622b6d85",
"metadata": {},
"outputs": [],
"source": [
"def inference(model, input):\n",
" def _compute(input):\n",
" return sliding_window_inference(\n",
" inputs=input,\n",
" roi_size=(96, 96, 96),\n",
" sw_batch_size=4, # one window is proecessed at a time\n",
" predictor=model,\n",
" overlap=0.5,\n",
" )\n",
"\n",
" with torch.cuda.amp.autocast():\n",
" return _compute(input)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "25a38840-efae-4d81-82fc-194e713add65",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/5 [00:00<?, ?it/s]/tmp/ipykernel_93653/339001617.py:11: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
" with torch.cuda.amp.autocast():\n",
"100%|██████████| 5/5 [00:46<00:00, 9.33s/it]\n"
]
}
],
"source": [
"from monai.data import decollate_batch\n",
"from tqdm import tqdm\n",
"\n",
"post_transforms = Compose([\n",
" Activationsd(keys=\"pred\", softmax=True),\n",
" AsDiscreted(keys=\"pred\", argmax=True)\n",
"])\n",
"\n",
"predictions = []\n",
"with torch.no_grad():\n",
" for data in tqdm(test_loader):\n",
" tomogram = data['image'].to(device) # only support batch=1 and channel first\n",
" data[\"pred\"] = inference(model, tomogram)\n",
" data = [post_transforms(i) for i in decollate_batch(data)]\n",
" for b in data:\n",
" predictions.append(b['pred'].squeeze(0).numpy(force=True))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "786ec4ce-e831-4383-8998-5f25f35e8964",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0. 2. 3. 4. 5. 6. 7.]\n"
]
}
],
"source": [
"print(np.unique(predictions[0]))"
]
},
{
"cell_type": "markdown",
"id": "9d0b7be5-56d4-4f0c-8e68-c1d90a41fb24",
"metadata": {},
"source": [
"## Visualize the inference results"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "c07be623-c498-4c54-b9b5-b6a94a76fece",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1500x500 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"# Plot the images\n",
"plt.figure(figsize=(15, 5))\n",
"\n",
"plt.subplot(1, 3, 1)\n",
"plt.title('Tomogram')\n",
"plt.imshow(test_dataset[0]['image'][60], cmap='gray')\n",
"plt.axis('off')\n",
"\n",
"plt.subplot(1, 3, 2)\n",
"plt.title('Ground Truth Picks')\n",
"plt.imshow(test_dataset[0]['label'][60], cmap='viridis')\n",
"plt.axis('off')\n",
"\n",
"plt.subplot(1, 3, 3)\n",
"plt.title('Predicted Mask')\n",
"plt.imshow(predictions[0][60], cmap='viridis')\n",
"plt.axis('off')\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "6d161f6c-968e-438b-9476-6fe30051dd08",
"metadata": {},
"source": [
"## Get picks from the inference masks, and save them to the copick directory"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "95a6e47d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0., 2., 3., 4., 5., 6., 7.], dtype=float32)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.unique(predictions[0])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "e5fb4fd6-6a8e-4e81-a50d-50aa7ebbb3ee",
"metadata": {},
"outputs": [],
"source": [
"import copick_utils\n",
"from copick_utils.segmentation.picks_from_segmentation import picks_from_segmentation\n",
"\n",
"\n",
"particles = dict()\n",
"for po in root.config.pickable_objects:\n",
" particles[po.name] = po.label\n",
"\n",
" \n",
"maxima_filter_size = 10\n",
"min_particle_size = 0\n",
"max_particle_size = 10\n",
"new_session_id = \"1\"\n",
"new_user_id = \"paintedFromInferencePicks\"\n",
"for prediction, run in tqdm(zip(predictions, root.runs[7:])):\n",
" for po in particles.keys():\n",
" if po != \"membrane\":\n",
" class_label = particles[po]\n",
" picks_from_segmentation(prediction, class_label, maxima_filter_size, min_particle_size, max_particle_size, new_session_id, new_user_id, po, run, voxel_spacing=10)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "mlchallenge",
"language": "python",
"name": "mlchallenge"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.19"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Unknown |
3D | czimaginginstitute/2024_czii_mlchallenge_notebooks | DeepFindET/train.ipynb | .ipynb | 962,774 | 1,025 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3D CNN Instance Segmentation of Proteins in Cryo-ET Tomograms\n",
"\n",
"This tutorial guides you through the process of training 3D U-Nets for instance segmentation of proteins in Cryo-ET tomograms. It draws inspiration from the segmentation framework introduced by E. Moebel and co-authors in [DeepFinder](https://www.nature.com/articles/s41592-021-01275-4). However, this repository introduces new developments in model architectures, data augmentations, and efficient model training, with support for datasets available on both local and remote resources.\n",
"\n",
"n this notebook, we demonstrate how to utilize this infrastructure to predict the 3D coordinates of six proteins of varying sizes, provided by the [CryoET Dataportal](https://cryoetdataportal.czscience.com) (Dataset ID: [10439](https://cryoetdataportal.czscience.com/datasets/10439)). This is a synthetic dataset generated by [Polnet](https://github.com/anmartinezs/polnet/tree/main), allowing us to reliably use the ground truth coordinates to evaluate the accuracy of our model.\n",
"\n",
"The tutorial is structured into two main components:\n",
"\n",
"1. Data Preparation: Generating target volumes that the network will use to predict coordinates.\n",
"2. Model Training: Training the 3D U-Net model.\n",
"\n",
"By following this tutorial, you will gain insights into preparing data, training a 3D U-Net model for the instance segmentation of proteins in Cryo-ET tomograms."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Installation\n",
"\n",
"# !pip install copick git+https://github.com/copick/copick-utils.git git+https://github.com/copick/DeepFindET.git"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Creating a copick project\n",
"\n",
"import os\n",
"import shutil\n",
"\n",
"config_blob = \"\"\"{\n",
" \"name\": \"czii_cryoet_mlchallenge_2024\",\n",
" \"description\": \"2024 CZII CryoET ML Challenge training data.\",\n",
" \"version\": \"1.0.0\",\n",
"\n",
" \"pickable_objects\": [\n",
" {\n",
" \"name\": \"apo-ferritin\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"4V1W\",\n",
" \"label\": 1,\n",
" \"color\": [ 0, 117, 220, 128],\n",
" \"radius\": 60,\n",
" \"map_threshold\": 0.0418\n",
" },\n",
" {\n",
" \"name\": \"beta-amylase\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"1FA2\",\n",
" \"label\": 2,\n",
" \"color\": [153, 63, 0, 128],\n",
" \"radius\": 65,\n",
" \"map_threshold\": 0.035\n",
" },\n",
" {\n",
" \"name\": \"beta-galactosidase\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"6X1Q\",\n",
" \"label\": 3,\n",
" \"color\": [ 76, 0, 92, 128],\n",
" \"radius\": 90,\n",
" \"map_threshold\": 0.0578\n",
" },\n",
" {\n",
" \"name\": \"ribosome\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"6EK0\",\n",
" \"label\": 4,\n",
" \"color\": [ 0, 92, 49, 128],\n",
" \"radius\": 150,\n",
" \"map_threshold\": 0.0374\n",
" },\n",
" {\n",
" \"name\": \"thyroglobulin\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"6SCJ\",\n",
" \"label\": 5,\n",
" \"color\": [ 43, 206, 72, 128],\n",
" \"radius\": 130,\n",
" \"map_threshold\": 0.0278\n",
" },\n",
" {\n",
" \"name\": \"virus-like-particle\",\n",
" \"is_particle\": true,\n",
" \"label\": 6,\n",
" \"color\": [255, 204, 153, 128],\n",
" \"radius\": 135,\n",
" \"map_threshold\": 0.201\n",
" },\n",
" {\n",
" \"name\": \"membrane\",\n",
" \"is_particle\": false,\n",
" \"label\": 8,\n",
" \"color\": [100, 100, 100, 128]\n",
" },\n",
" {\n",
" \"name\": \"background\",\n",
" \"is_particle\": false,\n",
" \"label\": 9,\n",
" \"color\": [10, 150, 200, 128]\n",
" }\n",
" ],\n",
"\n",
" \"overlay_root\": \"/kaggle/working/overlay\",\n",
"\n",
" \"overlay_fs_args\": {\n",
" \"auto_mkdir\": true\n",
" },\n",
"\n",
" \"static_root\": \"/kaggle/input/czii-cryo-et-object-identification/train/static\"\n",
"}\"\"\"\n",
"\n",
"copick_config_path = \"/kaggle/working/copick.config\"\n",
"output_overlay = \"/kaggle/working/overlay\"\n",
"\n",
"with open(copick_config_path, \"w\") as f:\n",
" f.write(config_blob)\n",
" \n",
"# Now, setup new overlay directory\n",
"\n",
"# Define source and destination directories\n",
"source_dir = '/kaggle/input/czii-cryo-et-object-identification/train/overlay'\n",
"destination_dir = '/kaggle/working/overlay'\n",
"\n",
"# Walk through the source directory\n",
"for root, dirs, files in os.walk(source_dir):\n",
" # Create corresponding subdirectories in the destination\n",
" relative_path = os.path.relpath(root, source_dir)\n",
" target_dir = os.path.join(destination_dir, relative_path)\n",
" os.makedirs(target_dir, exist_ok=True)\n",
" \n",
" # Copy and rename each file\n",
" for file in files:\n",
" if file.startswith(\"curation_0_\"):\n",
" new_filename = file\n",
" else:\n",
" new_filename = f\"curation_0_{file}\"\n",
" \n",
" \n",
" # Define full paths for the source and destination files\n",
" source_file = os.path.join(root, file)\n",
" destination_file = os.path.join(target_dir, new_filename)\n",
" \n",
" # Copy the file with the new name\n",
" shutil.copy2(source_file, destination_file)\n",
" print(f\"Copied {source_file} to {destination_file}\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 1 - Data Preparation: Generate Targets for Training and Predicting Coordinates of Proteins in a Tomogram \n",
"\n",
"In this step, we will prepare the target data necessary for training our model and predicting the coordinates of proteins within a tomogram. \n",
"\n",
"We will use the [Copick](https://github.com/uermel/copick) tool to manage the filesystem, extract tomogram IDs, and create spherical targets corresponding to the locations of proteins. The key tasks performed in this cell include:\n",
"\n",
"* Loading Parameters: We define the size of the target spheres, specify the CoPick path, voxel size, target file name, and user ID.\n",
"* Generating Targets: For each tomogram, we extract particle coordinates, reset the target volume, generate spherical targets based on these coordinates, and save the target data in OME Zarr format."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from deepfindET.entry_points import step1\n",
"from deepfindET.utils import copick_tools\n",
"import matplotlib.pyplot as plt\n",
"import copick\n",
"\n",
"%matplotlib inline\n",
"\n",
"################## Input Parameters #################\n",
"\n",
"# Config File\n",
"config = '/kaggle/working/copick.config'\n",
"\n",
"# Query Tomogram\n",
"voxel_size = 10 \n",
"tomogram_algorithm = 'denoised'\n",
"\n",
"# Output Name for the Segmentation Targets\n",
"out_name = 'remotetargets'\n",
"out_user_id = 'deepfindET'\n",
"out_session_id = '0'\n",
"\n",
"# Read Copick Directory\n",
"copickRoot = copick.from_file(config)\n",
"\n",
"# Query Train Protein Coordiantes and any Associated Segmentations\n",
"train_targets = {}\n",
"\n",
"# Define protein targets with their respective radii\n",
"# We can Provide two forms of inputs, either \n",
"# ('protein-name',radius) or ('protein-name', 'user-id', 'session-id', 'radius')\n",
"targets = [(obj.name, None, None, (obj.radius / voxel_size)) for obj in copickRoot.pickable_objects if obj.is_particle]\n",
"\n",
"# Set run_ids to None, indicating that targets will be generated for the entire CoPick project by default.\n",
"# If specific Run-IDs were provided, this variable would contain a list of those IDs.\n",
"run_ids = None"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's generate the targets for our model. There are two methods to generate the training targets: either through a function call or via a CLI command. In this notebook, we'll use the function, but for those interested, the equivalent CLI command is `step1 create`. To see all available options, you can run `step1 create --help`.\n",
"\n",
"Targets can be created for specific user-defined Run-IDs or for the entire CoPick project. If no Run-IDs are specified, DeepFindET will default to creating targets for the entire project."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16172\n",
"Annotating 1774 objects ...\n",
"\n",
"Processing Run: 16173\n",
"Annotating 1786 objects ...\n",
"\n",
"Processing Run: 16174\n",
"Annotating 1784 objects ...\n",
"\n",
"Processing Run: 16175\n",
"Annotating 1784 objects ...\n",
"\n",
"Processing Run: 16176\n",
"Annotating 1781 objects ...\n",
"\n",
"Processing Run: 16177\n",
"Annotating 1827 objects ...\n",
"\n",
"Processing Run: 16178\n",
"Annotating 1781 objects ...\n",
"\n",
"Processing Run: 16179\n",
"Annotating 1811 objects ...\n",
"\n",
"Processing Run: 16180\n",
"Annotating 1808 objects ...\n",
"\n",
"Processing Run: 16181\n",
"Annotating 1786 objects ...\n",
"\n",
"Processing Run: 16182\n",
"Annotating 1777 objects ...\n",
"\n",
"Processing Run: 16183\n",
"Annotating 1805 objects ...\n",
"\n",
"Processing Run: 16184\n",
"Annotating 1791 objects ...\n",
"\n",
"Processing Run: 16185\n",
"Annotating 1806 objects ...\n",
"\n",
"Processing Run: 16186\n",
"Annotating 1759 objects ...\n",
"\n",
"Processing Run: 16187\n",
"Annotating 1809 objects ...\n",
"\n",
"Processing Run: 16188\n",
"Annotating 1770 objects ...\n",
"\n",
"Processing Run: 16189\n",
"Annotating 1765 objects ...\n",
"\n",
"Processing Run: 16190\n",
"Annotating 1796 objects ...\n",
"\n",
"Processing Run: 16191\n",
"Annotating 1801 objects ...\n",
"\n",
"Processing Run: 16192\n",
"Annotating 1766 objects ...\n",
"\n",
"Processing Run: 16193\n",
"Annotating 1789 objects ...\n",
"\n",
"Processing Run: 16194\n",
"Annotating 1799 objects ...\n",
"\n",
"Processing Run: 16195\n",
"Annotating 1784 objects ...\n",
"\n",
"Processing Run: 16196\n",
"Annotating 1783 objects ...\n",
"\n",
"Processing Run: 16197\n",
"Annotating 1824 objects ...\n",
"\n",
"Processing Run: 16198\n",
"Annotating 1751 objects ...\n",
"\n"
]
}
],
"source": [
"# Generate train target information\n",
"for t in targets:\n",
" obj_name, user_id, session_id, radius = t\n",
" info = {\n",
" \"label\": copickRoot.get_object(obj_name).label,\n",
" \"user_id\": user_id,\n",
" \"session_id\": session_id,\n",
" \"radius\": radius,\n",
" \"is_particle_target\": True,\n",
" }\n",
" train_targets[obj_name] = info\n",
"\n",
"\n",
"# Define segmentation target (e.g., membrane)\n",
"seg_targets = [('membrane', None, None)]\n",
"\n",
"# Generate segmentation target information\n",
"for s in seg_targets:\n",
" obj_name, user_id, session_id = s\n",
" info = {\n",
" \"label\": copickRoot.get_object(obj_name).label,\n",
" \"user_id\": user_id,\n",
" \"session_id\": session_id,\n",
" \"radius\": None, \n",
" \"is_particle_target\": False, \n",
" }\n",
" train_targets[obj_name] = info\n",
"\n",
"# Call the create_train_targets function from step1 to generate the training targets for the 3D U-Net model.\n",
"# The function will use the parameters defined in the previous cells and the following inputs:\n",
"step1.create_train_targets(\n",
" config, # The configuration file path specifying various settings and parameters for the project.\n",
" train_targets, # A dictionary containing the target information for each protein or object to be segmented.\n",
" run_ids, # The list of Run-IDs for which to generate targets. None means targets for the entire project.\n",
" voxel_size, # The voxel size to be used in the tomogram data.\n",
" tomogram_algorithm, # The reconstruction algorithm used for the tomograms, e.g., 'wbp' (weighted back projection).\n",
" out_name, # The output name for the generated segmentation targets.\n",
" out_user_id, # The user ID under which the output targets will be saved.\n",
" out_session_id, # The session ID associated with the output, typically used for tracking purposes.\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### (Optional) Visualize the Tomogram and Target Volumes "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Option 1: Query All RunIDs\n",
"# Retrieve all available Run-IDs from the CoPick project. This generates a list of Run-IDs by iterating over all runs in copickRoot.\n",
"run_ids = [run.name for run in copickRoot.runs]\n",
"\n",
"# Option 2: Manually Specify Specific Run\n",
"# Define a specific Run-ID manually. This is useful for extracting volumes for a specific run.\n",
"runID = 'TS_6_4'\n",
"\n",
"# Retrieve the specific run object from CoPick using the manually specified Run-ID.\n",
"copick_run = copickRoot.get_run(runID)\n",
"\n",
"# Extract the segmentation target associated with the specified run.\n",
"# The function get_copick_segmentation retrieves the segmentation data (e.g., target volume) based on the run object,\n",
"# segmentation name, user ID, and session ID.\n",
"train_target = copick_tools.get_copick_segmentation(\n",
" copick_run, # The run object obtained from CoPick for the specific Run-ID.\n",
" segmentationName='remotetargets', # The name of the segmentation target to retrieve.\n",
" userID='deepfindET', # The user ID under which the segmentation data is saved.\n",
" sessionID='0' # The session ID associated with the segmentation data.\n",
")\n",
"\n",
"# Retrieve the tomogram associated with the specified Run-ID from the CoPick project.\n",
"# The function get_copick_tomogram extracts the tomogram data, using the voxel size, algorithm, and Run-ID.\n",
"train_tomogram = copick_tools.get_copick_tomogram(\n",
" copickRoot, # The root object for the CoPick project, containing all runs and associated data.\n",
" voxelSize=voxel_size, # The voxel size to be used for retrieving the tomogram.\n",
" tomoAlgorithm='denoised', # The reconstruction algorithm used for the tomogram\n",
" tomoID=runID # The specific Run-ID for which the tomogram is being retrieved.\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1500x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the images\n",
"plt.figure(figsize=(15, 5))\n",
"\n",
"# Original Image\n",
"plt.subplot(1, 2, 1)\n",
"plt.title('Tomogram')\n",
"plt.imshow(train_tomogram[90,],cmap='gray')\n",
"plt.axis('off')\n",
"\n",
"# Original Image\n",
"plt.subplot(1, 2, 2)\n",
"plt.title('Train Target')\n",
"plt.imshow(train_target[90,])\n",
"plt.axis('off')\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 2: Traing DeepFindET 3D CNN Model\n",
"\n",
"In this step, we will configure and train the DeepFinder 3D CNN model, leveraging the targets generated in the previous step. This model is designed to classify different protein types and accurately predict their 3D coordinates within tomograms. By the end of this step, the model will be capable of segmenting and identifying proteins in complex 3D Cryo-ET datasets."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-08-30 10:42:08.157945: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2024-08-30 10:42:08.172011: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2024-08-30 10:42:08.176172: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"2024-08-30 10:42:08.187916: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"2024-08-30 10:42:09.613658: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
]
}
],
"source": [
"from deepfindET.entry_points import step2\n",
"\n",
"# Specify the directory where the training results will be saved.\n",
"training_output_path = '/kaggle/working/train_results'\n",
"\n",
"# Set the model architecture to Residual U-Net ('res_unet'), \n",
"# which combines U-Net with residual connections to improve training.\n",
"model_name = 'res_unet'\n",
"\n",
"# Set to None to indicate that the model will betrained from scratch \n",
"# without using pre-trained weights.\n",
"model_pre_weights = None\n",
"\n",
"# Number of classes the model will predict. \n",
"# Here, we are working with 8 different classes (6 proteins + membrane + background).\n",
"n_class = 8\n",
"\n",
"# Input dimension size of the 3D volumes in voxels. Each input is a 72x72x72 voxel cube -- (72 Å)^3.\n",
"dim_in = 72 # [voxels]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Launch Model Training\n",
"\n",
"Similarly to the target creation command, initiating the training process for the DeepFindET 3D U-Net model can be done through a CLI call to train a model. The equivalent CLI command is step2 train. You can view all available options by running:\n",
"\n",
"`step2 train --help` \n",
"\n",
"**Dataset Splitting** \n",
"If specific runs for validation and training aren't provided, the entire dataset in the Copick directory will automatically be split into 70/15/15 for training, validation, and testing, respectively.\n",
"\n",
"**Training Strategy** \n",
"In this setup, the training process is inspired by the methodology described in the DeepFinder paper. To address data imbalance issues commonly encountered in semantic segmentation tasks, bootstrapping is employed. Additionally, a multi-class Tversky loss function is utilized to improve performance on imbalanced datasets.\n",
"\n",
"**Model Architecture** \n",
"Users have the flexibility to experiment with various model architectures, including traditional U-Nets (`unet`), residual U-Nets (`res_unet`), and attention U-Nets (`attn_res_unet`). This versatility allows fine-tuning the model to better fit the specific characteristics of the dataset being used.\n",
"\n",
"**Initiating Training** \n",
"Below is the code snippet to initiate the training of the DeepFindET 3D U-Net model. The training parameters will automatically be saved in the training_output_path as `experiment_config.json`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Training res_unet with Randomly Initialized Weights\n",
"\n",
"Physical devices cannot be modified after being initialized\n",
"Loading Targets and Tomograms for the Following Runs: ['16197', '16173', '16190', '16192', '16179']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████████| 5/5 [00:42<00:00, 8.51s/it]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading Targets and Tomograms for the Following Runs: ['16183', '16185', '16189', '16184', '16180']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████████| 5/5 [00:39<00:00, 7.95s/it]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Configuration saved to train_results/experiment_config.json\n",
"\n",
"Training Parameters: {\n",
" \"input\": {\n",
" \"config_path_train\": \"config_10439.json\",\n",
" \"config_path_valid\": null,\n",
" \"target_name\": \"remotetargets\",\n",
" \"target_user_id\": \"deepfindET\",\n",
" \"target_session_id\": \"1\"\n",
" },\n",
" \"output\": {\n",
" \"out_dir\": \"train_results\",\n",
" \"classes\": {\n",
" \"membrane\": 1,\n",
" \"adp-mitochondrial\": 2,\n",
" \"alkaline-phosphate\": 3,\n",
" \"nucleosome\": 4,\n",
" \"ribosome\": 5,\n",
" \"vault\": 6,\n",
" \"virus-like-capsid\": 7\n",
" }\n",
" },\n",
" \"network_architecture\": {\n",
" \"architecture\": \"res_unet\",\n",
" \"layers\": [\n",
" 48,\n",
" 64,\n",
" 128\n",
" ],\n",
" \"dropout_rate\": 0.0\n",
" },\n",
" \"training_params\": {\n",
" \"n_class\": 8,\n",
" \"dim_in\": 72,\n",
" \"batch_size\": 10,\n",
" \"epochs\": 70,\n",
" \"steps_per_epoch\": 150,\n",
" \"steps_per_valid\": 10,\n",
" \"num_sub_epoch\": 10,\n",
" \"sample_size\": 5,\n",
" \"loss\": \"tversky_loss\",\n",
" \"class_weights\": {\n",
" \"0\": 1,\n",
" \"1\": 1,\n",
" \"2\": 3000,\n",
" \"3\": 3000,\n",
" \"4\": 3000,\n",
" \"5\": 750,\n",
" \"6\": 500,\n",
" \"7\": 750\n",
" }\n",
" },\n",
" \"learning_params\": {\n",
" \"learning_rate\": 0.0001,\n",
" \"min_learning_rate\": 1e-06,\n",
" \"monitor\": \"val_f1\",\n",
" \"factor\": 0.75,\n",
" \"patience\": 6\n",
" }\n",
"} \n",
"\n",
"Launch training ...\n",
"Epoch 1/70\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-08-29 21:58:37.808601: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f16[10,112,72,72,72]{4,3,2,1,0}, u8[0]{0}) custom-call(f16[10,48,72,72,72]{4,3,2,1,0}, f16[48,112,3,3,3]{4,3,2,1,0}), window={size=3x3x3 pad=1_1x1_1x1_1}, dim_labels=bf012_oi012->bf012, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0},\"force_earliest_schedule\":false} is taking a while...\n",
"2024-08-29 21:58:38.176965: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 1.368469351s\n",
"Trying algorithm eng0{} for conv (f16[10,112,72,72,72]{4,3,2,1,0}, u8[0]{0}) custom-call(f16[10,48,72,72,72]{4,3,2,1,0}, f16[48,112,3,3,3]{4,3,2,1,0}), window={size=3x3x3 pad=1_1x1_1x1_1}, dim_labels=bf012_oi012->bf012, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0},\"force_earliest_schedule\":false} is taking a while...\n",
"2024-08-29 21:59:01.750845: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'input_reduce_fusion_48__7', 3672 bytes spill stores, 3592 bytes spill loads\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 1s/step step - accuracy: 0.5958 - loss: 90\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m152s\u001b[0m 715ms/step - accuracy: 0.5969 - loss: 904.8229 - val_accuracy: 0.8922 - val_loss: 6.7759 - learning_rate: 1.0000e-04\n",
"Epoch 2/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9019 - loss: 6\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 527ms/step - accuracy: 0.9019 - loss: 686.3742 - val_accuracy: 0.9167 - val_loss: 5.3855 - learning_rate: 1.0000e-04\n",
"Epoch 3/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/stepstep - accuracy: 0.9181 - loss: 6\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 515ms/step - accuracy: 0.9182 - loss: 635.7211 - val_accuracy: 0.9326 - val_loss: 4.7395 - learning_rate: 1.0000e-04\n",
"Epoch 4/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9287 - loss: 6\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m72s\u001b[0m 485ms/step - accuracy: 0.9287 - loss: 607.3633 - val_accuracy: 0.9269 - val_loss: 4.9752 - learning_rate: 1.0000e-04\n",
"Epoch 5/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9350 - loss: 5\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 432ms/step - accuracy: 0.9351 - loss: 575.3796 - val_accuracy: 0.9366 - val_loss: 4.9201 - learning_rate: 1.0000e-04\n",
"Epoch 6/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9379 - loss: 5\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 411ms/step - accuracy: 0.9380 - loss: 550.3423 - val_accuracy: 0.9124 - val_loss: 4.6726 - learning_rate: 1.0000e-04\n",
"Epoch 7/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9385 - loss: 5\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 413ms/step - accuracy: 0.9385 - loss: 541.2126 - val_accuracy: 0.9347 - val_loss: 4.1045 - learning_rate: 1.0000e-04\n",
"Epoch 8/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9414 - loss: 5\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 413ms/step - accuracy: 0.9414 - loss: 506.1590 - val_accuracy: 0.9426 - val_loss: 4.1934 - learning_rate: 1.0000e-04\n",
"Epoch 9/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9412 - loss: 5\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 412ms/step - accuracy: 0.9412 - loss: 509.3693 - val_accuracy: 0.9330 - val_loss: 4.6670 - learning_rate: 1.0000e-04\n",
"Epoch 10/70\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - accuracy: 0.9444 - loss: 517.2029"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step\n",
"Swapping datasets at epoch 10\n",
"Loading Targets and Tomograms for the Following Runs: ['16177', '16198', '16190', '16192', '16179']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████████| 5/5 [00:40<00:00, 8.12s/it]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading Targets and Tomograms for the Following Runs: ['16189', '16183', '16185', '16184', '16180']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████████| 5/5 [00:51<00:00, 10.34s/it]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m198s\u001b[0m 1s/step - accuracy: 0.9444 - loss: 517.1545 - val_accuracy: 0.9246 - val_loss: 4.3672 - learning_rate: 1.0000e-04\n",
"Epoch 11/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/stepstep - accuracy: 0.9489 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 651ms/step - accuracy: 0.9489 - loss: 476.4933 - val_accuracy: 0.9422 - val_loss: 4.0214 - learning_rate: 1.0000e-04\n",
"Epoch 12/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9450 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 365ms/step - accuracy: 0.9450 - loss: 499.1635 - val_accuracy: 0.9218 - val_loss: 4.2824 - learning_rate: 1.0000e-04\n",
"Epoch 13/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9473 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 361ms/step - accuracy: 0.9473 - loss: 492.6158 - val_accuracy: 0.9046 - val_loss: 4.6625 - learning_rate: 1.0000e-04\n",
"Epoch 14/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9479 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 359ms/step - accuracy: 0.9479 - loss: 487.2518 - val_accuracy: 0.9431 - val_loss: 3.8160 - learning_rate: 1.0000e-04\n",
"Epoch 15/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/stepstep - accuracy: 0.9465 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 361ms/step - accuracy: 0.9465 - loss: 484.7548 - val_accuracy: 0.9390 - val_loss: 3.9647 - learning_rate: 1.0000e-04\n",
"Epoch 16/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9487 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 359ms/step - accuracy: 0.9487 - loss: 474.7296 - val_accuracy: 0.9036 - val_loss: 4.4996 - learning_rate: 1.0000e-04\n",
"Epoch 17/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9476 - loss: 5\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 361ms/step - accuracy: 0.9476 - loss: 507.1517 - val_accuracy: 0.9337 - val_loss: 4.4091 - learning_rate: 1.0000e-04\n",
"Epoch 18/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9475 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 361ms/step - accuracy: 0.9475 - loss: 489.6140 - val_accuracy: 0.9351 - val_loss: 3.5281 - learning_rate: 1.0000e-04\n",
"Epoch 19/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9496 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 361ms/step - accuracy: 0.9496 - loss: 474.9468 - val_accuracy: 0.9406 - val_loss: 3.8171 - learning_rate: 1.0000e-04\n",
"Epoch 20/70\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - accuracy: 0.9469 - loss: 477.1599"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step\n",
"Swapping datasets at epoch 20\n",
"Loading Targets and Tomograms for the Following Runs: ['16176', '16190', '16182', '16197', '16194']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████████| 5/5 [00:47<00:00, 9.42s/it]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading Targets and Tomograms for the Following Runs: ['16183', '16189', '16185', '16180', '16184']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████████| 5/5 [00:47<00:00, 9.45s/it]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m187s\u001b[0m 1s/step - accuracy: 0.9469 - loss: 477.0893 - val_accuracy: 0.9379 - val_loss: 3.8493 - learning_rate: 1.0000e-04\n",
"Epoch 21/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/stepstep - accuracy: 0.9517 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 360ms/step - accuracy: 0.9517 - loss: 454.8186 - val_accuracy: 0.9355 - val_loss: 4.5029 - learning_rate: 1.0000e-04\n",
"Epoch 22/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9506 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 361ms/step - accuracy: 0.9506 - loss: 444.5406 - val_accuracy: 0.9335 - val_loss: 4.2236 - learning_rate: 1.0000e-04\n",
"Epoch 23/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/stepstep - accuracy: 0.9501 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 360ms/step - accuracy: 0.9501 - loss: 465.7236 - val_accuracy: 0.9478 - val_loss: 3.8726 - learning_rate: 1.0000e-04\n",
"Epoch 24/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9482 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 367ms/step - accuracy: 0.9482 - loss: 483.8343 - val_accuracy: 0.9222 - val_loss: 4.2939 - learning_rate: 1.0000e-04\n",
"Epoch 25/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/stepstep - accuracy: 0.9501 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 360ms/step - accuracy: 0.9501 - loss: 483.0594 - val_accuracy: 0.9433 - val_loss: 4.0976 - learning_rate: 1.0000e-04\n",
"Epoch 26/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9522 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 358ms/step - accuracy: 0.9522 - loss: 449.4234 - val_accuracy: 0.9159 - val_loss: 4.7830 - learning_rate: 1.0000e-04\n",
"Epoch 27/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/stepstep - accuracy: 0.9498 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 361ms/step - accuracy: 0.9498 - loss: 457.4147 - val_accuracy: 0.9295 - val_loss: 4.6645 - learning_rate: 1.0000e-04\n",
"Epoch 28/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9502 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 360ms/step - accuracy: 0.9502 - loss: 455.0070 - val_accuracy: 0.9458 - val_loss: 4.2837 - learning_rate: 1.0000e-04\n",
"Epoch 29/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9515 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 362ms/step - accuracy: 0.9515 - loss: 474.0929 - val_accuracy: 0.9445 - val_loss: 3.3593 - learning_rate: 1.0000e-04\n",
"Epoch 30/70\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - accuracy: 0.9504 - loss: 457.9122"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step\n",
"Swapping datasets at epoch 30\n",
"Loading Targets and Tomograms for the Following Runs: ['16198', '16196', '16174', '16173', '16187']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████████| 5/5 [00:43<00:00, 8.65s/it]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading Targets and Tomograms for the Following Runs: ['16184', '16180', '16185', '16183', '16189']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████████| 5/5 [00:43<00:00, 8.68s/it]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m179s\u001b[0m 1s/step - accuracy: 0.9504 - loss: 457.8996 - val_accuracy: 0.9377 - val_loss: 3.8451 - learning_rate: 1.0000e-04\n",
"Epoch 31/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9530 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 360ms/step - accuracy: 0.9530 - loss: 464.6775 - val_accuracy: 0.9546 - val_loss: 3.3027 - learning_rate: 1.0000e-04\n",
"Epoch 32/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9543 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 360ms/step - accuracy: 0.9543 - loss: 439.7340 - val_accuracy: 0.9361 - val_loss: 3.8473 - learning_rate: 1.0000e-04\n",
"Epoch 33/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9534 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 365ms/step - accuracy: 0.9534 - loss: 456.9513 - val_accuracy: 0.9518 - val_loss: 3.5836 - learning_rate: 1.0000e-04\n",
"Epoch 34/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9553 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 360ms/step - accuracy: 0.9553 - loss: 447.2854 - val_accuracy: 0.9342 - val_loss: 3.4544 - learning_rate: 1.0000e-04\n",
"Epoch 35/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9541 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 361ms/step - accuracy: 0.9541 - loss: 428.2058 - val_accuracy: 0.9456 - val_loss: 3.9685 - learning_rate: 1.0000e-04\n",
"Epoch 36/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9538 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 360ms/step - accuracy: 0.9537 - loss: 435.5706 - val_accuracy: 0.9397 - val_loss: 4.0703 - learning_rate: 1.0000e-04\n",
"Epoch 37/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9524 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 361ms/step - accuracy: 0.9524 - loss: 443.4194 - val_accuracy: 0.9381 - val_loss: 3.9391 - learning_rate: 1.0000e-04\n",
"Epoch 38/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/stepstep - accuracy: 0.9516 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 360ms/step - accuracy: 0.9516 - loss: 455.4965 - val_accuracy: 0.9207 - val_loss: 4.2185 - learning_rate: 1.0000e-04\n",
"Epoch 39/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/stepstep - accuracy: 0.9527 - loss: 4\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 361ms/step - accuracy: 0.9527 - loss: 447.2529 - val_accuracy: 0.9433 - val_loss: 4.0564 - learning_rate: 1.0000e-04\n",
"Epoch 40/70\n",
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - accuracy: 0.9528 - loss: 467.5904"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step\n",
"Swapping datasets at epoch 40\n",
"Loading Targets and Tomograms for the Following Runs: ['16178', '16182', '16174', '16195', '16179']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████████| 5/5 [00:50<00:00, 10.07s/it]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading Targets and Tomograms for the Following Runs: ['16184', '16180', '16185', '16189', '16183']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████████| 5/5 [00:43<00:00, 8.67s/it]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m187s\u001b[0m 1s/step - accuracy: 0.9528 - loss: 467.5276 - val_accuracy: 0.9362 - val_loss: 4.4366 - learning_rate: 1.0000e-04\n",
"Epoch 41/70\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 223ms/stepstep - accuracy: 0.9529 - loss: \n"
]
}
],
"source": [
"# Initiate the training of the DeepFindET 3D U-Net model.\n",
"step2.train_model(\n",
" config, # Configuration file with various settings for the project.\n",
" voxel_size, # Voxel size used in the tomogram data.\n",
" tomogram_algorithm, # Reconstruction algorithm used for the tomograms (e.g., 'wbp').\n",
" targets, # Target data for training the model.\n",
" training_output_path, # Path where the training outputs will be saved.\n",
" model_name, # Model architecture name ('res_unet').\n",
" model_pre_weights, # Pre-trained weights (None for training from scratch).\n",
" n_class, # Number of classes for segmentation (8 in this case).\n",
" path_valid=None, # Path to validation data (None means internal splitting may be used).\n",
" dim_in=dim_in, # Input dimension size in voxels.\n",
" n_sub_epoch=10, # Number of epochs to train on tomograms prior to swapping to a new set of tomograms.\n",
" sample_size=5, # Number of tomograms to extract per epoch.\n",
" batch_size=10, # Batch size used during training.\n",
" epochs=70, # Total number of training epochs.\n",
" steps_per_epoch=150, # Number of steps per epoch.\n",
" n_valid=20, # Number of validation samples.\n",
" model_filters=[48, 64, 128], # Filters in the convolutional layers at each level of the U-Net.\n",
" model_dropout=0, # Dropout rate (0 means no dropout applied).\n",
" target_name=\"remotetargets\", # Name of the segmentation targets.\n",
" target_user_id=\"deepfindET\", # User ID for the segmentation labels.\n",
" target_session_id=\"0\", # Session ID associated with the labeling.\n",
" valid_tomo_ids=None, # List of tomogram IDs for validation.\n",
" train_tomo_ids=None, # List of tomogram IDs for training.\n",
" class_weights=(('apo-ferritin', 62400), ('beta-amylase', 4130), ('beta-galactosidase', 3080), ('ribosome', 1800), ('thyroglobulin', 10100), ('virus-like-particle', 8400))\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### (Optional) Inspect the Training Curves"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1500x1200 with 5 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from deepfindET.utils import core\n",
"import h5py, os\n",
"\n",
"# Set the path to the training history file\n",
"history_path = os.path.join(training_output_path, 'net_train_history.h5')\n",
"\n",
"# Convert the HDF5 file containing the training history into a dictionary format\n",
"# This allows easy access to the training metrics like loss, accuracy, etc., stored during training\n",
"history = core.convert_hdf5_to_dictionary(history_path)\n",
"\n",
"# Plot the training history to visualize the learning process\n",
"# The plot_history function will generate curves for metrics such as training and validation loss, accuracy, etc.\n",
"core.plot_history(history, save_figure=False)\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| Unknown |
3D | czimaginginstitute/2024_czii_mlchallenge_notebooks | DeepFindET/inference.ipynb | .ipynb | 933,033 | 5,248 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3D CNN Instance Segmentation of Proteins in Cryo-ET Tomograms (Inference / Prediction)\n",
"\n",
"This tutorial walks you through the process of using trained 3D U-Nets for instance segmentation of proteins within Cryo-ET tomograms. The implementation leverages the CoPick file management system for streamlined data handling.\n",
"\n",
"The tutorial is structured into three main components:\n",
"\n",
"1. (Step3) Inference: Utilize the trained DeepFindET model to generate segmentation masks for proteins within the tomograms.\n",
"2. (Step4) Localization: Extract and determine the 3D coordinates of proteins from the generated segmentation masks.\n",
"3. (Step5) Evaluation: Assess the accuracy and quality of the predicted protein coordinates.\n",
"\n",
"By following this tutorial, you will be able to segment tomograms with trained DeepFindET models and extract 3D coordinates of proteins for sub-tomogram averaging."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Install packages\n",
"\n",
"# !pip install copick git+https://github.com/copick/copick-utils.git git+https://github.com/copick/DeepFindET.git"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Make a copick project\n",
"\n",
"config_blob = \"\"\"{\n",
" \"name\": \"czii_cryoet_mlchallenge_2024\",\n",
" \"description\": \"2024 CZII CryoET ML Challenge training data.\",\n",
" \"version\": \"1.0.0\",\n",
"\n",
" \"pickable_objects\": [\n",
" {\n",
" \"name\": \"apo-ferritin\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"4V1W\",\n",
" \"label\": 1,\n",
" \"color\": [ 0, 117, 220, 128],\n",
" \"radius\": 60,\n",
" \"map_threshold\": 0.0418\n",
" },\n",
" {\n",
" \"name\": \"beta-amylase\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"1FA2\",\n",
" \"label\": 2,\n",
" \"color\": [153, 63, 0, 128],\n",
" \"radius\": 65,\n",
" \"map_threshold\": 0.035\n",
" },\n",
" {\n",
" \"name\": \"beta-galactosidase\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"6X1Q\",\n",
" \"label\": 3,\n",
" \"color\": [ 76, 0, 92, 128],\n",
" \"radius\": 90,\n",
" \"map_threshold\": 0.0578\n",
" },\n",
" {\n",
" \"name\": \"ribosome\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"6EK0\",\n",
" \"label\": 4,\n",
" \"color\": [ 0, 92, 49, 128],\n",
" \"radius\": 150,\n",
" \"map_threshold\": 0.0374\n",
" },\n",
" {\n",
" \"name\": \"thyroglobulin\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"6SCJ\",\n",
" \"label\": 5,\n",
" \"color\": [ 43, 206, 72, 128],\n",
" \"radius\": 130,\n",
" \"map_threshold\": 0.0278\n",
" },\n",
" {\n",
" \"name\": \"virus-like-particle\",\n",
" \"is_particle\": true,\n",
" \"pdb_id\": \"6N4V\", \n",
" \"label\": 6,\n",
" \"color\": [255, 204, 153, 128],\n",
" \"radius\": 135,\n",
" \"map_threshold\": 0.201\n",
" }\n",
" ],\n",
"\n",
" \"overlay_root\": \"/kaggle/working/test/overlay\",\n",
"\n",
" \"overlay_fs_args\": {\n",
" \"auto_mkdir\": true\n",
" },\n",
"\n",
" \"static_root\": \"/kaggle/input/czii-cryo-et-object-identification/test/static\"\n",
"}\"\"\"\n",
"\n",
"copick_config_path = \"/kaggle/working/copick.config\"\n",
"\n",
"with open(copick_config_path, \"w\") as f:\n",
" f.write(config_blob)\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 3: Perform Inference on Test Dataset and Evaluate Results \n",
"\n",
"With a trained model in hand, it's time to move forward with making predictions. In this step, you will execute model inference using a checkpoint from a saved epoch, allowing you to evaluate the model's performance on your test dataset."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-09-04 11:44:30.371701: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2024-09-04 11:44:30.383336: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2024-09-04 11:44:30.386901: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"2024-09-04 11:44:30.396886: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"2024-09-04 11:44:31.976469: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
]
}
],
"source": [
"from deepfindET.entry_points import step3\n",
"from deepfindET.utils import copick_tools\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import copick\n",
"\n",
"%matplotlib inline\n",
"\n",
"################## Input Parameters #################\n",
"\n",
"# Copick Configuration File\n",
"config = '/kaggle/working/copick.config'\n",
"\n",
"# Model Parameters\n",
"n_class = 8 # Number of classes to predict.\n",
"patch_size = 160 # Size of the input patch fed into the model for inference.\n",
"model_name = 'res_unet' # The model architecture used for training.\n",
"filters = [48, 64, 128] # Number of filters for U-Net (same parameter as used for training).\n",
"dropout = 0 # Dropout rate applied during inference.\n",
"\n",
"# Path to the pre-trained model weights for the chosen architecture.\n",
"model_weights = '/kaggle/input/deepfindetv002/keras/default/1/net_weights_epoch70.h5'\n",
"\n",
"# Query for Tomogram\n",
"voxel_size = 10 # Resolution of the tomogram in voxel size.\n",
"tomogram_algorithm = 'denoised' # Reconstruction algorithm used for generating the tomogram\n",
"\n",
"# Output Segmentation Write Name\n",
"segmentation_name = 'predict'\n",
"session_id = '0'\n",
"user_id = 'deepfindET'\n",
"\n",
"######################################################"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Training res_unet with train_results2/net_weights_FINAL.h5 Weights\n",
"\n",
"\n",
"Segmentation Parameters: {\n",
" \"input\": {\n",
" \"predict_config\": \"config_10439.json\",\n",
" \"voxel_size\": 10,\n",
" \"tomogram_algorithm\": \"wbp\"\n",
" },\n",
" \"model_architecture\": {\n",
" \"n_class\": 8,\n",
" \"model_name\": \"res_unet\",\n",
" \"path_weights\": \"train_results2/net_weights_FINAL.h5\",\n",
" \"patch_size\": 160,\n",
" \"model_filters\": [\n",
" 48,\n",
" 64,\n",
" 128\n",
" ],\n",
" \"model_dropout\": 0\n",
" },\n",
" \"output\": {\n",
" \"user_id\": \"deepfindET\",\n",
" \"session_id\": \"1\",\n",
" \"output_scoremap\": false,\n",
" \"scoremap_name\": \"scoremap\",\n",
" \"segmentation_name\": \"predict\",\n",
" \"tomo_ids\": null\n",
" }\n",
"} \n",
"\n",
"\n",
"Processing Run: 16172 (0/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Model took 33.30 seconds to predict\n",
"\n",
"Processing Run: 16173 (1/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Model took 25.27 seconds to predict\n",
"\n",
"Processing Run: 16174 (2/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 9 / 72 ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Model took 25.30 seconds to predict\n",
"\n",
"Processing Run: 16175 (3/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 17 / 72 ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Model took 25.43 seconds to predict\n",
"\n",
"Processing Run: 16176 (4/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 25 / 72 ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Model took 25.52 seconds to predict\n",
"\n",
"Processing Run: 16177 (5/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 33 / 72 ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Model took 25.44 seconds to predict\n",
"\n",
"Processing Run: 16178 (6/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 41 / 72 ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Model took 25.58 seconds to predict\n",
"\n",
"Processing Run: 16179 (7/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 49 / 72 ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Model took 25.06 seconds to predict\n",
"\n",
"Processing Run: 16180 (8/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 57 / 72 ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Model took 24.99 seconds to predict\n",
"\n",
"Processing Run: 16181 (9/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 65 / 72 ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Model took 25.43 seconds to predict\n",
"\n",
"Processing Run: 16182 (10/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Model took 25.21 seconds to predict\n",
"\n",
"Processing Run: 16183 (11/27)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Model took 24.88 seconds to predict\n",
"\n",
"Processing Run: 16184 (12/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 8 / 72 ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Model took 25.41 seconds to predict\n",
"\n",
"Processing Run: 16185 (13/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 16 / 72 ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Model took 24.88 seconds to predict\n",
"\n",
"Processing Run: 16186 (14/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 24 / 72 ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Model took 24.78 seconds to predict\n",
"\n",
"Processing Run: 16187 (15/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 32 / 72 ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Model took 24.65 seconds to predict\n",
"\n",
"Processing Run: 16188 (16/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 40 / 72 ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Model took 24.81 seconds to predict\n",
"\n",
"Processing Run: 16189 (17/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 48 / 72 ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Model took 25.22 seconds to predict\n",
"\n",
"Processing Run: 16190 (18/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 56 / 72 ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Model took 25.03 seconds to predict\n",
"\n",
"Processing Run: 16191 (19/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 64 / 72 ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Model took 25.06 seconds to predict\n",
"\n",
"Processing Run: 16192 (20/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 72 / 72 ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Model took 25.79 seconds to predict\n",
"\n",
"Processing Run: 16193 (21/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Model took 25.48 seconds to predict\n",
"\n",
"Processing Run: 16194 (22/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 7 / 72 ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Model took 25.81 seconds to predict\n",
"\n",
"Processing Run: 16195 (23/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 15 / 72 ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Model took 25.46 seconds to predict\n",
"\n",
"Processing Run: 16196 (24/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 23 / 72 ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Model took 25.92 seconds to predict\n",
"\n",
"Processing Run: 16197 (25/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 31 / 72 ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 39 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Model took 26.29 seconds to predict\n",
"\n",
"Processing Run: 16198 (26/27)\n",
"Data array is divided in 72 patches ...\n",
"Segmenting patch 1 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 2 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 3 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 4 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 5 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 6 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 7 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 8 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 9 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 10 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 11 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 12 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 13 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 14 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 15 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 16 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 17 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 18 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 19 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 20 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 21 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 22 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"Segmenting patch 23 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 24 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 25 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 26 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 27 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 28 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 29 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 30 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
"Segmenting patch 31 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 32 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 33 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 34 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 35 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 36 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 37 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 38 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 39 / 72 ...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 40 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
"Segmenting patch 41 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 42 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 43 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 44 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 45 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 46 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 47 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 48 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 49 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 50 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 51 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 52 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 53 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 54 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 55 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 56 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 57 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 58 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 59 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 60 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
"Segmenting patch 61 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 62 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
"Segmenting patch 63 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 64 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 65 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 66 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 67 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 68 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 69 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
"Segmenting patch 70 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
"Segmenting patch 71 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Segmenting patch 72 / 72 ...\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
"Model took 25.86 seconds to predict\n",
"Segmentations Complete!\n"
]
}
],
"source": [
"# Run the train DeepFindET 3D U-Net model on the copick directory.\n",
"step3.inference_tomogram_segmentation(\n",
" config, # Copick Configuration File\n",
" n_class, # Number of classes to predict.\n",
" model_name, # The model architecture used for training.\n",
" model_weights, # Path to the pre-trained model weights for the chosen architecture.\n",
" patch_size, # Size of the input patch fed into the model for inference.\n",
" user_id, # Identifier of the user or project running the segmentation.\n",
" session_id, # Session identifier for tracking purposes.\n",
" segmentation_name=segmentation_name, # Identifier for the output segmentation file.\n",
" voxel_size = voxel_size, # Voxel Size of Tomogram to Run Inference On \n",
" model_filters = filters, # Number of filters for U-Net\n",
" model_dropout = dropout, # Dropout rate\n",
" tomogram_algorithm= tomogram_algorithm, # Reconstruction algorithm used for generating the tomogram\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### (Optional) Visualize the Segmentations"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Manually Specify Specific Run\n",
"# Define a specific Run-ID manually. This is useful for extracting volumes for a specific run.\n",
"runID = 'TS_6_4'\n",
"\n",
"# Retrieve the specific run object from CoPick using the manually specified Run-ID.\n",
"copick_root = copick.from_file(config)\n",
"copick_run = copick_root.get_run(runID)\n",
"\n",
"# Extract the segmentation target associated with the specified run.\n",
"# The function get_copick_segmentation retrieves the segmentation data (e.g., target volume) based on the run object,\n",
"# segmentation name, user ID, and session ID.\n",
"train_target = copick_tools.get_copick_segmentation(\n",
" copick_run, # The run object obtained from CoPick for the specific Run-ID.\n",
" segmentationName=segmentation_name, # The name of the segmentation target to retrieve.\n",
" userID=user_id, # The user ID under which the segmentation data is saved.\n",
" sessionID=session_id # The session ID associated with the segmentation data.\n",
")\n",
"\n",
"# Retrieve the tomogram associated with the specified Run-ID from the CoPick project.\n",
"# The function get_copick_tomogram extracts the tomogram data, using the voxel size, algorithm, and Run-ID.\n",
"train_tomogram = copick_tools.get_copick_tomogram(\n",
" copick_root, # The root object for the CoPick project, containing all runs and associated data.\n",
" voxelSize=voxel_size, # The voxel size to be used for retrieving the tomogram.\n",
" tomoAlgorithm='wbp', # The reconstruction algorithm used for the tomogram, e.g., 'wbp' (weighted back projection).\n",
" tomoID=runID # The specific Run-ID for which the tomogram is being retrieved.\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1500x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the images\n",
"plt.figure(figsize=(15, 5))\n",
"\n",
"# Original Image\n",
"plt.subplot(1, 2, 1)\n",
"plt.title('Tomogram')\n",
"plt.imshow(train_tomogram[90,],cmap='gray')\n",
"plt.axis('off')\n",
"\n",
"# Original Image\n",
"plt.subplot(1, 2, 2)\n",
"plt.title('Predicted Segmentation')\n",
"plt.imshow(train_target[90,])\n",
"plt.axis('off')\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 4: Measure Protein Coordinates from Segmentation Maps\n",
"\n",
"With the segmentation masks, we can filter potential "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"from deepfindET.entry_points import step4\n",
"\n",
"# Session ID for the Output Picks\n",
"picks_session_id = '0'\n",
"\n",
"segmentation_session_id = '0'\n",
"\n",
"segmentation_name = 'predict' \n",
"\n",
"min_protein_size = 0.4 \n",
"\n",
"path_output = f\"{copick_root.root_overlay}/ExperimentRuns\""
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Localization Parameters: {\n",
" \"input\": {\n",
" \"predict_config\": \"config_10439.json\",\n",
" \"voxel_size\": 10,\n",
" \"user_id\": \"deepfindET\",\n",
" \"segmentation_name\": \"predict\",\n",
" \"segmentation_session_id\": \"1\"\n",
" },\n",
" \"output\": {\n",
" \"user_id\": \"deepfindET\",\n",
" \"picks_session_id\": \"1\",\n",
" \"min_protein_size\": 0.4,\n",
" \"path_output\": \"overlay_10439\",\n",
" \"starfile_write_path\": null,\n",
" \"tomo_ids\": null\n",
" }\n",
"} \n",
"\n",
"Processing Run: 16172 (0)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|███████████████████████████████████████████| 76/76 [00:54<00:00, 1.40it/s]\n",
"100%|█████████████████████████████████████████| 166/166 [01:59<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 30/30 [00:21<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████████| 5/5 [00:03<00:00, 1.42it/s]\n",
"100%|█████████████████████████████████████████████| 2/2 [00:01<00:00, 1.42it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Error processing label 6 in tomo 16172: The number of observations cannot be determined on an empty distance matrix.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████████| 9/9 [00:06<00:00, 1.38it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16173 (1)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|███████████████████████████████████████████| 76/76 [00:54<00:00, 1.38it/s]\n",
"100%|█████████████████████████████████████████| 151/151 [01:48<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 18/18 [00:12<00:00, 1.42it/s]\n",
"100%|███████████████████████████████████████████| 10/10 [00:07<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████████| 1/1 [00:00<00:00, 1.35it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Error processing label 6 in tomo 16173: Found array with 0 sample(s) (shape=(0, 3)) while a minimum of 1 is required by check_pairwise_arrays.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████████| 8/8 [00:05<00:00, 1.38it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16174 (2)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████| 178/178 [02:07<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████| 316/316 [03:46<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████| 235/235 [02:48<00:00, 1.40it/s]\n",
"100%|███████████████████████████████████████████| 88/88 [01:02<00:00, 1.40it/s]\n",
"100%|███████████████████████████████████████████| 28/28 [00:20<00:00, 1.38it/s]\n",
"100%|███████████████████████████████████████████| 10/10 [00:07<00:00, 1.39it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16175 (3)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████| 179/179 [02:07<00:00, 1.40it/s]\n",
"100%|█████████████████████████████████████████| 347/347 [04:10<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████| 255/255 [03:02<00:00, 1.40it/s]\n",
"100%|███████████████████████████████████████████| 91/91 [01:04<00:00, 1.40it/s]\n",
"100%|███████████████████████████████████████████| 27/27 [00:19<00:00, 1.40it/s]\n",
"100%|█████████████████████████████████████████████| 9/9 [00:06<00:00, 1.36it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16176 (4)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████| 172/172 [02:02<00:00, 1.40it/s]\n",
"100%|█████████████████████████████████████████| 289/289 [03:26<00:00, 1.40it/s]\n",
"100%|█████████████████████████████████████████| 261/261 [03:06<00:00, 1.40it/s]\n",
"100%|███████████████████████████████████████████| 85/85 [01:00<00:00, 1.40it/s]\n",
"100%|███████████████████████████████████████████| 28/28 [00:20<00:00, 1.40it/s]\n",
"100%|███████████████████████████████████████████| 10/10 [00:07<00:00, 1.39it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16177 (5)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████| 218/218 [02:36<00:00, 1.40it/s]\n",
"100%|█████████████████████████████████████████| 312/312 [03:43<00:00, 1.40it/s]\n",
"100%|█████████████████████████████████████████| 243/243 [02:54<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 90/90 [01:04<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 28/28 [00:19<00:00, 1.40it/s]\n",
"100%|█████████████████████████████████████████████| 9/9 [00:06<00:00, 1.40it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16178 (6)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████| 181/181 [02:10<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████| 336/336 [04:00<00:00, 1.40it/s]\n",
"100%|█████████████████████████████████████████| 237/237 [02:50<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 85/85 [01:00<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 28/28 [00:19<00:00, 1.40it/s]\n",
"100%|███████████████████████████████████████████| 11/11 [00:07<00:00, 1.40it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16179 (7)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████| 200/200 [02:23<00:00, 1.40it/s]\n",
"100%|█████████████████████████████████████████| 334/334 [03:59<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████| 237/237 [02:50<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 86/86 [01:01<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 29/29 [00:21<00:00, 1.37it/s]\n",
"100%|███████████████████████████████████████████| 11/11 [00:08<00:00, 1.37it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16180 (8)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████| 195/195 [02:19<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████| 337/337 [04:00<00:00, 1.40it/s]\n",
"100%|█████████████████████████████████████████| 242/242 [02:53<00:00, 1.40it/s]\n",
"100%|███████████████████████████████████████████| 84/84 [01:00<00:00, 1.38it/s]\n",
"100%|███████████████████████████████████████████| 29/29 [00:20<00:00, 1.40it/s]\n",
"100%|███████████████████████████████████████████| 10/10 [00:07<00:00, 1.40it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16181 (9)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████| 188/188 [02:15<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████| 330/330 [03:57<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████| 231/231 [02:46<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 90/90 [01:04<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 27/27 [00:19<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████████| 9/9 [00:06<00:00, 1.39it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16182 (10)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████| 180/180 [02:09<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████| 305/305 [03:38<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████| 217/217 [02:36<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 85/85 [01:01<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 28/28 [00:20<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████████| 9/9 [00:06<00:00, 1.38it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16183 (11)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████| 179/179 [02:08<00:00, 1.40it/s]\n",
"100%|█████████████████████████████████████████| 340/340 [04:03<00:00, 1.40it/s]\n",
"100%|█████████████████████████████████████████| 213/213 [02:32<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 90/90 [01:04<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 28/28 [00:19<00:00, 1.40it/s]\n",
"100%|███████████████████████████████████████████| 10/10 [00:07<00:00, 1.38it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16184 (12)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|███████████████████████████████████████████| 70/70 [00:50<00:00, 1.37it/s]\n",
"100%|█████████████████████████████████████████| 201/201 [02:23<00:00, 1.40it/s]\n",
"100%|███████████████████████████████████████████| 36/36 [00:26<00:00, 1.38it/s]\n",
"100%|█████████████████████████████████████████████| 6/6 [00:04<00:00, 1.41it/s]\n",
"100%|█████████████████████████████████████████████| 1/1 [00:00<00:00, 1.40it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Error processing label 6 in tomo 16184: Found array with 0 sample(s) (shape=(0, 3)) while a minimum of 1 is required by check_pairwise_arrays.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████████| 8/8 [00:05<00:00, 1.40it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16185 (13)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████| 196/196 [02:21<00:00, 1.38it/s]\n",
"100%|█████████████████████████████████████████| 305/305 [03:39<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████| 220/220 [02:37<00:00, 1.40it/s]\n",
"100%|███████████████████████████████████████████| 82/82 [00:58<00:00, 1.40it/s]\n",
"100%|███████████████████████████████████████████| 28/28 [00:20<00:00, 1.37it/s]\n",
"100%|█████████████████████████████████████████████| 9/9 [00:06<00:00, 1.37it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16186 (14)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████| 164/164 [01:59<00:00, 1.37it/s]\n",
"100%|█████████████████████████████████████████| 270/270 [03:15<00:00, 1.38it/s]\n",
"100%|█████████████████████████████████████████| 205/205 [02:28<00:00, 1.38it/s]\n",
"100%|███████████████████████████████████████████| 88/88 [01:02<00:00, 1.40it/s]\n",
"100%|███████████████████████████████████████████| 29/29 [00:20<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████████| 9/9 [00:06<00:00, 1.37it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16187 (15)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████| 166/166 [02:01<00:00, 1.37it/s]\n",
"100%|█████████████████████████████████████████| 319/319 [03:52<00:00, 1.37it/s]\n",
"100%|█████████████████████████████████████████| 232/232 [02:47<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 87/87 [01:03<00:00, 1.38it/s]\n",
"100%|███████████████████████████████████████████| 29/29 [00:20<00:00, 1.40it/s]\n",
"100%|███████████████████████████████████████████| 10/10 [00:07<00:00, 1.40it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16188 (16)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████| 184/184 [02:11<00:00, 1.40it/s]\n",
"100%|█████████████████████████████████████████| 293/293 [03:31<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████| 204/204 [02:28<00:00, 1.38it/s]\n",
"100%|███████████████████████████████████████████| 90/90 [01:05<00:00, 1.38it/s]\n",
"100%|███████████████████████████████████████████| 28/28 [00:20<00:00, 1.37it/s]\n",
"100%|█████████████████████████████████████████████| 9/9 [00:06<00:00, 1.36it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16189 (17)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████| 159/159 [01:54<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████| 307/307 [03:39<00:00, 1.40it/s]\n",
"100%|█████████████████████████████████████████| 216/216 [02:35<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 92/92 [01:06<00:00, 1.38it/s]\n",
"100%|███████████████████████████████████████████| 29/29 [00:21<00:00, 1.38it/s]\n",
"100%|█████████████████████████████████████████████| 8/8 [00:05<00:00, 1.41it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16190 (18)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████| 197/197 [02:21<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████| 286/286 [03:27<00:00, 1.38it/s]\n",
"100%|█████████████████████████████████████████| 199/199 [02:23<00:00, 1.38it/s]\n",
"100%|███████████████████████████████████████████| 83/83 [00:59<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 28/28 [00:19<00:00, 1.41it/s]\n",
"100%|█████████████████████████████████████████████| 9/9 [00:06<00:00, 1.37it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16191 (19)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████| 190/190 [02:16<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████| 269/269 [03:12<00:00, 1.40it/s]\n",
"100%|█████████████████████████████████████████| 198/198 [02:22<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 85/85 [01:01<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 29/29 [00:20<00:00, 1.41it/s]\n",
"100%|█████████████████████████████████████████████| 9/9 [00:06<00:00, 1.40it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16192 (20)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████| 166/166 [02:00<00:00, 1.38it/s]\n",
"100%|█████████████████████████████████████████| 409/409 [04:52<00:00, 1.40it/s]\n",
"100%|█████████████████████████████████████████| 215/215 [02:34<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 91/91 [01:04<00:00, 1.40it/s]\n",
"100%|███████████████████████████████████████████| 28/28 [00:20<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████████| 9/9 [00:06<00:00, 1.40it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16193 (21)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████| 139/139 [01:40<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████| 448/448 [05:22<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████| 204/204 [02:26<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 91/91 [01:05<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 28/28 [00:20<00:00, 1.38it/s]\n",
"100%|███████████████████████████████████████████| 11/11 [00:07<00:00, 1.41it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16194 (22)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████| 156/156 [01:51<00:00, 1.40it/s]\n",
"100%|█████████████████████████████████████████| 421/421 [05:01<00:00, 1.40it/s]\n",
"100%|█████████████████████████████████████████| 218/218 [02:37<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 85/85 [01:01<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 28/28 [00:19<00:00, 1.41it/s]\n",
"100%|█████████████████████████████████████████████| 8/8 [00:05<00:00, 1.41it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16195 (23)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████| 188/188 [02:14<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████| 431/431 [05:08<00:00, 1.40it/s]\n",
"100%|█████████████████████████████████████████| 281/281 [03:20<00:00, 1.40it/s]\n",
"100%|███████████████████████████████████████████| 89/89 [01:03<00:00, 1.40it/s]\n",
"100%|███████████████████████████████████████████| 28/28 [00:19<00:00, 1.41it/s]\n",
"100%|███████████████████████████████████████████| 10/10 [00:07<00:00, 1.37it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16196 (24)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████| 177/177 [02:06<00:00, 1.40it/s]\n",
"100%|█████████████████████████████████████████| 394/394 [04:41<00:00, 1.40it/s]\n",
"100%|█████████████████████████████████████████| 263/263 [03:08<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 84/84 [01:00<00:00, 1.40it/s]\n",
"100%|███████████████████████████████████████████| 29/29 [00:20<00:00, 1.40it/s]\n",
"100%|███████████████████████████████████████████| 10/10 [00:07<00:00, 1.39it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16197 (25)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████| 193/193 [02:18<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████| 376/376 [04:29<00:00, 1.39it/s]\n",
"100%|█████████████████████████████████████████| 259/259 [03:10<00:00, 1.36it/s]\n",
"100%|███████████████████████████████████████████| 89/89 [01:03<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 28/28 [00:20<00:00, 1.38it/s]\n",
"100%|███████████████████████████████████████████| 11/11 [00:07<00:00, 1.38it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Run: 16198 (26)/27\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████| 178/178 [02:07<00:00, 1.40it/s]\n",
"100%|█████████████████████████████████████████| 366/366 [04:22<00:00, 1.40it/s]\n",
"100%|█████████████████████████████████████████| 260/260 [03:06<00:00, 1.39it/s]\n",
"100%|███████████████████████████████████████████| 89/89 [01:03<00:00, 1.40it/s]\n",
"100%|███████████████████████████████████████████| 28/28 [00:20<00:00, 1.38it/s]\n",
"100%|███████████████████████████████████████████| 10/10 [00:07<00:00, 1.37it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extraction of Particle Coordinates Complete!\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"# Derived from https://github.com/copick/DeepFindET/blob/main/deepfindET/entry_points/step4.py\n",
"\n",
"import deepfindET.utils.copick_tools as tools\n",
"import deepfindET.utils.evaluate as evaluate\n",
"import scipy.ndimage as ndimage\n",
"from tqdm import tqdm\n",
"\n",
"# Currently Filtering Process always finds coordinate at (cx,cy,cz) - center coordinate\n",
"# This seems to always be at the first row, so we can remove it \n",
"remove_index = 0\n",
"\n",
"# Extract Protein Coordinates from the Segmentation Masks\n",
"def extract_coords(pickable_object, copick_run):\n",
" labelmap = tools.get_copick_segmentation(copick_run, segmentation_name, user_id, segmentation_session_id)[:]\n",
" label = pickable_object.label\n",
" protein_name = pickable_object.name\n",
" label_objs, _ = ndimage.label(labelmap == label)\n",
"\n",
" # Filter Candidates based on Object Size\n",
" # Get the sizes of all objects\n",
" object_sizes = np.bincount(label_objs.flat)\n",
"\n",
" # Filter the objects based on size\n",
" min_object_size = 4/3 * np.pi * ((pickable_object.radius/voxel_size)**2) * min_protein_size\n",
" valid_objects = np.where(object_sizes > min_object_size)[0] \n",
"\n",
" # Estimate Coordiantes from CoM for LabelMaps\n",
" deepFinderCoords = []\n",
" for object_num in tqdm(valid_objects):\n",
" com = ndimage.center_of_mass(label_objs == object_num)\n",
" swapped_com = (com[2], com[1], com[0])\n",
" deepFinderCoords.append(swapped_com)\n",
" deepFinderCoords = np.array(deepFinderCoords) \n",
"\n",
" # For some reason, consistently extracting center coordinate\n",
" # Remove the row with the closest index\n",
" deepFinderCoords = np.delete(deepFinderCoords, remove_index, axis=0) \n",
"\n",
" # Estimate Distance Threshold Based on 1/2 of Particle Diameter\n",
" threshold = np.ceil( pickable_object.radius / (voxel_size * 3) )\n",
"\n",
" try: \n",
" # Remove Double Counted Coordinates\n",
" deepFinderCoords = evaluate.remove_repeated_picks(deepFinderCoords, threshold)\n",
"\n",
" # Append Euler Angles to Coordinates [ Expand Dimensions from Nx3 -> Nx6 ]\n",
" deepFinderCoords = np.concatenate((deepFinderCoords, np.zeros(deepFinderCoords.shape)),axis=1)\n",
"\n",
" # Convert from Voxel to Physical Units\n",
" deepFinderCoords *= voxel_size\n",
"\n",
" except Exception as e:\n",
" print(f\"Error processing label {label} in tomo {copick_run}: {e}\")\n",
" deepFinderCoords = np.array([]).reshape(0,6)\n",
"\n",
" # Save Picks in Copick Format / Directory \n",
" tools.write_copick_output(protein_name, copick_run.meta.name, deepFinderCoords, path_output, pickMethod=user_id, sessionID = picks_session_id)\n",
" \n",
" \n",
"for run in copick_root.runs:\n",
" print(f\"Run {run}\")\n",
" for pickable_object in copick_root.pickable_objects:\n",
" print(pickable_object.name)\n",
" if pickable_object.is_particle:\n",
" extract_coords(pickable_object, run)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 5: Generate a Kaggle submission file\n",
"\n",
"Now we need to generate a submission.csv for Kaggle"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Evaluation for adp-mitochondrial: \n",
"{'precision': 0.8564102564102564, 'recall': 0.7076271186440678, 'f1_score': 0.7749419953596287, 'accuracy': 0.6325757575757576, 'true_positives': 167, 'false_positives': 28, 'false_negatives': 69}\n",
"\n",
"Evaluation for alkaline-phosphate: \n",
"{'precision': 0.1611842105263158, 'recall': 0.0394842868654311, 'f1_score': 0.06343042071197412, 'accuracy': 0.032754010695187165, 'true_positives': 49, 'false_positives': 255, 'false_negatives': 1192}\n",
"\n",
"Evaluation for nucleosome: \n",
"{'precision': 0.8036529680365296, 'recall': 0.8543689320388349, 'f1_score': 0.828235294117647, 'accuracy': 0.7068273092369478, 'true_positives': 176, 'false_positives': 43, 'false_negatives': 30}\n",
"\n",
"Evaluation for ribosome: \n",
"{'precision': 0.9259259259259259, 'recall': 0.8522727272727273, 'f1_score': 0.8875739644970415, 'accuracy': 0.7978723404255319, 'true_positives': 75, 'false_positives': 6, 'false_negatives': 13}\n",
"\n",
"Evaluation for vault: \n",
"{'precision': 1.0, 'recall': 1.0, 'f1_score': 1.0, 'accuracy': 1.0, 'true_positives': 27, 'false_positives': 0, 'false_negatives': 0}\n",
"\n",
"Evaluation for virus-like-capsid: \n",
"{'precision': 1.0, 'recall': 1.0, 'f1_score': 1.0, 'accuracy': 1.0, 'true_positives': 8, 'false_positives': 0, 'false_negatives': 0}\n",
"\n"
]
}
],
"source": [
"import csv\n",
"import os\n",
"\n",
"os.listdir(\"/kaggle/input/czii-cryo-et-object-identification/test/static/ExperimentRuns\")\n",
"\n",
"results = []\n",
"pick_id = 0\n",
"\n",
"# id,experiment,particle_type,x,y,z\n",
"for run in copick_root.runs:\n",
" run_id = run.meta.name\n",
" for particle_type in copick_root.pickable_objects:\n",
" picks = run.get_picks(particle_type.name, user_id=\"deepfindET\")\n",
" if picks:\n",
" picks = picks[0]\n",
" points = picks.points\n",
" for point in points: \n",
" row = [pick_id, run_id, particle_type.name, point.location.x, point.location.y, point.location.z]\n",
" results.append(row)\n",
" pick_id += 1\n",
"\n",
"print(f\"Found {len(results)} picks\")\n",
"\n",
"# Define CSV output file path\n",
"output_csv_path = \"/kaggle/working/submission.csv\"\n",
"\n",
"# Write results to CSV\n",
"with open(output_csv_path, mode='w', newline='') as file:\n",
" writer = csv.writer(file)\n",
" # Write header\n",
" writer.writerow([\"id\", \"experiment\", \"particle_type\", \"x\", \"y\", \"z\"])\n",
" # Write data rows\n",
" writer.writerows(results)\n",
" \n",
"\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| Unknown |
3D | febiosoftware/FEBio | BUILD.md | .md | 12,568 | 117 | # FEBio Build Guide
### Table of contents
- [Prerequisites](#prereq)
- [Running CMake](#runCMake)
- [Building FEBio](#build)
- [Limitations of CMake](#limits)
- [Troubleshooting](#trouble)
## Prerequisites <a name="prereq"></a>
### CMake
FEBio's build process utilizes CMake, an open-source, cross-platform tool designed to streamline the configuration of the build environment. The CMake script in this repository will help you to locate necessary third party libraries on your machine, set up include and library paths, and allow you to choose which of FEBio’s features you would like to include in your build.
Please download the latest release of CMake from https://cmake.org/, and install it on your machine before proceeding. Many Linux distributions come with CMake pre-installed, or have CMake available through their package managers.
### Intel Math Kernel Library
FEBio requires the Intel Math Kernel Library (MKL) in order to utilize the Pardiso linear solver and some of the iterative linear solvers. This library can be downloaded as part of the Intel oneAPI Base Toolkit from Intel's website: https://software.intel.com/content/www/us/en/develop/tools/oneapi/base-toolkit.html. In the absence of MKL, FEBio will default to using the Skyline linear solver. However, the Pardiso solver is significantly faster and more memory-efficient than the Skyline solver, and it is strongly recommended that the Pardiso solver be used.
### Additional Third Party Packages
FEBio makes use of the following third party packages to add additional functionality. If you do not need the functionality provided by a given package, you can still compile FEBio without it. The libraries below are organized according to the type of functionality they add.
* The Lourakis levmar routine is required by FEBio in order for it to perform its parameter optimization functions. The source for this library can be downloaded from http://users.ics.forth.gr/~lourakis/levmar/.
* MMG is used in the adaptive mesh refinement framework (the FEAMR FEBio library). FEBio only uses the mmg3d component of MMG. To use this library, you will need to download the source and compile it yourself. The source for this library can be downloaded from [MMG's GitHub account](https://github.com/MmgTools/mmg).
* HYPRE is used by FEBio for the BoomerAMG iterative linear solver, which is an algebraic multi-grid solver. To use this library, you will need to download the source and compile it yourself. The source for this library can be downloaded from [HYPRE's GitHub account](https://github.com/hypre-space/hypre).
* zlib is an open-source, lossless data-compression library that is used by FEBio to optionally compress plot files to save disc space. This library is generally pre-installed on macOS, and most Linux distributions. For Windows, you will need to download the latest source from zlib's website, and compile the library yourself.
* FFTW: Fastest Fourier Transform in the West. Alternate library to perform FFTs since certain solvers use conflicting versions of MKL. The library can be found at [FFTW's wegpage](https://fftw.org/)
## Running CMake <a name="runCMake"></a>
CMake is used to configure the build environment for FEBio on your machine. It can be used to generate a Microsoft Visual Studio Solution on Windows, an XCode Project on macOS, and a set of makefiles to be used with the GNU Make tool on Linux.
CMake generates a large number of configuration files that can cause the build directory to become cluttered. It is therefore strongly recommended that you do an [out-of-source build](https://gitlab.kitware.com/cmake/community/-/wikis/FAQ#what-is-an-out-of-source-build) by pointing CMake to an empty directory. The _cbuild_ directory in the FEBio repository is provided for this purpose. If for some reason you find yourself needing to clear out your CMake configuration for FEBio and start this process from scratch, all you will need to do is delete everything in that build directory.
### CMake GUI
<img src="Documentation/BuildGuide/CMakeGUI.png" alt="CMake GUI" width="75%">
<!--  -->
On Windows, and macOS CMake is run using the CMake graphical user interface (GUI). The CMake GUI is also available on Linux, but is generally installed separately, and so the command line interface (CLI) version, _ccmake_ is generally used (see below).
To start the configuration process, enter the path to the root directory of the FEBio repository that you've downloaded onto your machine into the box labeled _Where is the source code:_. To insure that you are doing an out-of-source build, enter the path to the _cbuild_ directory of your FEBio repository in the box labeled _Where to build the binaries:_. You may also locate these directories using a file browser by clicking on the _Browse_ buttons to the right of these fields.
### ccmake
<img src="Documentation/BuildGuide/ccmake.png" alt="ccmake" width="75%">
<!--  -->
If you are running Linux and have not installed the CMake GUI, there are two ways to run cmake. You can run the command `cmake` which will call CMake and run through the configuration and generation processes automatically. This method, however does not allow for interactivity, and so it is highly recommended that you instead run the command `ccmake`. This will run an interactive version of CMake with an in-terminal GUI as shown above. Using ccmake should allow you to follow along with the rest of this tutorial. To start an out-of-source build with ccmake, open a terminal in the _cbuild_ directory of your local copy fo the FEBio Studio repository and run the following command:
```
ccmake ..
```
### First Configuration
The configuration step in the CMake build process runs the script defined in `CMakeLists.txt` located in the root directory of the FEBio repository. This script does several things:
* Attempts to locate MKL, and any other third party packages that FEBio Studio uses.
* Automatically enables or disables FEBio features based on which libraries it was able to find.
* Automatically sets up include and library paths for your build system based on the libraries that it found, and the features that have been enabled.
To run the configuration process click the _Configure_ button in the lower left part of the GUI, or type `c` if you are using ccmake. If you are running the CMake GUI, you will be asked to choose a generator for the project. On Windows, choose the version of MSVC that you have installed and click _Finish_. On macOS, leave the default value and click _Finish_. CMake will now run the configuration process, the output of which can be seen in the text field at the bottom of the GUI. If all goes well, new fields will be added in red to the GUI, and it should look something like the image below:
<img src="Documentation/BuildGuide/CMakeGUIFull.png" alt="ccmake" width="75%">
<!--  -->
After running the configuration process, the CMake GUI will populate with several build options that can be toggled on or off, each corresponding to one of the third party packages listed above. Building FEBio with a given build option enabled requires the corresponding third party packages to be installed on your machine and to be located by CMake.
The CMake script will do its best to automatically locate these packages, but if it is unable to do so, you will have to point CMake to the packages manually for each package that you'd like to use.
### Manually Locating MKL
If CMake is unable to locate MKL automatically, the `USE_MKL` option will be automatically turned off. A simple mechanism is provided for you to help the script to locate your MKL installtion. A variable called `MKLROOT` will have appeared in the CMake GUI. Enter the path to the _mkl_ directory of your Intel oneAPI installation as the value for the variable, then run the configuration step again. If you've correctly entered the path, it should find the necessary components of MKL. At this point you will need to manually turn the `USE_MKL` option back on.
### Manually Locating Other Packages
If CMake is unable to find any of the other third party packages on your system, it will automatically disable the corresponding build option. It will also make visible the fields for the include and library paths for the missing packages. In order to build with any of these options, you will need to manually edit the include and library paths for the required packages. The include path for a given package should point to the directory containing that package's header files, and the library path should point to the library file. Once you have updated the paths for the required packages, you then need to manually toggle the option back on.
### Project Generation
Once the desired optional packages have been located, and their corresponding build options have been enabled, it is time to generate the platform-specific build files. It's always a good idea to run Configure one more time before you generate the build files. This will make sure that the CMakeLists script catches any errors that you may have introduced by manually changing paths, or toggling build options. Once you've run Configure again, click the _Generate_ button (or type `g` if you're using ccmake). On Windows this will generate a Visual Studio Project, on macOS this will generate an XCode Project, and on Linux this will generate a Makefile. If you're running Windows or macOS, you can click the _Open Project_ button and it will automatically open the created project.
## Building FEBio <a name="build"></a>
### Windows
Once you have the Visual Studio project open, you can choose whether you'd like to build a debug or a release version of the software, and then start the build process by either clicking on the Play button, or by pressing _F5_. After a successful build, the software should launch automatically.
### macOS
Once you have the XCode project open, you'll want to change the build target from `ALL_BUILD` to `FEBio`. Do this by clicking on the button that says `ALL_BUILD` in the upper left corner of XCode, next to the Play and Stop buttons. XCode will fully build FEBio with either target selected, but this will ensure that XCode will automatically launch FEBio after it's been built. Then you can start the build by either click on the Play button, or by pressing ⌘R. After a successful build, the software should launch automatically.
### Linux
Once the Makefiles have been generated, open a terminal in the _cbuild_ directory, and run `make`. If your machine has multiple cores, you can increase the speed of the build by passing a `-j` flag to _make_, followed by the number of threads you want _make_ to use (e.g. `make -j4`). Please note that this will only increase the speed of the build, and will in no way affect the final binary. After a successful build, the compiled binary can be found in the _bin_ subdirectory.
## Limitations of CMake <a name="limits"></a>
CMake is a useful tool for automating cross-platform builds, but it is not without its limitations:
* CMake is unable to create project files or makefiles that can automatically detect the presence of new source files. If you update your local repository after a new FEBio release, or if you modify FEBio's source and add new source files, you will have to rerun CMake in order to insure that any new source files are included in the build.
* On Linux, the type of build (e.g. debug, release, etc) is determined by CMake during the generation process, since the related options are baked into the resulting makefiles. To change the type of build you are building, you must rerun CMake and select the desired build type.
## Troubleshooting <a name="trouble"></a>
* If you get errors that look something like this<br><br>Could not find HYPRE library. Check HYPRE_LIB.<br><br>This means that CMake is unable to locate the library associated with a currently active build option. To fix this issue, ensure that the CMake variable for that package's library is pointing to the correct location on your machine, and that the library file exists in that location. If CMake is still unable to find the required library, you may either have to rebuild or reinstall the third-party package, or disable the build option that uses it.
* If you run into other issues while building FEBio, please visit [our forums](https://forums.febio.org/) for more help.
| Markdown |
3D | febiosoftware/FEBio | ROADMAP.md | .md | 2,318 | 35 | # Roadmap
This roadmap briefly discusses some planned milestones for the FEBio project.
Please check out our [contribution guidelines](CONTRIBUTING.md) and [code of conduct](CODE_OF_CONDUCT.md) for information on how to contribute to the FEBio project.
## Main Milestones
The main focus at this point is the implementation of the main aims of the NIH RO1 grant that funds FEBio development. The estimated completion date for these milestones is summer of 2023, although some functionality may be available sooner.
### Formulate and implement a computationally efficient damage and fatigue failure framework for fibrous tissues.
This aim focuses on the development and implementation of a novel theoretical framework for modeling damage and fatigue failure mechanics. This work will extend the remodeling and growth framework already implemented in FEBio to include damage and failure of soft tissues.
### Expand the multiphysics capabilities of FEBio to include solute transport, reactions involving solutes, and tissue growth and remodeling in fluid domains at their interfaces.
In this aim, the multiphisics capabilities of FEBio will be extended to include various transport and reaction modeling capabilites at solid-fluid boundaries.
### Integrate the use of image data through the entire simulation pipeline, from model setup to model validation.
This aim proposes to include image data throughout the entire modeling pipeline, from pre-processing to post-processing. Most of this development will actually occur in FEBio Studio, but the focus in FEBio is the representation of heterogeneous model parameters (e.g. material parameters) that are extracted from image data.
## Long term milestones
The following topics are currently under consideration as potential milestones, depending on user interest and feasibility.
### topology optimization
FEBio already has optimization algorithms for optimizing model parameters while preserving the geometry. In this milestone we would look into extending this framework to allow for topology changes.
### Integration with the Julia programming language.
The Julia programming language appears to be an interesting potential path for extending FEBio's capabilities through scripting, or as a mechanism for integrating FEBio with other tools.
| Markdown |
3D | febiosoftware/FEBio | CONTRIBUTING.md | .md | 2,003 | 29 | # Contribution Guidelines
## Reporting issues
- **Search for existing issues.** Please check to see if someone else has reported the same issue.
- **Share as much information as possible.** Include operating system and version, browser and version. Also, include steps to reproduce the bug.
## Project Setup
Refer to the [README](README.md).
## Code Style
### Variable Naming
Not all current code follows the conventions below but these will be followed for future developments.
- Maximize the use of semantic and descriptive variables names (e.g. `faceIndices` not `fcInd` or `fi`). Avoid abbreviations except in cases of industry wide usage. In some cases non-descriptive and short variable names are exceptable for instance vertices (points), faces, edges, colors and logic arrays may be denoted `V`, `F`, `E`, `C`, `L`. Furthermore, if a mathematrical symbol or letter is commonly used for some entity it may be acceptable to use short names e.g. coordinates may be referred to as `X`, `Y` and `Z` and image coordinates of indices may be referred to as `I`, `J` and `K`.
## Testing
Proving example files as well as a description of the expected outcomes. This can be in the form of a log file or data file that contain partial solution data.
## Pull requests
- Try not to pollute your pull request with unintended changes – keep them simple and small. If possible, squash your commits.
- Try to share how your code has been tested before submitting a pull request.
- If your PR resolves an issue, include **closes #ISSUE_NUMBER** in your commit message (or a [synonym](https://help.github.com/articles/closing-issues-via-commit-messages)).
- Review
- If your PR is ready for review, another contributor will be assigned to review your PR
- The reviewer will accept or comment on the PR.
- If needed address the comments left by the reviewer. Once you're ready to continue the review, ping the reviewer in a comment.
- Once accepted your code will be merged to `master`
| Markdown |
3D | febiosoftware/FEBio | CODE_OF_CONDUCT.md | .md | 5,217 | 128 | # Contributor Covenant Code of Conduct
## Our Pledge
We as members, contributors, and leaders pledge to make participation in our
community a harassment-free experience for everyone, regardless of age, body
size, visible or invisible disability, ethnicity, sex characteristics, gender
identity and expression, level of experience, education, socio-economic status,
nationality, personal appearance, race, religion, or sexual identity
and orientation.
We pledge to act and interact in ways that contribute to an open, welcoming,
diverse, inclusive, and healthy community.
## Our Standards
Examples of behavior that contributes to a positive environment for our
community include:
* Demonstrating empathy and kindness toward other people
* Being respectful of differing opinions, viewpoints, and experiences
* Giving and gracefully accepting constructive feedback
* Accepting responsibility and apologizing to those affected by our mistakes,
and learning from the experience
* Focusing on what is best not just for us as individuals, but for the
overall community
Examples of unacceptable behavior include:
* The use of sexualized language or imagery, and sexual attention or
advances of any kind
* Trolling, insulting or derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or email
address, without their explicit permission
* Other conduct which could reasonably be considered inappropriate in a
professional setting
## Enforcement Responsibilities
Community leaders are responsible for clarifying and enforcing our standards of
acceptable behavior and will take appropriate and fair corrective action in
response to any behavior that they deem inappropriate, threatening, offensive,
or harmful.
Community leaders have the right and responsibility to remove, edit, or reject
comments, commits, code, wiki edits, issues, and other contributions that are
not aligned to this Code of Conduct, and will communicate reasons for moderation
decisions when appropriate.
## Scope
This Code of Conduct applies within all community spaces, and also applies when
an individual is officially representing the community in public spaces.
Examples of representing our community include using an official e-mail address,
posting via an official social media account, or acting as an appointed
representative at an online or offline event.
## Enforcement
Instances of abusive, harassing, or otherwise unacceptable behavior may be
reported to the community leaders responsible for enforcement at
[info@febio.org].
All complaints will be reviewed and investigated promptly and fairly.
All community leaders are obligated to respect the privacy and security of the
reporter of any incident.
## Enforcement Guidelines
Community leaders will follow these Community Impact Guidelines in determining
the consequences for any action they deem in violation of this Code of Conduct:
### 1. Correction
**Community Impact**: Use of inappropriate language or other behavior deemed
unprofessional or unwelcome in the community.
**Consequence**: A private, written warning from community leaders, providing
clarity around the nature of the violation and an explanation of why the
behavior was inappropriate. A public apology may be requested.
### 2. Warning
**Community Impact**: A violation through a single incident or series
of actions.
**Consequence**: A warning with consequences for continued behavior. No
interaction with the people involved, including unsolicited interaction with
those enforcing the Code of Conduct, for a specified period of time. This
includes avoiding interactions in community spaces as well as external channels
like social media. Violating these terms may lead to a temporary or
permanent ban.
### 3. Temporary Ban
**Community Impact**: A serious violation of community standards, including
sustained inappropriate behavior.
**Consequence**: A temporary ban from any sort of interaction or public
communication with the community for a specified period of time. No public or
private interaction with the people involved, including unsolicited interaction
with those enforcing the Code of Conduct, is allowed during this period.
Violating these terms may lead to a permanent ban.
### 4. Permanent Ban
**Community Impact**: Demonstrating a pattern of violation of community
standards, including sustained inappropriate behavior, harassment of an
individual, or aggression toward or disparagement of classes of individuals.
**Consequence**: A permanent ban from any sort of public interaction within
the community.
## Attribution
This Code of Conduct is adapted from the [Contributor Covenant][homepage],
version 2.0, available at
https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
Community Impact Guidelines were inspired by [Mozilla's code of conduct
enforcement ladder](https://github.com/mozilla/diversity).
[homepage]: https://www.contributor-covenant.org
For answers to common questions about this code of conduct, see the FAQ at
https://www.contributor-covenant.org/faq. Translations are available at
https://www.contributor-covenant.org/translations. | Markdown |
3D | febiosoftware/FEBio | infrastructure/common/linux/apt.sh | .sh | 772 | 23 | #!/bin/bash
set -e
export DEBIAN_FRONTEND=noninteractive
SUDO=""
if command -v sudo &> /dev/null
then
SUDO=$(which sudo)
fi
$SUDO apt-get update
$SUDO apt-get install linux-headers-generic -y
$SUDO apt-get install software-properties-common wget gpg sudo -y
$SUDO apt-get update --fix-missing
wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | sudo tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null
echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | sudo tee /etc/apt/sources.list.d/oneAPI.list
$SUDO apt-get update
DEBIAN_FRONTEND=noninteractive $SUDO apt-get install lua5.3 -y
xargs $SUDO apt-get install </tmp/linux/packages.txt -y
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/linux/aws.sh | .sh | 203 | 8 | #!/bin/bash
# set -e
export DEBIAN_FRONTEND=noninteractive
sudo curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip"
sudo unzip awscliv2.zip
sudo ./aws/install
aws --version
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/linux/cmake.sh | .sh | 141 | 8 | #!/bin/bash
set -e
export DEBIAN_FRONTEND=noninteractive
sudo apt update
sudo apt install libssl3 cmake cmake-curses-gui -y
cmake --version
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/linux/git.sh | .sh | 218 | 13 | #!/bin/bash
set -e
export DEBIAN_FRONTEND=noninteractive
if command -v git &> /dev/null
then
git --version
fi
sudo add-apt-repository ppa:git-core/ppa -y
sudo apt-get update
sudo apt-get install git -y
git --version
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/linux/openapi.sh | .sh | 429 | 14 | #!/bin/bash
set -e
export DEBIAN_FRONTEND=noninteractive
SUDO=""
if command -v sudo &> /dev/null
then
SUDO=$(which sudo)
fi
INSTALLER="intel-oneapi-base-toolkit-2025.0.1.46_offline.sh"
aws s3 cp "s3://febiosoftware/linux/oneapi/${INSTALLER}" .
chmod +x ./$INSTALLER
$SUDO ./intel-oneapi-base-toolkit-2025.0.1.46_offline.sh -a --cli --silent --eula accept --action install --components intel.oneapi.lin.mkl.devel
rm ./$INSTALLER | Shell |
3D | febiosoftware/FEBio | infrastructure/common/linux/qt.sh | .sh | 226 | 12 | #!/bin/bash
set -e
export DEBIAN_FRONTEND=noninteractive
SUDO=""
if command -v sudo &> /dev/null
then
SUDO=$(which sudo)
fi
$SUDO pip install aqtinstall -v
$SUDO aqt install-qt --outputdir /opt/Qt linux desktop 6.9.3 -m all
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/linux/install-builder.sh | .sh | 381 | 15 | #!/bin/bash
set -ex
export DEBIAN_FRONTEND=noninteractive
pushd /tmp
INSTALLER="installbuilder-enterprise-23.11.0-linux-x64-installer.run"
aws s3 cp "s3://febiosoftware/linux/installbuilder/${INSTALLER}" .
chmod +x ./$INSTALLER
sudo ./$INSTALLER \
--mode unattended \
--installer-language en
sudo ln -s /opt/installbuilder-23.11.0/bin/builder /usr/local/bin/
builder --version
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/linux/dependencies/mmg.sh | .sh | 599 | 34 | #! /bin/bash
set -o errexit
set -o verbose
# shellcheck disable=1091
. ./common-functions.sh
MMG="https://github.com/MmgTools/mmg.git"
BRANCH="v5.7.3"
build_and_install() {
local source=$1
local branch=$2
git clone --depth 1 --branch "$branch" "$source" "$branch"
pushd $branch || exit 1
cmake . -B cmbuild \
-DCMAKE_INSTALL_PREFIX="/usr/local" \
-DCMAKE_POSITION_INDEPENDENT_CODE=ON
pushd cmbuild || exit 1
make -j "$(nproc)"
sudo make install
popd || exit 1
popd || exit 1
}
main() {
pushd "$BUILD_PATH" || exit 1
build_and_install "$MMG" "$BRANCH"
popd || exit 1
}
main
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/linux/dependencies/levmar.sh | .sh | 488 | 31 | #! /bin/bash
set -o errexit
set -o verbose
REPO=https://github.com/jturney/levmar.git
DIR=levmar
build_and_install() {
local repo=$1
local dir=$2
git clone $repo $dir
pushd $dir
cmake . -B cmbuild \
-DCMAKE_INSTALL_PREFIX="/usr/local" \
-DCMAKE_POSITION_INDEPENDENT_CODE=ON \
-DBUILD_DEMO:BOOLEAN=false
pushd cmbuild
make -j "$(nproc)"
sudo make install
popd || exit 1
popd || exit 1
}
main() {
pushd $BUILD_PATH
build_and_install $REPO $DIR
popd || exit 1
}
main
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/linux/dependencies/netgen.sh | .sh | 1,099 | 51 | #! /bin/bash
set -o errexit
set -o verbose
NETGEN="https://github.com/NGSolve/netgen.git"
BRANCH="v6.2.2406"
build_and_install() {
local source=$1
local branch=$2
git clone --depth 1 --branch "$branch" "$source" "$branch"
pushd $branch || exit 1
git submodule update --init --recursive
cmake . -LA -B cmbuild \
-DCMAKE_BUILD_TYPE=Release \
-DCMAKE_INSTALL_PREFIX="/usr/local" \
-DUSE_CCACHE:BOOL=OFF \
-DUSE_CGNS:BOOL=OFF \
-DUSE_CSG:BOOL=ON \
-DUSE_GEOM2D:BOOL=ON \
-DUSE_GUI:BOOL=OFF \
-DUSE_INTERFACE:BOOL=ON \
-DUSE_INTERNAL_TCL:BOOL=OFF \
-DUSE_JPEG:BOOL=OFF \
-DUSE_MPEG:BOOL=OFF \
-DUSE_MPI:BOOL=OFF \
-DUSE_MPI4PY:BOOL=OFF \
-DUSE_NATIVE_ARCH:BOOL=OFF \
-DUSE_NUMA:BOOL=OFF \
-DUSE_OCC:BOOL=ON \
-DUSE_PYTHON:BOOL=OFF \
-DUSE_STLGEOM:BOOL=ON \
-DUSE_SUPERBUILD:BOOL=OFF \
-DENABLE_CPP_CORE_GUIDELINES_CHECK:BOOL=OFF \
-DENABLE_UNIT_TESTS:BOOL=OFF
pushd cmbuild
make -j "$(nproc)"
sudo make install
popd || exit 1
popd || exit 1
}
main() {
pushd "$BUILD_PATH" || exit 1
build_and_install "$NETGEN" "$BRANCH"
popd || exit 1
}
main
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/linux/dependencies/hypre.sh | .sh | 751 | 37 | #! /bin/bash
set -o errexit
set -o verbose
# shellcheck disable=1091
. ./common-functions.sh
HYPRE_SOURCE="https://github.com/hypre-space/hypre/archive/refs/tags/v2.23.0.zip"
HYPRE_ARCHIVE=$(basename $HYPRE_SOURCE)
HYPRE_PATH="hypre-2.23.0"
build_and_install() {
local source=$1
pushd "$source" || exit 1
pushd src || exit 1
cmake . -LA -B cmbuild \
-DCMAKE_INSTALL_PREFIX="/usr/local" \
-DHYPRE_HAVE_MPI=Off -DHYPRE_WITH_MPI=Off \
-DCMAKE_POSITION_INDEPENDENT_CODE=On
pushd cmbuild
make -j "$(nproc)"
sudo make install
popd || exit 1
popd || exit 1
popd || exit 1
}
main() {
pushd "$BUILD_PATH" || exit 1
download_source "$HYPRE_SOURCE"
extract_source "$HYPRE_ARCHIVE"
build_and_install "$HYPRE_PATH"
popd || exit 1
}
main
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/linux/dependencies/tetgen.sh | .sh | 672 | 33 | #! /bin/bash
set -o errexit
set -o verbose
# shellcheck disable=1091
. ./common-functions.sh
TETGEN_GIT="https://github.com/ufz/tetgen.git"
TETGEN_FILENAME=$(basename $TETGEN_GIT)
TETGEN_SOURCE_DIR="${TETGEN_FILENAME%.*}"
build_and_install() {
local git_remote=$1
local src_dir=$2
git clone $git_remote $src_dir
pushd "${src_dir}" || exit 1
cmake . -LA -B cmbuild -DCMAKE_INSTALL_PREFIX="/usr/local" -DCMAKE_BUILD_TYPE="Release" -DCMAKE_CXX_FLAGS="-fPIC"
pushd cmbuild
make -j "$(nproc)"
sudo make install
popd || exit 1
popd || exit 1
}
main() {
pushd "$BUILD_PATH" || exit 1
build_and_install "$TETGEN_GIT" "$TETGEN_SOURCE_DIR"
popd || exit 1
}
main
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/linux/dependencies/libzip.sh | .sh | 965 | 49 | #! /bin/bash
set -o errexit
set -o verbose
# shellcheck disable=1091
. ./common-functions.sh
SOURCE="https://github.com/nih-at/libzip.git"
BRANCH="v1.10.1"
build_and_install() {
local source=$1
local branch=$2
git clone --depth 1 --branch "$branch" "$source" "$branch"
pushd $branch || exit 1
cmake . -LA -B cmbuild \
-DCMAKE_INSTALL_PREFIX="/usr/local" \
-DBUILD_DOC=OFF \
-DBUILD_EXAMPLES=OFF \
-DBUILD_OSSFUZZ=OFF \
-DBUILD_REGRESS=OFF \
-DBUILD_TOOLS=OFF \
-DBUILD_SHARED_LIBS=ON \
-DENABLE_BZIP2=OFF \
-DENABLE_COMMONCRYPTO=OFF \
-DENABLE_FDOPEN=OFF \
-DENABLE_GNUTILS=OFF \
-DENABLE_LZMA=OFF \
-DENABLE_MBEDTLS=OFF \
-DENABLE_OPENSSL=OFF \
-DENABLE_WINDOWS_CRYPTO=OFF \
-DLIBZIP_DO_INSTALL=ON \
-DSHARED_LIB_VERSIONNING=ON
pushd cmbuild
make -j "$(nproc)"
sudo make install
popd || exit 1
popd || exit 1
}
main() {
pushd "$BUILD_PATH" || exit 1
build_and_install "$SOURCE" "$BRANCH"
popd || exit 1
}
main
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/linux/dependencies/python.sh | .sh | 166 | 10 | curl https://pyenv.run | bash
export PYENV_ROOT="$HOME/.pyenv"
export PATH="$PYENV_ROOT/bin:$PATH"
eval "$(pyenv init -)"
pyenv install 3.13.1
pyenv global 3.13.1
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/linux/dependencies/itk.sh | .sh | 759 | 39 | #! /bin/bash
set -o errexit
set -o verbose
# shellcheck disable=1091
. ./common-functions.sh
ITK="https://github.com/InsightSoftwareConsortium/ITK.git"
BRANCH="v5.2.1"
build_and_install() {
local source=$1
local branch=$2
git clone --depth 1 --branch "$branch" "$source" "$branch"
pushd $branch || exit 1
cmake . -B cmbuild \
-DCMAKE_INSTALL_PREFIX="/usr/local" \
-DBUILD_EXAMPLES:BOOL=OFF \
-DBUILD_SHARED_LIBS:BOOL=OFF \
-DBUILD_TESTING:BOOL=OFF \
-DITK_WRAP_PYTHON:BOOL=OFF \
-DITK_DOXYGEN_HTML:BOOL=OFF \
-DCMAKE_BUILD_TYPE=Release
pushd cmbuild
make -j "$(nproc --ignore 2)"
sudo make install
popd || exit 1
popd || exit 1
}
main() {
pushd "$BUILD_PATH" || exit 1
build_and_install "$ITK" "$BRANCH"
popd || exit 1
}
main
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/linux/dependencies/quazip.sh | .sh | 558 | 33 | #! /bin/bash
set -o errexit
set -o verbose
# shellcheck disable=1091
. ./common-functions.sh
QUAZIP="https://github.com/stachenov/quazip.git"
BRANCH="v1.4"
build_and_install() {
local source=$1
local branch=$2
git clone --depth 1 --branch "$branch" "$source" "$branch"
pushd $branch || exit 1
cmake . -LA -B cmbuild \
-DCMAKE_INSTALL_PREFIX="/usr/local"
pushd cmbuild
make -j "$(nproc)"
sudo make install
popd || exit 1
popd || exit 1
}
main() {
pushd "$BUILD_PATH" || exit 1
build_and_install "$QUAZIP" "$BRANCH"
popd || exit 1
}
main
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/linux/dependencies/sitk.sh | .sh | 723 | 38 | #! /bin/bash
set -o errexit
set -o verbose
# shellcheck disable=1091
. ./common-functions.sh
SITK="https://github.com/SimpleITK/SimpleITK.git"
BRANCH="v2.1.1.2"
build_and_install() {
local source=$1
local branch=$2
git clone --depth 1 --branch "$branch" "$source" "$branch"
pushd $branch || exit 1
cmake . -LA -B cmbuild \
-DCMAKE_INSTALL_PREFIX="/usr/local" \
-DWRAP_DEFAULT:BOOL=OFF \
-DBUILD_EXAMPLES:BOOL=OFF \
-DBUILD_TESTING:BOOL=OFF \
-DBUILD_SHARED_LIBS:BOOL=OFF \
-DCMAKE_BUILD_TYPE=Release
pushd cmbuild
make -j "$(nproc --ignore 2)"
sudo make install
popd || exit 1
popd || exit 1
}
main() {
pushd "$BUILD_PATH" || exit 1
build_and_install "$SITK" "$BRANCH"
popd || exit 1
}
main
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/linux/dependencies/ffmpeg.sh | .sh | 617 | 38 | #!/bin/bash
set -e
REPO="https://github.com/FFmpeg/FFmpeg.git"
BRANCH="n6.1"
build_and_install() {
local source=$1
local branch=$2
git clone --depth 1 --branch "$branch" "$source" "$branch"
pushd $BRANCH
./configure \
--disable-everything \
--disable-programs \
--disable-doc \
--disable-static \
--disable-debug \
--enable-shared \
--enable-encoder=mpeg1video \
--enable-muxer=mpeg1video \
--prefix="/usr/local"
sudo make -j $(nproc)
sudo make install
popd
}
main() {
pushd "$BUILD_PATH" || exit 1
build_and_install "$REPO" "$BRANCH"
popd || exit 1
}
main
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/linux/dependencies/occt.sh | .sh | 1,528 | 62 | #! /bin/bash
set -o errexit
set -o verbose
# shellcheck disable=1091
. ./common-functions.sh
OCCT="https://github.com/Open-Cascade-SAS/OCCT.git"
BRANCH="V7_7_2"
build_and_install() {
local source=$1
local branch=$2
git clone --depth 1 --branch "$branch" "$source" "$branch"
pushd $branch || exit 1
cmake . -B cmbuild \
-DCMAKE_INSTALL_PREFIX="/usr/local" \
-DBUILD_DOC_Overview:BOOL=OFF \
-DBUILD_ENABLE_FPE_SIGNAL_HANDLER:BOOL=OFF \
-DBUILD_Inspector:BOOL=OFF \
-DBUILD_LIBRARY_TYPE:STRING=Shared \
-DBUILD_MODULE_ApplicationFramework:BOOL=OFF \
-DBUILD_MODULE_DETools:BOOL=OFF \
-DBUILD_MODULE_DataExchange:BOOL=ON \
-DBUILD_MODULE_Draw:BOOL=OFF \
-DBUILD_MODULE_FoundationClasses:BOOL=TRUE \
-DBUILD_MODULE_ModelingAlgorithms:BOOL=TRUE \
-DBUILD_MODULE_ModelingData:BOOL=OFF \
-DBUILD_MODULE_Visualization:BOOL=OFF \
-DBUILD_OPT_PROFILE:STRING=Default \
-DBUILD_RELEASE_DISABLE_EXCEPTIONS:BOOL=OFF \
-DBUILD_RESOURCES:BOOL=OFF \
-DBUILD_SAMPLES_QT:BOOL=OFF \
-DBUILD_USE_PCH:BOOL=OFF \
-DUSE_DRACO:BOOL=OFF \
-DUSE_FFMPEG:BOOL=OFF \
-DUSE_FREEIMAGE:BOOL=OFF \
-DUSE_FREETYPE:BOOL=OFF \
-DUSE_GLES2:BOOL=OFF \
-DUSE_OPENGL:BOOL=OFF \
-DUSE_OPENVR:BOOL=OFF \
-DUSE_RAPIDJSON:BOOL=OFF \
-DUSE_TBB:BOOL=OFF \
-DUSE_TK:BOOL=OFF \
-DUSE_VTK:BOOL=OFF \
-DUSE_XLIB:BOOL=OFF
pushd cmbuild
make -j "$(nproc)"
sudo make install
popd || exit 1
popd || exit 1
}
main() {
pushd "$BUILD_PATH" || exit 1
build_and_install "$OCCT" "$BRANCH"
popd || exit 1
}
main
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/linux/dependencies/install.sh | .sh | 377 | 24 | #! /bin/bash
set -o errexit
set -o verbose
export BUILD_PATH=/tmp/src
mkdir -p $BUILD_PATH
SETVARS="${SETVARS:-/opt/intel/oneapi/setvars.sh}"
. $SETVARS
main() {
local dir=$1
pushd $dir
local installers=(hypre levmar mmg tetgen itk sitk occt netgen libzip ffmpeg python)
for installer in ${installers[@]}; do
./${installer}.sh
done
popd
}
main $DEPENDENCIES_PATH
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/linux/dependencies/common-functions.sh | .sh | 138 | 14 | #! /bin/bash
download_source() {
local source=$1
curl -L -O "$source"
}
extract_source() {
local archive=$1
unzip -o "$archive"
}
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/macos/installation_prep.sh | .sh | 296 | 16 | #!/bin/zsh
set -ex
if [ -d "${INSTALLATION_PATH}" ]; then
echo "Paths already configured"
exit
fi
mkdir -p "${SOURCE_PATH}"
mkdir -p "${INSTALLATION_PATH}"
chown -R "${SSH_USER}" "${INSTALLATION_PATH}"
cat << EOF >> /Users/$SSH_USER/.zprofile
export PATH="\$PATH:${INSTALLATION_PATH}"
EOF
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/macos/mmg.sh | .sh | 343 | 18 | #!/bin/zsh
pushd $SOURCE_PATH
git clone https://github.com/MmgTools/mmg.git
pushd mmg
git checkout v5.7.3
cmake . -L -B cmbuild \
-DCMAKE_INSTALL_PREFIX="/Users/gitRunner/local/x86_64" \
-DCMAKE_OSX_ARCHITECTURES="x86_64" \
-DCMAKE_OSX_DEPLOYMENT_TARGET=10.15
pushd cmbuild
make install -j $(sysctl -n hw.ncpu)
popd
popd
rm -rf mmg
popd
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/macos/levmar.sh | .sh | 390 | 19 | #!/bin/zsh
pushd $SOURCE_PATH
git clone https://github.com/jturney/levmar.git
pushd levmar
cmake . -LA -B cmbuild \
-DCMAKE_INSTALL_PREFIX="$INSTALLATION_PATH" \
-DCMAKE_POSITION_INDEPENDENT_CODE=ON \
-DBUILD_DEMO:BOOLEAN=false \
-DCMAKE_OSX_ARCHITECTURES="x86_64" \
-DCMAKE_OSX_DEPLOYMENT_TARGET=10.15
pushd cmbuild
make install -j $(sysctl -n hw.ncpu)
popd
popd
rm -rf levmar
popd
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/macos/netgen.sh | .sh | 910 | 38 | #!/bin/zsh
pushd $SOURCE_PATH
git clone --depth 1 --branch "v6.2.2406" "https://github.com/NGSolve/netgen.git"
pushd netgen
cmake . -L -B cmbuild \
-DCMAKE_BUILD_TYPE=Release \
-DCMAKE_INSTALL_PREFIX="$INSTALLATION_PATH" \
-DUSE_CCACHE:BOOL=OFF \
-DUSE_CGNS:BOOL=OFF \
-DUSE_CSG:BOOL=ON \
-DUSE_GEOM2D:BOOL=ON \
-DUSE_GUI:BOOL=OFF \
-DUSE_INTERFACE:BOOL=ON \
-DUSE_INTERNAL_TCL:BOOL=OFF \
-DUSE_JPEG:BOOL=OFF \
-DUSE_MPEG:BOOL=OFF \
-DUSE_MPI:BOOL=OFF \
-DUSE_MPI4PY:BOOL=OFF \
-DUSE_NATIVE_ARCH:BOOL=OFF \
-DUSE_NUMA:BOOL=OFF \
-DUSE_OCC:BOOL=ON \
-DUSE_PYTHON:BOOL=OFF \
-DUSE_STLGEOM:BOOL=ON \
-DUSE_SUPERBUILD:BOOL=OFF \
-DENABLE_CPP_CORE_GUIDELINES_CHECK:BOOL=OFF \
-DENABLE_UNIT_TESTS:BOOL=OFF \
-DCMAKE_OSX_ARCHITECTURES="x86_64" \
-DCMAKE_OSX_DEPLOYMENT_TARGET=10.15
pushd cmbuild
make -j $(sysctl -n hw.ncpu)
make install
popd
popd
rm -rf netgen
popd
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/macos/hypre.sh | .sh | 373 | 19 | #!/bin/zsh
pushd $SOURCE_PATH
git clone https://github.com/hypre-space/hypre.git
pushd hypre/src
cmake . -L -B cmbuild \
-DCMAKE_INSTALL_PREFIX="$INSTALLATION_PATH" \
-DHYPRE_HAVE_MPI=Off \
-DHYPRE_WITH_MPI=Off \
-DCMAKE_OSX_ARCHITECTURES="x86_64" \
-DCMAKE_OSX_DEPLOYMENT_TARGET=10.15
pushd cmbuild
make install -j $(sysctl -n hw.ncpu)
popd
popd
rm -rf hypre
popd
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/macos/tetgen.sh | .sh | 324 | 19 | #!/bin/zsh
pushd $SOURCE_PATH
git clone "https://github.com/ufz/tetgen.git"
pushd tetgen
cmake . -L -B cmbuild \
-DCMAKE_INSTALL_PREFIX="$INSTALLATION_PATH" \
-DCMAKE_OSX_ARCHITECTURES="x86_64" \
-DCMAKE_OSX_DEPLOYMENT_TARGET=10.15
pushd cmbuild
make -j $(sysctl -n hw.ncpu)
make install
popd
popd
rm -rf tetgen
popd
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/macos/libzip.sh | .sh | 804 | 36 | #!/bin/zsh
pushd $SOURCE_PATH
repo="https://github.com/nih-at/libzip.git"
branch="v1.10.1"
git clone --depth 1 --branch "$branch" "$repo" "$branch"
pushd $branch
cmake . -LA -B cmbuild \
-DCMAKE_INSTALL_PREFIX="$INSTALLATION_PATH" \
-DBUILD_DOC=OFF \
-DBUILD_EXAMPLES=OFF \
-DBUILD_OSSFUZZ=OFF \
-DBUILD_REGRESS=OFF \
-DBUILD_TOOLS=OFF \
-DBUILD_SHARED_LIBS=ON \
-DENABLE_BZIP2=OFF \
-DENABLE_COMMONCRYPTO=OFF \
-DENABLE_FDOPEN=OFF \
-DENABLE_GNUTILS=OFF \
-DENABLE_LZMA=OFF \
-DENABLE_MBEDTLS=OFF \
-DENABLE_OPENSSL=OFF \
-DENABLE_WINDOWS_CRYPTO=OFF \
-DLIBZIP_DO_INSTALL=ON \
-DSHARED_LIB_VERSIONNING=ON \
-DCMAKE_OSX_ARCHITECTURES="x86_64" \
-DCMAKE_OSX_DEPLOYMENT_TARGET=10.15
pushd cmbuild
make install -j $(sysctl -n hw.ncpu)
popd
popd
rm -rf $branch
popd
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/macos/homebrew-packages.sh | .sh | 177 | 7 | #!/bin/zsh
set -ex
packages=(jq awscli eigen glew libssh libssh2 yasm zstd pcre2 harfbuzz freetype pkg-config jpeg-turbo)
arch -x86_64 $HOMEBREW_BIN install "${packages[@]}"
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/macos/itk.sh | .sh | 580 | 24 | #!/bin/zsh
pushd $SOURCE_PATH
git clone --depth 1 --branch "v5.2.1" "https://github.com/InsightSoftwareConsortium/ITK.git"
pushd ITK
cmake . -L -B cmbuild \
-DCMAKE_INSTALL_PREFIX="$INSTALLATION_PATH" \
-DBUILD_EXAMPLES:BOOL=OFF \
-DBUILD_SHARED_LIBS:BOOL=OFF \
-DBUILD_TESTING:BOOL=OFF \
-DITK_USE_SYSTEM_EIGEN:BOOL=ON \
-DITK_WRAP_PYTHON:BOOL=OFF \
-DITK_DOXYGEN_HTML:BOOL=OFF \
-DCMAKE_BUILD_TYPE=Release \
-DCMAKE_OSX_ARCHITECTURES="x86_64" \
-DCMAKE_OSX_DEPLOYMENT_TARGET=10.15
pushd cmbuild
make install -j $(sysctl -n hw.ncpu)
popd
popd
rm -rf ITK
popd
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/macos/sitk.sh | .sh | 514 | 22 | #!/bin/zsh
pushd $SOURCE_PATH
git clone --depth 1 --branch "v2.1.1.2" "https://github.com/SimpleITK/SimpleITK.git"
pushd SimpleITK
cmake . -L -B cmbuild \
-DCMAKE_INSTALL_PREFIX="$INSTALLATION_PATH" \
-DWRAP_DEFAULT:BOOL=OFF \
-DBUILD_EXAMPLES:BOOL=OFF \
-DBUILD_TESTING:BOOL=OFF \
-DBUILD_SHARED_LIBS:BOOL=OFF \
-DCMAKE_BUILD_TYPE=Release \
-DCMAKE_OSX_ARCHITECTURES="x86_64" \
-DCMAKE_OSX_DEPLOYMENT_TARGET=10.15
pushd cmbuild
make install -j $(sysctl -n hw.ncpu)
popd
popd
rm -rf SimpleITK
popd
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/macos/ffmpeg.sh | .sh | 627 | 34 | #!/bin/zsh
set -e
REPO="https://github.com/FFmpeg/FFmpeg.git"
BRANCH="n6.1"
pushd $SOURCE_PATH
rm -rf "${SOURCE_PATH}/${BRANCH}"
export MACOSX_DEPLOYMENT_TARGET=10.15
export MACOSX_DEPLOYMENT_ARCHITECTURES=x86_64
git clone --depth 1 --branch $BRANCH $REPO $BRANCH
pushd $BRANCH
arch -x86_64 ./configure \
--disable-everything \
--disable-programs \
--disable-doc \
--disable-static \
--disable-debug \
--enable-shared \
--enable-encoder=mpeg1video \
--enable-muxer=mpeg1video \
--prefix="$INSTALLATION_PATH"
arch -x86_64 make -j $(sysctl -n hw.ncpu)
arch -x86_64 make install
popd
popd
rm -rf $BRANCH
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/macos/occt.sh | .sh | 1,326 | 46 | #!/bin/zsh
pushd $SOURCE_PATH
git clone --depth 1 --branch "V7_7_2" "https://github.com/Open-Cascade-SAS/OCCT.git"
pushd OCCT
cmake . -L -B cmbuild \
-DCMAKE_INSTALL_PREFIX="$INSTALLATION_PATH" \
-DBUILD_DOC_Overview:BOOL=OFF \
-DBUILD_ENABLE_FPE_SIGNAL_HANDLER:BOOL=OFF \
-DBUILD_Inspector:BOOL=OFF \
-DBUILD_LIBRARY_TYPE:STRING=Shared \
-DBUILD_MODULE_ApplicationFramework:BOOL=OFF \
-DBUILD_MODULE_DETools:BOOL=OFF \
-DBUILD_MODULE_DataExchange:BOOL=ON \
-DBUILD_MODULE_Draw:BOOL=OFF \
-DBUILD_MODULE_FoundationClasses:BOOL=TRUE \
-DBUILD_MODULE_ModelingAlgorithms:BOOL=TRUE \
-DBUILD_MODULE_ModelingData:BOOL=OFF \
-DBUILD_MODULE_Visualization:BOOL=OFF \
-DBUILD_OPT_PROFILE:STRING=Default \
-DBUILD_RELEASE_DISABLE_EXCEPTIONS:BOOL=OFF \
-DBUILD_RESOURCES:BOOL=OFF \
-DBUILD_SAMPLES_QT:BOOL=OFF \
-DBUILD_USE_PCH:BOOL=OFF \
-DUSE_DRACO:BOOL=OFF \
-DUSE_FFMPEG:BOOL=OFF \
-DUSE_FREEIMAGE:BOOL=OFF \
-DUSE_FREETYPE:BOOL=OFF \
-DUSE_GLES2:BOOL=OFF \
-DUSE_OPENGL:BOOL=OFF \
-DUSE_OPENVR:BOOL=OFF \
-DUSE_RAPIDJSON:BOOL=OFF \
-DUSE_TBB:BOOL=OFF \
-DUSE_TK:BOOL=OFF \
-DUSE_VTK:BOOL=OFF \
-DUSE_XLIB:BOOL=OFF \
-DCMAKE_OSX_ARCHITECTURES="x86_64" \
-DCMAKE_OSX_DEPLOYMENT_TARGET=10.15
pushd cmbuild
make install -j $(sysctl -n hw.ncpu)
popd
popd
rm -rf OCCT
popd
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/macos/openapi.sh | .sh | 622 | 20 | #!/bin/bash
set -e
if [ -f /opt/intel/oneapi/setvars.sh ]; then
echo "Oneapi was previously installed"
exit 0
fi
TARGET_DIR="/tmp/intel"
ONEAPI_FILE="m_BaseKit_p_2023.2.0.49398_offline"
ONEAPI_DMG="$ONEAPI_FILE.dmg"
ONEAPI_URI="https://registrationcenter-download.intel.com/akdlm/IRC_NAS/cd013e6c-49c4-488b-8b86-25df6693a9b7/$ONEAPI_DMG"
curl --create-dirs -O --output-dir "$TARGET_DIR" "$ONEAPI_URI"
hdiutil attach "$TARGET_DIR/$ONEAPI_DMG"
pushd /Volumes/$ONEAPI_FILE/bootstrapper.app/Contents/MacOS/
chmod +x ./install.sh
arch -x86_64 ./install.sh --silent --eula accept
popd
hdiutil detach "/Volumes/$ONEAPI_FILE"
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/macos/lua.sh | .sh | 165 | 9 | #!/bin/zsh
set -ex
arch -x86_64 $HOMEBREW_BIN install lua@5.3
cat << EOF >> /Users/$SSH_USER/.zprofile
export PATH="${HOMEBREW_PREFIX}/opt/lua@5.3/bin:\$PATH"
EOF
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/macos/rosetta.sh | .sh | 177 | 9 | #!/bin/bash
set -e
if arch -x86_64 /usr/bin/true 2> /dev/null; then
echo "Rosetta previously installed"
else
/usr/sbin/softwareupdate --install-rosetta --agree-to-license
fi
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/macos/homebrew-x86.sh | .sh | 389 | 15 | #!/bin/bash
set -ex
if [ -f "${HOMEBREW_BIN}" ]; then
echo "Homebrew already installed at: ${HOMEBREW_PREFIX}"
exit
fi
git clone https://github.com/Homebrew/brew.git $HOMEBREW_PREFIX
cat << EOF >> /Users/$SSH_USER/.zprofile
export HOMEBREW_PREFIX="${HOMEBREW_PREFIX}"
export PATH="\$PATH:${HOMEBREW_PREFIX}:${HOMEBREW_PREFIX}/bin:${HOMEBREW_PREFIX}/Cellar:${HOMEBREW_PREFIX}/opt"
EOF
| Shell |
3D | febiosoftware/FEBio | infrastructure/common/macos/qt.sh | .sh | 846 | 38 | #!/bin/zsh
set -e
REPO="https://github.com/qt/qtbase.git"
BRANCH="v6.6.1"
pushd $SOURCE_PATH
rm -rf "${SOURCE_PATH}/${BRANCH}"
git clone --depth 1 --branch $BRANCH $REPO $BRANCH
pushd $BRANCH
cmake . -G Ninja -L -B cmbuild \
-DCMAKE_OSX_ARCHITECTURES="x86_64" \
-DCMAKE_OSX_DEPLOYMENT_TARGET=10.15 \
-DCMAKE_INSTALL_PREFIX="${INSTALLATION_PATH}/Qt" \
-DCMAKE_PREFIX_PATH="${INSTALLATION_PATH}/homebrew" \
-DFEATURE_developer_build=ON \
-DFEATURE_private_tests=OFF \
-DCMAKE_BUILD_TYPE=Release \
-DQT_BUILD_TESTS=OFF \
-DQT_BUILD_TESTS_BY_DEFAULT=OFF \
-DQT_BUILD_EXAMPLES_BY_DEFAULT=OFF \
-DQT_INSTALL_EXAMPLE_SOURCE_BY_DEFAULT=OFF \
pushd cmbuild
cmake --build . --parallel
cmake --install .
popd
popd
rm -rf "${BRANCH}"
cat << EOF >> /Users/$SSH_USER/.zprofile
export PATH="${INSTALLATION_PATH}/Qt/bin:\$PATH"
EOF
| Shell |
3D | febiosoftware/FEBio | FEBioRVE/FE2OMicroConstraint.cpp | .cpp | 11,063 | 432 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#include "stdafx.h"
#include "FE2OMicroConstraint.h"
#include <FECore/log.h>
#include <FEBioMech/FEBioMech.h>
#include <FECore/FELinearSystem.h>
#include <FECore/FEMesh.h>
//-----------------------------------------------------------------------------
//! constructor
FEMicroFlucSurface::FEMicroFlucSurface(FEModel* fem) : FESurface(fem)
{
m_Lm.x = 0.; m_Lm.y = 0.; m_Lm.z = 0.;
m_pv.x = 0.; m_pv.y = 0.; m_pv.z = 0.;
m_c.x = 0.; m_c.y = 0.; m_c.z = 0.;
m_Fm.unit(); m_Gm.zero();
}
//-----------------------------------------------------------------------------
bool FEMicroFlucSurface::Init()
{
// calculate the intial microfluctations across the surface
m_c = SurfMicrofluc();
return true;
}
//-----------------------------------------------------------------------------
void FEMicroFlucSurface::CopyFrom(FEMicroFlucSurface& s)
{
m_Node = s.m_Node;
// create elements
int NE = s.Elements();
Create(NE);
for (int i=0; i<NE; ++i) Element(i) = s.Element(i);
// copy surface data
m_Lm = s.m_Lm;
m_pv = s.m_pv;
m_c = s.m_c;
m_Fm = s.m_Fm;
m_Gm = s.m_Gm;
}
//-----------------------------------------------------------------------------
//! Calculate the initial volume
vec3d FEMicroFlucSurface::SurfMicrofluc()
{
// Integration of microfluctation field across surface
vec3d c;
// get the mesh
FEMesh& mesh = *GetMesh();
// loop over all elements
double vol = 0.0;
int NE = Elements();
vec3d x[FEElement::MAX_NODES];
vec3d x0[FEElement::MAX_NODES];
for (int i=0; i<NE; ++i)
{
// get the next element
FESurfaceElement& el = Element(i);
// get the nodal coordinates
int neln = el.Nodes();
for (int j=0; j<neln; ++j){
x[j] = mesh.Node(el.m_node[j]).m_rt;
x0[j] = mesh.Node(el.m_node[j]).m_r0;
}
// loop over integration points
double* w = el.GaussWeights();
int nint = el.GaussPoints();
for (int n=0; n<nint; ++n)
{
vec3d r = el.eval(x, n);
vec3d r0 = el.eval(x0, n);
vec3d u = r - r0;
mat3d I; I.unit();
c += (u - (m_Fm - I)*r0 - m_Gm.contractdyad1(r0)*0.5)*w[n];
}
}
return c;
}
//-----------------------------------------------------------------------------
BEGIN_FECORE_CLASS(FE2OMicroConstraint, FESurfaceConstraint);
ADD_PARAMETER(m_blaugon, "laugon" );
ADD_PARAMETER(m_atol , "augtol" );
ADD_PARAMETER(m_eps , "penalty");
END_FECORE_CLASS();
//-----------------------------------------------------------------------------
//! constructor. Set default parameter values
FE2OMicroConstraint::FE2OMicroConstraint(FEModel* pfem) : FESurfaceConstraint(pfem), m_s(pfem), m_dofU(pfem)
{
m_eps = 0.0;
m_atol = 0.0;
m_blaugon = false;
m_binit = false; // will be set to true during activation
// TODO: Can this be done in Init, since there is no error checking
if (pfem)
{
m_dofU.AddVariable(FEBioMech::GetVariableName(FEBioMech::DISPLACEMENT));
}
}
//-----------------------------------------------------------------------------
void FE2OMicroConstraint::CopyFrom(FENLConstraint* plc)
{
// cast to a periodic boundary
FE2OMicroConstraint& mc = dynamic_cast<FE2OMicroConstraint&>(*plc);
// copy parameters
GetParameterList() = mc.GetParameterList();
// copy nodes
m_s.CopyFrom(mc.m_s);
}
//-----------------------------------------------------------------------------
//! Returns the surface
FESurface* FE2OMicroConstraint::GetSurface()
{
return &m_s;
}
//-----------------------------------------------------------------------------
//! Initializes data structures.
void FE2OMicroConstraint::Activate()
{
// don't forget to call base class
FENLConstraint::Activate();
// initialize the surface
if (m_binit == false) m_s.Init();
// set flag that initial volume is calculated
m_binit = true;
}
//-----------------------------------------------------------------------------
void FE2OMicroConstraint::BuildMatrixProfile(FEGlobalMatrix& M)
{
// We don't do anything here since the connectivity of a surface
// is implied by the domain to which it is attached.
}
//-----------------------------------------------------------------------------
void FE2OMicroConstraint::UnpackLM(FEElement& el, vector<int>& lm)
{
FEMesh& mesh = GetMesh();
int N = el.Nodes();
lm.resize(N*3);
for (int i=0; i<N; ++i)
{
int n = el.m_node[i];
FENode& node = mesh.Node(n);
vector<int>& id = node.m_ID;
lm[3*i ] = id[m_dofU[0]];
lm[3*i+1] = id[m_dofU[1]];
lm[3*i+2] = id[m_dofU[2]];
}
}
//-----------------------------------------------------------------------------
void FE2OMicroConstraint::LoadVector(FEGlobalVector& R, const FETimeInfo& tp)
{
FEMesh& mesh = *m_s.GetMesh();
vector<double> fe;
vector<int> lm;
// get the lagrange
vec3d Lm = m_s.m_Lm;
// loop over all elements
int NE = m_s.Elements();
vec3d x[FEElement::MAX_NODES];
for (int i=0; i<NE; ++i)
{
// get the next element
FESurfaceElement& el = m_s.Element(i);
// get the nodal coordinates
int neln = el.Nodes();
for (int j=0; j<neln; ++j) x[j] = mesh.Node(el.m_node[j]).m_rt;
// allocate element residual vector
int ndof = 3*neln;
fe.resize(ndof);
zero(fe);
// loop over all integration points
double* w = el.GaussWeights();
int nint = el.GaussPoints();
for (int n=0; n<nint; ++n)
{
// calculate the tangent vectors
double* Gr = el.Gr(n);
double* Gs = el.Gs(n);
vec3d dxr(0,0,0), dxs(0,0,0);
for (int j=0; j<neln; ++j)
{
dxr += x[j]*Gr[j];
dxs += x[j]*Gs[j];
}
// evaluate the "normal" vector
vec3d v = (dxr ^ dxs);
vec3d f = Lm*w[n]*v.norm();
// evaluate the element forces
double* H = el.H(n);
for (int j=0; j<neln; ++j)
{
fe[3*j ] += H[j]*f.x;
fe[3*j+1] += H[j]*f.y;
fe[3*j+2] += H[j]*f.z;
}
}
// get the element's LM vector
lm.resize(3*neln);
for (int j=0; j<neln; ++j)
{
vector<int>& id = mesh.Node(el.m_node[j]).m_ID;
lm[3*j ] = id[m_dofU[0]];
lm[3*j+1] = id[m_dofU[1]];
lm[3*j+2] = id[m_dofU[2]];
}
// add element force vector to global force vector
R.Assemble(el.m_node, lm, fe);
}
}
//-----------------------------------------------------------------------------
void FE2OMicroConstraint::StiffnessMatrix(FELinearSystem& LS, const FETimeInfo& tp)
{
FEMesh& mesh = *m_s.GetMesh();
// element stiffness matrix
FEElementMatrix ke;
vector<int> lm;
vector<double> fe;
// loop over all elements
int NE = m_s.Elements();
vec3d x[FEElement::MAX_NODES];
for (int l=0; l<NE; ++l)
{
// get the next element
FESurfaceElement& el = m_s.Element(l);
// get the nodal coordinates
int neln = el.Nodes();
for (int j=0; j<neln; ++j) x[j] = mesh.Node(el.m_node[j]).m_rt;
// allocate the stiffness matrix
int ndof = 3*neln;
ke.resize(ndof, ndof);
ke.zero();
fe.resize(ndof);
zero(fe);
// repeat over integration points
double* w = el.GaussWeights();
int nint = el.GaussPoints();
for (int n=0; n<nint; ++n)
{
// calculate tangent vectors
double* N = el.H(n);
double* Gr = el.Gr(n);
double* Gs = el.Gs(n);
vec3d dxr(0,0,0), dxs(0,0,0);
for (int j=0; j<neln; ++j)
{
dxr += x[j]*Gr[j];
dxs += x[j]*Gs[j];
}
// calculate pressure contribution
vec3d v = (dxr ^ dxs);
double vi;
double vj;
for (int i=0; i<neln; ++i)
for (int j=0; j<neln; ++j)
{
vi = N[i]*v.norm();
vj = N[j]*v.norm();
ke[3*i ][3*j ] += m_eps*vi*vj;
ke[3*i+1][3*j+1] += m_eps*vi*vj;
ke[3*i+2][3*j+2] += m_eps*vi*vj;
}
// calculate displacement contribution
vec3d qab;
for (int i=0; i<neln; ++i)
for (int j=0; j<neln; ++j)
{
qab = (-dxs*Gr[j] + dxr*Gs[j])*(N[i]/(2*(dxr ^ dxs).norm()))*w[n];
ke[3*i ][3*j ] += 0;
ke[3*i ][3*j+1] += qab.z;
ke[3*i ][3*j+2] += -qab.y;
ke[3*i+1][3*j ] += -qab.z;
ke[3*i+1][3*j+1] += 0;
ke[3*i+1][3*j+2] += qab.x;
ke[3*i+2][3*j ] += qab.y;
ke[3*i+2][3*j+1] += -qab.x;
ke[3*i+2][3*j+2] += 0;
}
}
// get the element's LM vector
lm.resize(3*neln);
for (int j=0; j<neln; ++j)
{
vector<int>& id = mesh.Node(el.m_node[j]).m_ID;
lm[3*j ] = id[m_dofU[0]];
lm[3*j+1] = id[m_dofU[1]];
lm[3*j+2] = id[m_dofU[2]];
}
// assemble element matrix in global stiffness matrix
ke.SetNodes(el.m_node);
ke.SetIndices(lm);
LS.Assemble(ke);
}
}
//-----------------------------------------------------------------------------
bool FE2OMicroConstraint::Augment(int naug, const FETimeInfo& tp)
{
// make sure we are augmenting
if ((m_blaugon == false) || (m_atol <= 0.0)) return true;
feLog("\n2O periodic surface microfluctation constraint:\n");
vec3d Dm = m_s.m_c*m_eps;
vec3d Lm = m_s.m_pv;
double Dnorm = Dm.norm();
double Lnorm = Lm.norm();
double err = Dnorm/Lnorm;
if (Lnorm == 0)
err = 0;
feLog("\tpressure vect norm: %lg\n", Lm.norm());
feLog("\tnorm : %lg (%lg)\n", err, m_atol);
feLog("\ttotal microfluc norm: %lg\n", m_s.m_c.norm());
// check convergence
if (err < m_atol) return true;
// update Lagrange multiplier (and pressure variable)
m_s.m_Lm = Lm;
m_s.m_pv = Lm + Dm;
return false;
}
//-----------------------------------------------------------------------------
void FE2OMicroConstraint::Serialize(DumpStream& ar)
{
ar & m_s.m_Lm;
ar & m_s.m_pv;
ar & m_s.m_c;
ar & m_s.m_Fm;
ar & m_s.m_Gm;
}
//-----------------------------------------------------------------------------
void FE2OMicroConstraint::Reset()
{
}
//-----------------------------------------------------------------------------
// This function is called when the FE model's state needs to be updated.
void FE2OMicroConstraint::Update(const FETimeInfo& tp)
{
// calculate the current volume
m_s.m_c = m_s.SurfMicrofluc();
// update pressure variable
m_s.m_pv = m_s.m_Lm - m_s.m_c*m_eps;
}
| C++ |
3D | febiosoftware/FEBio | FEBioRVE/FEElasticMaterial2O.h | .h | 3,253 | 99 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#pragma once
#include "FEBioMech/FEElasticMaterial.h"
#include <FECore/tens3d.h>
#include <FECore/tens4d.h>
#include <FECore/tens5d.h>
#include <FECore/tens6d.h>
//-----------------------------------------------------------------------------
// second order continuum elastic material point
class FEElasticMaterialPoint2O : public FEMaterialPointData
{
public:
//! constructor
FEElasticMaterialPoint2O(FEMaterialPointData* pt);
//! create a shallow copy
FEMaterialPointData* Copy();
//! serialize material point data
void Serialize(DumpStream& ar);
public:
mat3d m_PK1; //!< PK1 stress
tens3drs m_G; //!< gradient of deformation gradient
tens3drs m_Q; //!< higher-order stress tensor
};
//-----------------------------------------------------------------------------
//! This is the base class for second-order continuum elastic material
class FEElasticMaterial2O : public FEElasticMaterial
{
public:
//! constructor
FEElasticMaterial2O(FEModel* fem);
public: // these functions must be implemented by derived classes
//! Calculate PK1 stress and higher order stress Q
virtual void Stress(FEMaterialPoint& mp, mat3d& P, tens3drs& Q) = 0;
//! Calculate material tangents
//! C = dP/dF
//! L = dP/dG
//! H = dQ/dF
//! J = dQ/dG
virtual void Tangent(FEMaterialPoint& mp, tens4d& C, tens5d& L, tens5d& H, tens6d& J) = 0;
//! create material point data
FEMaterialPointData* CreateMaterialPointData() override;
public:
double m_beta; //!< beta parameter for DG
// flags for evaluating effects of stiffness contributions
// TODO: I'll probably delete this when all bugs are found
bool m_bKDG1;
bool m_bKDG2;
bool m_bKDG3;
bool m_buseJ0;
private:
// We don't need these functions
// TODO: Perhaps we should derive this class directly from FEMaterial so
// that these functions are not inherited.
mat3ds Stress(FEMaterialPoint& pt) override;
tens4ds Tangent(FEMaterialPoint& pt) override;
DECLARE_FECORE_CLASS();
};
| Unknown |
3D | febiosoftware/FEBio | FEBioRVE/febiorve_api.h | .h | 1,531 | 44 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#pragma once
#ifdef WIN32
#ifdef FECORE_DLL
#ifdef febiorve_EXPORTS
#define FEBIORVE_API __declspec(dllexport)
#else
#define FEBIORVE_API __declspec(dllimport)
#endif
#else
#define FEBIORVE_API
#endif
#else
#define FEBIORVE_API
#endif
| Unknown |
3D | febiosoftware/FEBio | FEBioRVE/FEMultiscaleDomainFactory.cpp | .cpp | 859 | 25 | #include "stdafx.h"
#include "FEMultiscaleDomainFactory.h"
#include "FEMicroMaterial.h"
#include "FEMicroMaterial2O.h"
#include "FEElasticMultiscaleDomain1O.h"
#include "FEElasticMultiscaleDomain2O.h"
//==========================================================================================
FEDomain* FEMultiScaleDomainFactory::CreateDomain(const FE_Element_Spec& spec, FEMesh* pm, FEMaterial* pmat)
{
FEModel* pfem = pmat->GetFEModel();
const char* sztype = 0;
if (dynamic_cast<FEMicroMaterial* >(pmat)) sztype = "elastic-mm-solid";
else if (dynamic_cast<FEMicroMaterial2O* >(pmat)) sztype = "elastic-mm-solid2O";
else if (dynamic_cast<FEElasticMaterial2O*>(pmat)) sztype = "elastic-solid2O";
if (sztype)
{
FESolidDomain* pd = fecore_new<FESolidDomain>(sztype, pfem);
if (pd) pd->SetMaterial(pmat);
return pd;
}
else return 0;
}
| C++ |
3D | febiosoftware/FEBio | FEBioRVE/FEElasticSolidDomain2O.cpp | .cpp | 65,319 | 2,119 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#include "stdafx.h"
#include "FEElasticSolidDomain2O.h"
#include "FEElasticMaterial2O.h"
#include <FECore/FEModel.h>
#include <FECore/FEMesh.h>
#include <FECore/FEGlobalMatrix.h>
#include <FECore/log.h>
//-----------------------------------------------------------------------------
// helper function for comparing two facets
bool compare_facets(int* na, int* nb, int nodes)
{
switch (nodes)
{
case 3: // 3-node triangle
case 6: // 6-node triangle
case 7: // 7-node triangle
if ((na[0]!=nb[0])&&(na[0]!=nb[1])&&(na[0]!=nb[2])) return false;
if ((na[1]!=nb[0])&&(na[1]!=nb[1])&&(na[1]!=nb[2])) return false;
if ((na[2]!=nb[0])&&(na[2]!=nb[1])&&(na[2]!=nb[2])) return false;
break;
case 4: // 4-node quad
case 8: // 8-node quad
case 9: // 9-node quad
if ((na[0]!=nb[0])&&(na[0]!=nb[1])&&(na[0]!=nb[2])&&(na[0]!=nb[3])) return false;
if ((na[1]!=nb[0])&&(na[1]!=nb[1])&&(na[1]!=nb[2])&&(na[1]!=nb[3])) return false;
if ((na[2]!=nb[0])&&(na[2]!=nb[1])&&(na[2]!=nb[2])&&(na[2]!=nb[3])) return false;
if ((na[3]!=nb[0])&&(na[3]!=nb[1])&&(na[3]!=nb[2])&&(na[3]!=nb[3])) return false;
break;
default:
return false;
}
return true;
}
//-----------------------------------------------------------------------------
inline bool compare_tri(int* na, int* nb, int i, int j, int k)
{
return ((na[0] == nb[i])&&(na[1] == nb[j])&&(na[2] == nb[k]));
}
//-----------------------------------------------------------------------------
inline bool compare_quad(int* na, int* nb, int i, int j, int k, int l)
{
return ((na[0] == nb[i])&&(na[1] == nb[j])&&(na[2] == nb[k])&&(na[3] == nb[l]));
}
//-----------------------------------------------------------------------------
// helper function for mapping surface facet coordinates to element facet coordinates
vec2d map_facet_to_facet(int* na, int* nb, int nodes, double r, double s)
{
switch (nodes)
{
// triangles
case 3:
case 6:
case 7:
if (compare_tri(na, nb, 0, 1, 2)) return vec2d( r , s);
if (compare_tri(na, nb, 2, 0, 1)) return vec2d( s, 1-r-s);
if (compare_tri(na, nb, 1, 2, 0)) return vec2d(1-r-s, r );
if (compare_tri(na, nb, 0, 2, 1)) return vec2d( s, r );
if (compare_tri(na, nb, 2, 1, 0)) return vec2d( r , 1-r-s);
if (compare_tri(na, nb, 1, 0, 2)) return vec2d(1-r-s, s);
break;
// quads
case 4:
case 8:
case 9:
if (compare_quad(na, nb, 0, 1, 2, 3)) return vec2d( r, s);
if (compare_quad(na, nb, 3, 0, 1, 2)) return vec2d( s, -r);
if (compare_quad(na, nb, 2, 3, 0, 1)) return vec2d( -r, -s);
if (compare_quad(na, nb, 1, 2, 3, 0)) return vec2d( -s, r);
if (compare_quad(na, nb, 2, 1, 0, 3)) return vec2d( -s, -r);
if (compare_quad(na, nb, 3, 2, 1, 0)) return vec2d( r, -s);
if (compare_quad(na, nb, 0, 3, 2, 1)) return vec2d( s, r);
if (compare_quad(na, nb, 1, 0, 3, 2)) return vec2d( -r, s);
break;
}
// we shouldn't get here
assert(false);
return vec2d(0,0);
}
//-----------------------------------------------------------------------------
// Notice that this depends on how the facet nodes are numbered (see FEElement::GetFace)
vec3d map_facet_to_volume_coordinates_tet(int nface, const vec2d& q)
{
double h1 = q.x(), h2 = q.y(), h3 = 1.0 - h1 - h2;
double g1, g2, g3;
switch (nface)
{
case 0: g1 = h1; g2 = 0.; g3 = h2; break;
case 1: g1 = h3; g2 = h1; g3 = h2; break;
case 2: g1 = 0.; g2 = h3; g3 = h2; break;
case 3: g1 = h1; g2 = h3; g3 = 0.; break;
default:
assert(false);
}
return vec3d(g1, g2, g3);
}
//-----------------------------------------------------------------------------
// Notice that this depends on how the facet nodes are numbered (see FEElement::GetFace)
vec3d map_facet_to_volume_coordinates_hex(int nface, const vec2d& q)
{
double h1 = q.x(), h2 = q.y();
double g1, g2, g3;
switch (nface)
{
case 0: g1 = h1; g2 = -1.; g3 = h2; break;
case 1: g1 = 1.; g2 = h1; g3 = h2; break;
case 2: g1 = -h1; g2 = 1.; g3 = h2; break;
case 3: g1 = -1.; g2 = -h1; g3 = h2; break;
case 4: g1 = h2; g2 = h1; g3 = -1.; break;
case 5: g1 = h1; g2 = h2; g3 = 1.; break;
default:
assert(false);
}
return vec3d(g1, g2, g3);
}
//-----------------------------------------------------------------------------
// helper function that maps natural coordinates from facets to elements
vec3d map_facet_to_solid(FEMesh& mesh, FESurfaceElement& face, FESolidElement& el, double r, double s)
{
int fn[FEElement::MAX_NODES];
int nfaces = el.Faces();
for (int i=0; i<nfaces; ++i)
{
el.GetFace(i, fn);
if (compare_facets(&face.m_node[0], fn, face.Nodes()))
{
// map the facet coordinates to the element's facet coordinates
// (faces can be rotated or inverted w.r.t. the element's face)
vec2d b = map_facet_to_facet(&face.m_node[0], fn, face.Nodes(), r, s);
// convert facet coordinates to volume coordinates
switch (el.Nodes())
{
// tets
case 4:
case 10:
case 15: return map_facet_to_volume_coordinates_tet(i, b); break;
// hexes
case 8:
case 20:
case 27: return map_facet_to_volume_coordinates_hex(i, b); break;
}
assert(false);
}
}
// we shouldn't get here
assert(false);
return vec3d(0,0,0);
}
//-----------------------------------------------------------------------------
FEElasticSolidDomain2O::FEInternalSurface2O::FEInternalSurface2O()
{
m_h = 1.0;
}
bool FEElasticSolidDomain2O::FEInternalSurface2O::Initialize(FEElasticSolidDomain2O* dom)
{
// get the material
// We need this for allocating material point data on the internal facets
FEMaterial* pmat = dom->GetMaterial();
assert(pmat);
if (pmat == 0) return false;
// build the inside surface
FEMesh& mesh = *dom->GetMesh();
m_ps = mesh.ElementBoundarySurface(false, true);
if (m_ps == 0) return false;
// allocate data
int NF = m_ps->Elements();
int nnf = 0;
for (int i=0; i<NF; ++i)
{
FESurfaceElement& el = m_ps->Element(i);
nnf += el.GaussPoints();
}
m_data.resize(nnf);
// allocate material point data
nnf = 0;
for (int i=0; i<NF; ++i)
{
FESurfaceElement& face = m_ps->Element(i);
int nint = face.GaussPoints();
for (int n=0; n<nint; ++n, ++nnf)
{
m_data[nnf].m_pt[0] = new FEMaterialPoint(pmat->CreateMaterialPointData());
m_data[nnf].m_pt[1] = new FEMaterialPoint(pmat->CreateMaterialPointData());
m_data[nnf].m_pt[0]->Init();
m_data[nnf].m_pt[1]->Init();
// iso-parametric coordinates in surface element
double h1 = face.gr(n);
double h2 = face.gs(n);
for (int k=0; k<2; ++k)
{
// get the adjacent solid element
FESolidElement& ek = static_cast<FESolidElement&>(*face.m_elem[k].pe);
// map the iso-parametric coordinates from the facet to the solid element
m_data[nnf].ksi[k] = map_facet_to_solid(mesh, face, ek, h1, h2);
}
#ifndef NDEBUG
// This checks that the two integration points coincide physically at the same point
FESolidElement& ea = static_cast<FESolidElement&>(*face.m_elem[0].pe);
FESolidElement& eb = static_cast<FESolidElement&>(*face.m_elem[1].pe);
vec3d xa[FEElement::MAX_NODES];
vec3d xb[FEElement::MAX_NODES];
for (int a=0; a<ea.Nodes(); a++) xa[a] = mesh.Node(ea.m_node[a]).m_r0;
for (int b=0; b<eb.Nodes(); b++) xb[b] = mesh.Node(eb.m_node[b]).m_r0;
vec3d ksia = m_data[nnf].ksi[0];
vec3d ksib = m_data[nnf].ksi[1];
vec3d ra = ea.evaluate(xa, ksia.x, ksia.y, ksia.z);
vec3d rb = eb.evaluate(xb, ksib.x, ksib.y, ksib.z);
vec3d dr = ra - rb;
double Dr = dr.norm();
assert(Dr <1e-12);
#endif
}
}
// calculate the element size
m_h = 0.0;
for (int i=0; i<NF; ++i)
{
FESurfaceElement& el = m_ps->Element(i);
int neln = el.Nodes();
FEBoundingBox box(mesh.Node(el.m_node[0]).m_r0);
for (int j=1; j<neln; ++j) box.add(mesh.Node(el.m_node[j]).m_r0);
double R = 0.5*box.radius();
if (R > m_h) m_h = R;
}
return true;
}
//-----------------------------------------------------------------------------
FEElasticSolidDomain2O::FEElasticSolidDomain2O(FEModel* pfem) : FEElasticSolidDomain(pfem)
{
m_binitJ0 = false;
}
//-----------------------------------------------------------------------------
//! Initialize element data
bool FEElasticSolidDomain2O::Init()
{
// initialize base class first
if (FEElasticSolidDomain::Init() == false) return false;
// initialize the internal surface data
if (m_surf.Initialize(this) == false) return false;
return true;
}
//-----------------------------------------------------------------------------
void FEElasticSolidDomain2O::BuildMatrixProfile(FEGlobalMatrix& M)
{
// call base class first
FEElasticSolidDomain::BuildMatrixProfile(M);
// do the interface elements
FEMesh& mesh = *GetMesh();
vector<int> lm;
// loop over all internal facets
int NF = m_surf.Elements();
for (int n=0; n<NF; ++n)
{
// get the next surface element
FESurfaceElement& el = m_surf.Element(n);
// get the solid elements from this interface element
FESolidElement& ela = static_cast<FESolidElement&>(*el.m_elem[1].pe);
FESolidElement& elb = static_cast<FESolidElement&>(*el.m_elem[0].pe);
int nelna = ela.Nodes();
int nelnb = elb.Nodes();
// setup the LM vector
// TODO: this assumes that the X,Y,Z degrees of freedom are 0,1,2 respectively
int ndof = 3*(nelna + nelnb);
lm.resize(ndof);
for (int i=0; i<nelna; ++i)
{
vector<int>& id = mesh.Node(ela.m_node[i]).m_ID;
lm[3*i ] = id[0];
lm[3*i+1] = id[1];
lm[3*i+2] = id[2];
}
for (int i=0; i<nelnb; ++i)
{
vector<int>& id = mesh.Node(elb.m_node[i]).m_ID;
lm[3*(nelna+i) ] = id[0];
lm[3*(nelna+i)+1] = id[1];
lm[3*(nelna+i)+2] = id[2];
}
// add it to the profile
M.build_add(lm);
}
}
//-----------------------------------------------------------------------------
void FEElasticSolidDomain2O::PreSolveUpdate(const FETimeInfo& timeInfo)
{
FEElasticSolidDomain::PreSolveUpdate(timeInfo);
int NF = m_surf.Elements(), nd = 0;
for (int i=0; i<NF; ++i)
{
FESurfaceElement& el = m_surf.Element(i);
int nint = el.GaussPoints();
for (int n=0; n<nint; ++n, ++nd)
{
FEInternalSurface2O::Data& data = m_surf.GetData(nd);
data.m_pt[0]->Update(timeInfo);
data.m_pt[1]->Update(timeInfo);
}
}
}
//-----------------------------------------------------------------------------
void FEElasticSolidDomain2O::Update(const FETimeInfo& tp)
{
// call base class first
// (this will call FEElasticMultiscaleDomain2O::UpdateElementStress)
FEElasticSolidDomain::Update(tp);
// update internal surfaces
UpdateInternalSurfaceStresses();
// update the kinematic variables
UpdateKinematics();
}
//-----------------------------------------------------------------------------
// Update some kinematical quantities needed for evaluating the discontinuous-Galerkin terms
void FEElasticSolidDomain2O::UpdateKinematics()
{
FEMesh& mesh = *GetMesh();
// nodal displacements
vec3d ut[FEElement::MAX_NODES];
// shape function derivatives
vec3d G[FEElement::MAX_NODES];
// loop over all facets
int nd = 0;
int NF = m_surf.Elements();
for (int i=0; i<NF; ++i)
{
// get the next facet
FESurfaceElement& face = m_surf.Element(i);
int nfn = face.Nodes();
int nint = face.GaussPoints();
// calculate the displacement gradient jump across this facet
for (int m=0; m<2; ++m)
{
// evaluate the spatial gradient of shape functions
FESolidElement& el = static_cast<FESolidElement&>(*face.m_elem[m].pe);
int neln = el.Nodes();
// get the nodal displacements
for (int j=0; j<neln; ++j)
{
FENode& node = mesh.Node(el.m_node[j]);
ut[j] = node.m_rt - node.m_r0;
}
// loop over all integration points
for (int n=0; n<nint; ++n)
{
// get the integration point data
FEInternalSurface2O::Data& data = m_surf.GetData(nd + n);
// evaluate element Jacobian and shape function derivatives
// at this integration point
vec3d& ksi = data.ksi[m];
ShapeGradient0(el, ksi.x, ksi.y, ksi.z, G);
// calculate displacement gradient
mat3d Gu; Gu.zero();
for (int j=0; j<neln; ++j) Gu += ut[j] & G[j];
if (m == 0) data.DgradU = Gu;
else data.DgradU = Gu - data.DgradU;
}
}
// don't forget to increment data counter
nd += nint;
}
}
//-----------------------------------------------------------------------------
// This function evaluates the stresses at either side of the internal surface
// facets.
void FEElasticSolidDomain2O::UpdateInternalSurfaceStresses()
{
FEMesh& mesh = *GetMesh();
// calculate the material
FEElasticMaterial2O* pmat = dynamic_cast<FEElasticMaterial2O*>(m_pMat);
// loop over all the internal surfaces
int NF = m_surf.Elements(), nd = 0;
for (int i=0; i<NF; ++i)
{
FESurfaceElement& face = m_surf.Element(i);
int nint = face.GaussPoints();
for (int n=0; n<nint; ++n, ++nd)
{
FEInternalSurface2O::Data& data = m_surf.GetData(nd);
data.Qavg.zero();
if (m_binitJ0 == false) data.J0avg.zero();
// get the deformation gradient and determinant
for (int k=0; k<2; ++k)
{
FEMaterialPoint& mp = *data.m_pt[k];
FEElasticMaterialPoint& pt = *mp.ExtractData<FEElasticMaterialPoint>();
FEElasticMaterialPoint2O& pt2O = *mp.ExtractData<FEElasticMaterialPoint2O>();
vec3d& ksi = data.ksi[k];
// TODO: Is face.m_elem is a local index?
FESolidElement& ek = static_cast<FESolidElement&>(*face.m_elem[k].pe);
// evaluate deformation gradient, Jacobian and Hessian for this element
pt.m_J = defgrad(ek, pt.m_F, ksi.x, ksi.y, ksi.z);
defhess(ek, ksi.x, ksi.y, ksi.z, pt2O.m_G);
// evaluate stresses at this integration point
pmat->Stress(mp, pt2O.m_PK1, pt2O.m_Q);
data.Qavg += pt2O.m_Q*0.5;
// TODO: Can I evaluate this elsewhere?
tens4d C;
tens5d L, H;
tens6d J;
pmat->Tangent(mp, C, L, H, J);
data.H[k] = H;
data.J[k] = J;
if (m_binitJ0 == false)
{
// we need to evaluate the initial stiffnesses as well
data.J0[k] = J;
data.H0[k] = H;
data.J0avg += J*0.5;
}
}
}
}
// set flag indicating J0 has been initialized
if (pmat->m_buseJ0) m_binitJ0 = true;
}
//-----------------------------------------------------------------------------
//! Update element state data (mostly stresses, but some other stuff as well)
//! \todo Remove the remodeling solid stuff
void FEElasticSolidDomain2O::UpdateElementStress(int iel, const FETimeInfo& tp)
{
// get the solid element
FESolidElement& el = m_Elem[iel];
// get the number of integration points
int nint = el.GaussPoints();
// number of nodes
int neln = el.Nodes();
// nodal coordinates
vec3d r0[FEElement::MAX_NODES];
vec3d rt[FEElement::MAX_NODES];
for (int j=0; j<neln; ++j)
{
r0[j] = m_pMesh->Node(el.m_node[j]).m_r0;
rt[j] = m_pMesh->Node(el.m_node[j]).m_rt;
}
// get the integration weights
double* gw = el.GaussWeights();
// calculate the stress at this material point
FEElasticMaterial2O* pmat = dynamic_cast<FEElasticMaterial2O*>(m_pMat);
// loop over the integration points and calculate
// the stress at the integration point
for (int n=0; n<nint; ++n)
{
FEMaterialPoint& mp = *el.GetMaterialPoint(n);
FEElasticMaterialPoint& pt = *(mp.ExtractData<FEElasticMaterialPoint>());
FEElasticMaterialPoint2O& pt2O = *(mp.ExtractData<FEElasticMaterialPoint2O>());
// material point coordinates
// TODO: I'm not entirly happy with this solution
// since the material point coordinates are used by most materials.
mp.m_r0 = el.Evaluate(r0, n);
mp.m_rt = el.Evaluate(rt, n);
// get the deformation gradient and determinant
pt.m_J = defgrad(el, pt.m_F, n);
defhess(el, n, pt2O.m_G);
// evaluate stresses
pmat->Stress(mp, pt2O.m_PK1, pt2O.m_Q);
// store the Cauchy stress as well
double J = pt.m_J;
const mat3d Ft = pt.m_F.transpose();
pt.m_s = (pt2O.m_PK1*Ft).sym()/J;
}
}
//-----------------------------------------------------------------------------
void FEElasticSolidDomain2O::InternalForces(FEGlobalVector& R)
{
// call base class first
FEElasticSolidDomain::InternalForces(R);
// add the discontinuous-Galerkin contribution
InternalForcesDG1(R);
InternalForcesDG2(R);
InternalForcesDG3(R);
}
//-----------------------------------------------------------------------------
//! Evaluate contribution of discontinuous-Galerkin enforcement of stress flux.
void FEElasticSolidDomain2O::InternalForcesDG1(FEGlobalVector& R)
{
FEMesh& mesh = *GetMesh();
FESurface& surf = *m_surf.GetSurface();
mat3d Ji;
double Gr[FEElement::MAX_NODES];
double Gs[FEElement::MAX_NODES];
double Gt[FEElement::MAX_NODES];
vector<double> fe;
vector<int> lm;
// loop over all internal surfaces elements
int nd = 0;
int NF = m_surf.Elements();
for (int i=0; i<NF; ++i)
{
// get the next surface
FESurfaceElement& face = m_surf.Element(i);
int nfn = face.Nodes();
int nint = face.GaussPoints();
// loop over both sides
for (int m=0; m<2; ++m)
{
// evaluate the spatial gradient of shape functions
FESolidElement& el = static_cast<FESolidElement&>(*face.m_elem[m].pe);
int neln = el.Nodes();
// sign
double sgn = (m==0 ? -1.0 : 1.0);
// allocate force vector
int ndof = neln*3;
fe.assign(ndof, 0.0);
// loop over all the integration points
double* gw = face.GaussWeights();
for (int n=0; n<nint; ++n)
{
// get facet data
FEInternalSurface2O::Data& data = m_surf.GetData(nd + n);
// average stress across interface
tens3drs& Qavg = data.Qavg;
// calculate jacobian and surface normal
vec3d nu(0,0,0);
double J = surf.jac0(face, n, nu);
// evaluate element Jacobian and shape function derivatives
// at this integration point
vec3d& ksi = data.ksi[m];
invjac0(el, ksi.x, ksi.y, ksi.z, Ji);
el.shape_deriv(Gr, Gs, Gt, ksi.x, ksi.y, ksi.z);
// loop over element nodes
for (int j=0; j<neln; ++j)
{
// calculate global gradient of shape functions
// note that we need the transposed of Ji, not Ji itself !
double G[3];
G[0] = Ji[0][0]*Gr[j]+Ji[1][0]*Gs[j]+Ji[2][0]*Gt[j];
G[1] = Ji[0][1]*Gr[j]+Ji[1][1]*Gs[j]+Ji[2][1]*Gt[j];
G[2] = Ji[0][2]*Gr[j]+Ji[1][2]*Gs[j]+Ji[2][2]*Gt[j];
// put it all together
double f[3] = {0}, Nu[3] = {nu.x, nu.y, nu.z};
for (int k=0; k<3; ++k)
for (int l=0; l<3; ++l)
{
f[0] += Qavg(0,k,l)*G[k]*Nu[l];
f[1] += Qavg(1,k,l)*G[k]*Nu[l];
f[2] += Qavg(2,k,l)*G[k]*Nu[l];
}
// the negative sign is because we need to subtract the internal forces
// from the residual
fe[3*j ] -= f[0]*gw[n]*J*sgn;
fe[3*j+1] -= f[1]*gw[n]*J*sgn;
fe[3*j+2] -= f[2]*gw[n]*J*sgn;
}
}
// unpack the LM values
UnpackLM(el, lm);
// assemble
R.Assemble(el.m_node, lm, fe);
}
// don't forgot to increment data counter
nd += nint;
}
}
//-----------------------------------------------------------------------------
void FEElasticSolidDomain2O::InternalForcesDG2(FEGlobalVector& R)
{
FEMesh& mesh = *GetMesh();
FESurface& surf = *m_surf.GetSurface();
vec3d X[FEElement::MAX_NODES];
vec3d G[FEElement::MAX_NODES];
mat3d H[FEElement::MAX_NODES];
vector<double> fe;
vector<int> lm;
// loop over all internal surface elements
int nd = 0;
int NF = m_surf.Elements();
for (int i=0; i<NF; ++i)
{
// get the next surface element
FESurfaceElement& face = m_surf.Element(i);
int nfn = face.Nodes();
int nint = face.GaussPoints();
// loop over both sides
for (int m=0; m<2; ++m)
{
FESolidElement& el = static_cast<FESolidElement&>(*face.m_elem[m].pe);
int neln = el.Nodes();
// get the initial nodal positions
for (int j=0; j<neln; ++j) X[j] = mesh.Node(el.m_node[j]).m_r0;
// allocate force vector
int ndof = neln*3;
fe.assign(ndof, 0.0);
// loop over integration points
double* gw = face.GaussWeights();
for (int n=0; n<nint; ++n)
{
// get facet data
FEInternalSurface2O::Data& data = m_surf.GetData(nd + n);
const tens6d& J0 = data.J0[m];
const tens5d& H0 = data.H0[m];
const mat3d& Du = data.DgradU;
// calculate jacobian and surface normal
vec3d nu(0,0,0);
double J = surf.jac0(face, n, nu);
double Nu[3] = {nu.x, nu.y, nu.z};
// evaluate element shape function derivatives
// at this integration point
vec3d& ksi = data.ksi[m];
ShapeGradient0(el, ksi.x, ksi.y, ksi.z, G);
shape_gradient2(el, X, ksi.x, ksi.y, ksi.z, H);
for (int a=0; a<neln; ++a)
{
double Ax[3][3][3] = {0};
double Ay[3][3][3] = {0};
double Az[3][3][3] = {0};
for (int j=0; j<3; ++j)
for (int k=0; k<3; ++k)
{
for (int q=0; q<3; ++q)
{
for (int r=0; r<3; ++r)
{
Ax[0][j][k] += J0(0, j, k, 0, q, r)*H[a](q ,r);
Ax[1][j][k] += J0(1, j, k, 0, q, r)*H[a](q ,r);
Ax[2][j][k] += J0(2, j, k, 0, q, r)*H[a](q ,r);
Ay[0][j][k] += J0(0, j, k, 1, q, r)*H[a](q ,r);
Ay[1][j][k] += J0(1, j, k, 1, q, r)*H[a](q ,r);
Ay[2][j][k] += J0(2, j, k, 1, q, r)*H[a](q ,r);
Az[0][j][k] += J0(0, j, k, 2, q, r)*H[a](q ,r);
Az[1][j][k] += J0(1, j, k, 2, q, r)*H[a](q ,r);
Az[2][j][k] += J0(2, j, k, 2, q, r)*H[a](q ,r);
}
}
Ax[0][j][k] += H0(0, j, k, 0, 0)*G[a].x + H0(0, j, k, 0, 1)*G[a].y + H0(0, j, k, 0, 2)*G[a].z;
Ax[1][j][k] += H0(1, j, k, 0, 0)*G[a].x + H0(1, j, k, 0, 1)*G[a].y + H0(1, j, k, 0, 2)*G[a].z;
Ax[2][j][k] += H0(2, j, k, 0, 0)*G[a].x + H0(2, j, k, 0, 1)*G[a].y + H0(2, j, k, 0, 2)*G[a].z;
Ay[0][j][k] += H0(0, j, k, 1, 0)*G[a].x + H0(0, j, k, 1, 1)*G[a].y + H0(0, j, k, 1, 2)*G[a].z;
Ay[1][j][k] += H0(1, j, k, 1, 0)*G[a].x + H0(1, j, k, 1, 1)*G[a].y + H0(1, j, k, 1, 2)*G[a].z;
Ay[2][j][k] += H0(2, j, k, 1, 0)*G[a].x + H0(2, j, k, 1, 1)*G[a].y + H0(2, j, k, 1, 2)*G[a].z;
Az[0][j][k] += H0(0, j, k, 2, 0)*G[a].x + H0(0, j, k, 2, 1)*G[a].y + H0(0, j, k, 2, 2)*G[a].z;
Az[1][j][k] += H0(1, j, k, 2, 0)*G[a].x + H0(1, j, k, 2, 1)*G[a].y + H0(1, j, k, 2, 2)*G[a].z;
Az[2][j][k] += H0(2, j, k, 2, 0)*G[a].x + H0(2, j, k, 2, 1)*G[a].y + H0(2, j, k, 2, 2)*G[a].z;
}
double fa[3] = {0};
for (int j=0; j<3; ++j)
for (int k=0; k<3; ++k)
{
fa[0] += Du(0,j)*Ax[0][j][k]*Nu[k] + Du(1,j)*Ax[1][j][k]*Nu[k] + Du(2,j)*Ax[2][j][k]*Nu[k];
fa[1] += Du(0,j)*Ay[0][j][k]*Nu[k] + Du(1,j)*Ay[1][j][k]*Nu[k] + Du(2,j)*Ay[2][j][k]*Nu[k];
fa[2] += Du(0,j)*Az[0][j][k]*Nu[k] + Du(1,j)*Az[1][j][k]*Nu[k] + Du(2,j)*Az[2][j][k]*Nu[k];
}
// the negative sign is because we need to subtract the internal forces
// from the residual
fe[3*a ] -= fa[0]*J*gw[n]*0.5;
fe[3*a+1] -= fa[1]*J*gw[n]*0.5;
fe[3*a+2] -= fa[2]*J*gw[n]*0.5;
}
}
// unpack the LM values
UnpackLM(el, lm);
// assemble
R.Assemble(el.m_node, lm, fe);
}
// don't forgot to increment data counter
nd += nint;
}
}
//-----------------------------------------------------------------------------
//! Evaluate contribution of discontinuous-Galerkin enforcement of stress flux.
void FEElasticSolidDomain2O::InternalForcesDG3(FEGlobalVector& R)
{
FEMesh& mesh = *GetMesh();
FESurface& surf = *m_surf.GetSurface();
FEElasticMaterial2O* pmat = dynamic_cast<FEElasticMaterial2O*>(GetMaterial());
assert(pmat);
double beta = pmat->m_beta;
double h = m_surf.GetElementSize();
mat3d Ji;
double Gr[FEElement::MAX_NODES];
double Gs[FEElement::MAX_NODES];
double Gt[FEElement::MAX_NODES];
vector<double> fe;
vector<int> lm;
// loop over all internal surfaces elements
int nd = 0;
int NF = m_surf.Elements();
for (int i=0; i<NF; ++i)
{
// get the next surface
FESurfaceElement& face = m_surf.Element(i);
int nfn = face.Nodes();
int nint = face.GaussPoints();
// loop over both sides
for (int m=0; m<2; ++m)
{
// evaluate the spatial gradient of shape functions
FESolidElement& el = static_cast<FESolidElement&>(*face.m_elem[m].pe);
int neln = el.Nodes();
// sign
double sgn = (m==0 ? -1.0 : 1.0);
// allocate force vector
int ndof = neln*3;
fe.assign(ndof, 0.0);
// loop over all the integration points
double* gw = face.GaussWeights();
for (int n=0; n<nint; ++n)
{
// get facet data
FEInternalSurface2O::Data& data = m_surf.GetData(nd + n);
// average stiffness across interface
tens6d& J0avg = data.J0avg;
// displacement gradient jump
const mat3d& Du = data.DgradU;
// calculate jacobian and surface normal
vec3d nu(0,0,0);
double J = surf.jac0(face, n, nu);
// evaluate element Jacobian and shape function derivatives
// at this integration point
vec3d& ksi = data.ksi[m];
invjac0(el, ksi.x, ksi.y, ksi.z, Ji);
el.shape_deriv(Gr, Gs, Gt, ksi.x, ksi.y, ksi.z);
// loop over element nodes
for (int j=0; j<neln; ++j)
{
// calculate global gradient of shape functions
// note that we need the transposed of Ji, not Ji itself !
double G[3];
G[0] = Ji[0][0]*Gr[j]+Ji[1][0]*Gs[j]+Ji[2][0]*Gt[j];
G[1] = Ji[0][1]*Gr[j]+Ji[1][1]*Gs[j]+Ji[2][1]*Gt[j];
G[2] = Ji[0][2]*Gr[j]+Ji[1][2]*Gs[j]+Ji[2][2]*Gt[j];
// put it all together
double f[3] = {0}, Nu[3] = {nu.x, nu.y, nu.z};
for (int k=0; k<3; ++k)
for (int l=0; l<3; ++l)
for (int p=0; p<3; ++p)
for (int q=0; q<3; ++q)
for (int r=0; r<3; ++r)
{
f[0] += Du(k,l)*Nu[p]*J0avg(k,l,p,0,q,r)*G[q]*Nu[r];
f[1] += Du(k,l)*Nu[p]*J0avg(k,l,p,1,q,r)*G[q]*Nu[r];
f[2] += Du(k,l)*Nu[p]*J0avg(k,l,p,2,q,r)*G[q]*Nu[r];
}
// the negative sign is because we need to subtract the internal forces
// from the residual
fe[3*j ] -= f[0]*gw[n]*J*sgn*beta/h;
fe[3*j+1] -= f[1]*gw[n]*J*sgn*beta/h;
fe[3*j+2] -= f[2]*gw[n]*J*sgn*beta/h;
}
}
// unpack the LM values
UnpackLM(el, lm);
// assemble
R.Assemble(el.m_node, lm, fe);
}
// don't forgot to increment data counter
nd += nint;
}
}
//-----------------------------------------------------------------------------
//! calculates the internal equivalent nodal forces for solid elements
void FEElasticSolidDomain2O::ElementInternalForce(FESolidElement& el, vector<double>& fe)
{
// contribution from stress
ElementInternalForce_PF(el, fe);
// contriubtion from higher-order stress
ElementInternalForce_QG(el, fe);
}
//-----------------------------------------------------------------------------
void FEElasticSolidDomain2O::ElementInternalForce_PF(FESolidElement& el, vector<double>& fe)
{
double Ji[3][3];
int nint = el.GaussPoints();
int neln = el.Nodes();
double* gw = el.GaussWeights();
// repeat for all integration points
for (int n=0; n<nint; ++n)
{
FEMaterialPoint& mp = *el.GetMaterialPoint(n);
FEElasticMaterialPoint& pt = *(mp.ExtractData<FEElasticMaterialPoint>());
// calculate the jacobian and its derivative
// (and multiply by integration weight)
double detJt = invjact(el, Ji, n)*gw[n];
// get the stress vector for this integration point
mat3ds& s = pt.m_s;
double* Gr = el.Gr(n);
double* Gs = el.Gs(n);
double* Gt = el.Gt(n);
// --- first-order contribution ---
for (int i=0; i<neln; ++i)
{
// calculate global gradient of shape functions
// note that we need the transposed of Ji, not Ji itself !
double Gx = Ji[0][0]*Gr[i]+Ji[1][0]*Gs[i]+Ji[2][0]*Gt[i];
double Gy = Ji[0][1]*Gr[i]+Ji[1][1]*Gs[i]+Ji[2][1]*Gt[i];
double Gz = Ji[0][2]*Gr[i]+Ji[1][2]*Gs[i]+Ji[2][2]*Gt[i];
// calculate internal force
// the '-' sign is so that the internal forces get subtracted
// from the global residual vector
fe[3*i ] -= (Gx*s.xx() + Gy*s.xy() + Gz*s.xz())*detJt;
fe[3*i+1] -= (Gx*s.xy() + Gy*s.yy() + Gz*s.yz())*detJt;
fe[3*i+2] -= (Gx*s.xz() + Gy*s.yz() + Gz*s.zz())*detJt;
}
}
}
//-----------------------------------------------------------------------------
void FEElasticSolidDomain2O::ElementInternalForce_QG(FESolidElement& el, vector<double>& fe)
{
// get the nodal positions
vec3d X[FEElement::MAX_NODES];
FEMesh& mesh = *GetMesh();
int neln = el.Nodes();
for (int i=0; i<neln; ++i) X[i] = mesh.Node(el.m_node[i]).m_r0;
mat3d H[FEElement::MAX_NODES];
// repeat for all integration points
int nint = el.GaussPoints();
double* gw = el.GaussWeights();
for (int n=0; n<nint; ++n)
{
FEMaterialPoint& mp = *el.GetMaterialPoint(n);
FEElasticMaterialPoint2O& pt2O = *(mp.ExtractData<FEElasticMaterialPoint2O>());
// get the higher-order stress
const tens3drs& Q = pt2O.m_Q;
// we'll evaluate this term in the material frame
// so we need the Jacobian with respect to the reference configuration
double J0 = detJ0(el, n);
// shape function derivatives
shape_gradient2(el, X, n, H);
// loop over nodes
for (int a=0; a<neln; ++a)
{
mat3d& Ha = H[a];
// calculate internal force
// the '-' sign is so that the internal forces get subtracted
// from the global residual vector
for (int j=0; j<3; ++j)
for (int k=0; k<3; ++k)
{
fe[3*a ] -= (Q(0,j,k)*Ha[j][k])*J0*gw[n];
fe[3*a+1] -= (Q(1,j,k)*Ha[j][k])*J0*gw[n];
fe[3*a+2] -= (Q(2,j,k)*Ha[j][k])*J0*gw[n];
}
}
}
}
//-----------------------------------------------------------------------------
void FEElasticSolidDomain2O::StiffnessMatrix(FELinearSystem& LS)
{
// repeat over all solid elements
int NE = (int)m_Elem.size();
FETimeInfo tp = GetFEModel()->GetTime();
#pragma omp parallel for shared (NE)
for (int iel=0; iel<NE; ++iel)
{
FESolidElement& el = m_Elem[iel];
// element stiffness matrix
FEElementMatrix ke(el);
// create the element's stiffness matrix
int ndof = 3*el.Nodes();
ke.resize(ndof, ndof);
ke.zero();
// calculate element stiffness
ElementStiffness(tp, iel, ke);
// get the element's LM vector
vector<int> lm;
UnpackLM(el, lm);
ke.SetIndices(lm);
// assemble element matrix in global stiffness matrix
LS.Assemble(ke);
}
// stiffness matrix from discontinuous Galerkin
StiffnessMatrixDG(LS);
}
//-----------------------------------------------------------------------------
void FEElasticSolidDomain2O::StiffnessMatrixDG(FELinearSystem& LS)
{
FEElasticMaterial2O* pmat = dynamic_cast<FEElasticMaterial2O*>(GetMaterial());
assert(pmat);
// get stiffness flags
bool bKDG1 = pmat->m_bKDG1;
bool bKDG2 = pmat->m_bKDG2;
bool bKDG3 = pmat->m_bKDG3;
if ((bKDG1==false)&&(bKDG2==false)&&(bKDG3==false)) return;
FEElementMatrix ke;
vector<int> lm;
vector<int> en;
FEMesh& mesh = *GetMesh();
// loop over all internal facets
int nd = 0;
int NF = m_surf.Elements();
for (int n=0; n<NF; ++n)
{
// get the next surface element
FESurfaceElement& el = m_surf.Element(n);
// get the solid elements from this interface element
FESolidElement& ela = static_cast<FESolidElement&>(*el.m_elem[1].pe);
FESolidElement& elb = static_cast<FESolidElement&>(*el.m_elem[0].pe);
int nelna = ela.Nodes();
int nelnb = elb.Nodes();
// init element stiffness
int ndof = 3*(nelna + nelnb);
ke.resize(ndof, ndof);
ke.zero();
// get the element stiffness matrix
if (bKDG1) ElementStiffnessMatrixDG1(el, &m_surf.GetData(nd), ke);
if (bKDG2) ElementStiffnessMatrixDG2(el, &m_surf.GetData(nd), ke);
if (bKDG3) ElementStiffnessMatrixDG3(el, &m_surf.GetData(nd), ke);
// setup the LM vector
// TODO: this assumes that the X,Y,Z degrees of freedom are 0,1,2 respectively
lm.resize(ndof);
for (int i=0; i<nelna; ++i)
{
vector<int>& id = mesh.Node(ela.m_node[i]).m_ID;
lm[3*i ] = id[0];
lm[3*i+1] = id[1];
lm[3*i+2] = id[2];
}
for (int i=0; i<nelnb; ++i)
{
vector<int>& id = mesh.Node(elb.m_node[i]).m_ID;
lm[3*(nelna+i) ] = id[0];
lm[3*(nelna+i)+1] = id[1];
lm[3*(nelna+i)+2] = id[2];
}
ke.SetIndices(lm);
// setup the en vector
en.resize(nelna + nelnb);
for (int i=0; i<nelna; i++) en[i ] = ela.m_node[i];
for (int i=0; i<nelnb; i++) en[i + nelna] = elb.m_node[i];
ke.SetNodes(en);
// assemble into global matrix
LS.Assemble(ke);
// don't forget to increment data counter
nd += el.GaussPoints();
}
}
//-----------------------------------------------------------------------------
void FEElasticSolidDomain2O::ElementStiffnessMatrixDG1(FESurfaceElement& face, FEInternalSurface2O::Data* pdata, matrix& ke)
{
FESurface& surf = *m_surf.GetSurface();
FEMesh& mesh = *GetMesh();
// get the solid elements of this interface element
FESolidElement& ela = static_cast<FESolidElement&>(*face.m_elem[1].pe);
FESolidElement& elb = static_cast<FESolidElement&>(*face.m_elem[0].pe);
// get the number nodes of each element
int nelna = ela.Nodes();
int nelnb = elb.Nodes();
// initialize the element stiffness matrix
int ndof = 3*(nelna + nelnb);
// nodal coordinates
vec3d Xa[FEElement::MAX_NODES];
vec3d Xb[FEElement::MAX_NODES];
for (int i=0; i<nelna; ++i) Xa[i] = mesh.Node(ela.m_node[i]).m_r0;
for (int i=0; i<nelnb; ++i) Xb[i] = mesh.Node(elb.m_node[i]).m_r0;
// shape function derivatives
vec3d Ga[FEElement::MAX_NODES];
vec3d Gb[FEElement::MAX_NODES];
mat3d Ha[FEElement::MAX_NODES];
mat3d Hb[FEElement::MAX_NODES];
// loop over all integration points
int nint = face.GaussPoints();
double* gw = face.GaussWeights();
for (int ng=0; ng<nint; ++ng)
{
// get the integration point data
FEInternalSurface2O::Data& data = pdata[ng];
// Jacobian and normal at this integration point
vec3d nu;
double J0 = surf.jac0(face, ng, nu);
double Nu[3] = {nu.x, nu.y, nu.z};
// shape function gradients at this integration point
ShapeGradient0(ela, data.ksi[1].x, data.ksi[1].y, data.ksi[1].z, Ga);
ShapeGradient0(elb, data.ksi[0].x, data.ksi[0].y, data.ksi[0].z, Gb);
shape_gradient2(ela, Xa, data.ksi[1].x, data.ksi[1].y, data.ksi[1].z, Ha);
shape_gradient2(elb, Xb, data.ksi[0].x, data.ksi[0].y, data.ksi[0].z, Hb);
// The a-a term
for (int a=0; a<nelna; ++a)
for (int b=0; b<nelna; ++b)
{
vec3d& GA = Ga[a];
vec3d& GB = Ga[b];
mat3d& G2B = Ha[b];
const tens5d& H = data.H[1];
const tens6d& J = data.J[1];
mat3d kab;
for (int i=0; i<3; ++i)
for (int k=0; k<3; ++k)
{
double kik = 0.0;
for (int l=0; l<3; ++l)
{
kik += GA.x*H(i,0,l,k,0)*GB.x + GA.x*H(i,0,l,k,1)*GB.y + GA.x*H(i,0,l,k,2)*GB.z;
kik += GA.y*H(i,1,l,k,0)*GB.x + GA.y*H(i,1,l,k,1)*GB.y + GA.y*H(i,1,l,k,2)*GB.z;
kik += GA.z*H(i,2,l,k,0)*GB.x + GA.z*H(i,2,l,k,1)*GB.y + GA.z*H(i,2,l,k,2)*GB.z;
}
kab[i][k] = kik*J0*gw[ng]*0.5;
}
for (int i=0; i<3; ++i)
for (int k=0; k<3; ++k)
{
double kik = 0.0;
for (int l=0; l<3; ++l)
for (int m=0; m<3; ++m)
for (int n=0; n<3; ++n)
{
kik += GA.x*J(i,0,k,l,m,n)*G2B[l][m] + GA.y*J(i,1,k,l,m,n)*G2B[l][m] + GA.z*J(i,2,k,l,m,n)*G2B[l][m];
}
kab[i][k] += kik*J0*gw[ng]*0.5;
}
// add it to the element matrix
ke.add(3*a, 3*b, kab);
}
// The a-b term
for (int a=0; a<nelna; ++a)
for (int b=0; b<nelnb; ++b)
{
vec3d& GA = Ga[a];
vec3d& GB = Gb[b];
mat3d& G2B = Hb[b];
const tens5d& H = data.H[0];
const tens6d& J = data.J[0];
mat3d kab;
for (int i=0; i<3; ++i)
for (int k=0; k<3; ++k)
{
double kik = 0.0;
for (int l=0; l<3; ++l)
{
kik += GA.x*H(i,0,l,k,0)*GB.x + GA.x*H(i,0,l,k,1)*GB.y + GA.x*H(i,0,l,k,2)*GB.z;
kik += GA.y*H(i,1,l,k,0)*GB.x + GA.y*H(i,1,l,k,1)*GB.y + GA.y*H(i,1,l,k,2)*GB.z;
kik += GA.z*H(i,2,l,k,0)*GB.x + GA.z*H(i,2,l,k,1)*GB.y + GA.z*H(i,2,l,k,2)*GB.z;
}
kab[i][k] = kik*J0*gw[ng]*0.5;
}
for (int i=0; i<3; ++i)
for (int k=0; k<3; ++k)
{
double kik = 0.0;
for (int l=0; l<3; ++l)
for (int m=0; m<3; ++m)
for (int n=0; n<3; ++n)
{
kik += GA.x*J(i,0,k,l,m,n)*G2B[l][m] + GA.y*J(i,1,k,l,m,n)*G2B[l][m] + GA.z*J(i,2,k,l,m,n)*G2B[l][m];
}
kab[i][k] += kik*J0*gw[ng]*0.5;
}
// add it to the element matrix
ke.add(3*a, 3*(nelna + b), kab);
}
// The b-a term
for (int a=0; a<nelnb; ++a)
for (int b=0; b<nelna; ++b)
{
vec3d& GA = Gb[a];
vec3d& GB = Ga[b];
mat3d& G2B = Ha[b];
const tens5d& H = data.H[1];
const tens6d& J = data.J[1];
mat3d kab;
for (int i=0; i<3; ++i)
for (int k=0; k<3; ++k)
{
double kik = 0.0;
for (int l=0; l<3; ++l)
{
kik += GA.x*H(i,0,l,k,0)*GB.x + GA.x*H(i,0,l,k,1)*GB.y + GA.x*H(i,0,l,k,2)*GB.z;
kik += GA.y*H(i,1,l,k,0)*GB.x + GA.y*H(i,1,l,k,1)*GB.y + GA.y*H(i,1,l,k,2)*GB.z;
kik += GA.z*H(i,2,l,k,0)*GB.x + GA.z*H(i,2,l,k,1)*GB.y + GA.z*H(i,2,l,k,2)*GB.z;
}
kab[i][k] = kik*J0*gw[ng]*0.5;
}
for (int i=0; i<3; ++i)
for (int k=0; k<3; ++k)
{
double kik = 0.0;
for (int l=0; l<3; ++l)
for (int m=0; m<3; ++m)
for (int n=0; n<3; ++n)
{
kik += GA.x*J(i,0,k,l,m,n)*G2B[l][m] + GA.y*J(i,1,k,l,m,n)*G2B[l][m] + GA.z*J(i,2,k,l,m,n)*G2B[l][m];
}
kab[i][k] += kik*J0*gw[ng]*0.5;
}
// add it to the element matrix
ke.sub(3*(nelna + a), 3*b, kab);
}
// The b-b term
for (int a=0; a<nelnb; ++a)
for (int b=0; b<nelnb; ++b)
{
vec3d& GA = Gb[a];
vec3d& GB = Gb[b];
mat3d& G2B = Hb[b];
const tens5d& H = data.H[0];
const tens6d& J = data.J[0];
mat3d kab;
for (int i=0; i<3; ++i)
for (int k=0; k<3; ++k)
{
double kik = 0.0;
for (int l=0; l<3; ++l)
{
kik += GA.x*H(i,0,l,k,0)*GB.x + GA.x*H(i,0,l,k,1)*GB.y + GA.x*H(i,0,l,k,2)*GB.z;
kik += GA.y*H(i,1,l,k,0)*GB.x + GA.y*H(i,1,l,k,1)*GB.y + GA.y*H(i,1,l,k,2)*GB.z;
kik += GA.z*H(i,2,l,k,0)*GB.x + GA.z*H(i,2,l,k,1)*GB.y + GA.z*H(i,2,l,k,2)*GB.z;
}
kab[i][k] = kik*J0*gw[ng]*0.5;
}
for (int i=0; i<3; ++i)
for (int k=0; k<3; ++k)
{
double kik = 0.0;
for (int l=0; l<3; ++l)
for (int m=0; m<3; ++m)
for (int n=0; n<3; ++n)
{
kik += GA.x*J(i,0,k,l,m,n)*G2B[l][m] + GA.y*J(i,1,k,l,m,n)*G2B[l][m] + GA.z*J(i,2,k,l,m,n)*G2B[l][m];
}
kab[i][k] += kik*J0*gw[ng]*0.5;
}
// add it to the element matrix
ke.sub(3*(nelna + a), 3*(nelna + b), kab);
}
}
}
//-----------------------------------------------------------------------------
void FEElasticSolidDomain2O::ElementStiffnessMatrixDG2(FESurfaceElement& face, FEInternalSurface2O::Data* pdata, matrix& ke)
{
// get the mesh and surface
FEMesh& mesh = *GetMesh();
FESurface& surf = *m_surf.GetSurface();
// get the solid elements of this interface element
FESolidElement& ela = static_cast<FESolidElement&>(*face.m_elem[1].pe);
FESolidElement& elb = static_cast<FESolidElement&>(*face.m_elem[0].pe);
// get the number nodes of each element
int nelna = ela.Nodes();
int nelnb = elb.Nodes();
// initialize the element stiffness matrix
int ndof = 3*(nelna + nelnb);
// shape function derivatives
vec3d G1a[FEElement::MAX_NODES];
vec3d G1b[FEElement::MAX_NODES];
mat3d G2a[FEElement::MAX_NODES];
mat3d G2b[FEElement::MAX_NODES];
// nodal coordinates
vec3d Xa[FEElement::MAX_NODES];
vec3d Xb[FEElement::MAX_NODES];
for (int i=0; i<nelna; ++i) Xa[i] = mesh.Node(ela.m_node[i]).m_r0;
for (int i=0; i<nelnb; ++i) Xb[i] = mesh.Node(elb.m_node[i]).m_r0;
// loop over all integration points
int nint = face.GaussPoints();
double* gw = face.GaussWeights();
for (int n=0; n<nint; ++n)
{
// get the integration point data
FEInternalSurface2O::Data& data = pdata[n];
// Jacobian and normal at this integration point
vec3d nu;
double J0 = surf.jac0(face, n, nu);
double Nu[3] = {nu.x, nu.y, nu.z};
// shape function gradients at this integration point
ShapeGradient0(ela, data.ksi[1].x, data.ksi[1].y, data.ksi[1].z, G1a);
ShapeGradient0(elb, data.ksi[0].x, data.ksi[0].y, data.ksi[0].z, G1b);
shape_gradient2(ela, Xa, data.ksi[1].x, data.ksi[1].y, data.ksi[1].z, G2a);
shape_gradient2(elb, Xb, data.ksi[0].x, data.ksi[0].y, data.ksi[0].z, G2b);
// stiffnesses
const tens5d& Ha = data.H0[1];
const tens5d& Hb = data.H0[0];
const tens6d& Ja = data.J0[1];
const tens6d& Jb = data.J0[0];
// The a-a term
for (int a=0; a<nelna; ++a)
for (int b=0; b<nelna; ++b)
{
double Ax[3][3][3] = {0};
double Ay[3][3][3] = {0};
double Az[3][3][3] = {0};
for (int i=0; i<3; ++i)
for (int j=0; j<3; ++j)
for (int k=0; k<3; ++k)
{
for (int q=0; q<3; ++q)
for (int r=0; r<3; ++r)
{
Ax[i][j][k] += Ja(i, j, k, 0, q, r)*G2a[a](q ,r);
Ay[i][j][k] += Ja(i, j, k, 1, q, r)*G2a[a](q ,r);
Az[i][j][k] += Ja(i, j, k, 2, q, r)*G2a[a](q ,r);
}
Ax[i][j][k] += Ha(i, j, k, 0, 0)*G1a[a].x + Ha(i, j, k, 0, 1)*G1a[a].y + Ha(i, j, k, 0, 2)*G1a[a].z;
Ay[i][j][k] += Ha(i, j, k, 1, 0)*G1a[a].x + Ha(i, j, k, 1, 1)*G1a[a].y + Ha(i, j, k, 1, 2)*G1a[a].z;
Az[i][j][k] += Ha(i, j, k, 2, 0)*G1a[a].x + Ha(i, j, k, 2, 1)*G1a[a].y + Ha(i, j, k, 2, 2)*G1a[a].z;
}
mat3d kab; kab.zero();
for (int k=0; k<3; ++k)
{
kab(0,0) += G1a[b].x*Ax[0][0][k]*Nu[k] + G1a[b].y*Ax[0][1][k]*Nu[k] + G1a[b].z*Ax[0][2][k]*Nu[k];
kab(0,1) += G1a[b].x*Ax[1][0][k]*Nu[k] + G1a[b].y*Ax[1][1][k]*Nu[k] + G1a[b].z*Ax[1][2][k]*Nu[k];
kab(0,2) += G1a[b].x*Ax[2][0][k]*Nu[k] + G1a[b].y*Ax[2][1][k]*Nu[k] + G1a[b].z*Ax[2][2][k]*Nu[k];
kab(1,0) += G1a[b].x*Ay[0][0][k]*Nu[k] + G1a[b].y*Ay[0][1][k]*Nu[k] + G1a[b].z*Ay[0][2][k]*Nu[k];
kab(1,1) += G1a[b].x*Ay[1][0][k]*Nu[k] + G1a[b].y*Ay[1][1][k]*Nu[k] + G1a[b].z*Ay[1][2][k]*Nu[k];
kab(1,2) += G1a[b].x*Ay[2][0][k]*Nu[k] + G1a[b].y*Ay[2][1][k]*Nu[k] + G1a[b].z*Ay[2][2][k]*Nu[k];
kab(2,0) += G1a[b].x*Az[0][0][k]*Nu[k] + G1a[b].y*Az[0][1][k]*Nu[k] + G1a[b].z*Az[0][2][k]*Nu[k];
kab(2,1) += G1a[b].x*Az[1][0][k]*Nu[k] + G1a[b].y*Az[1][1][k]*Nu[k] + G1a[b].z*Az[1][2][k]*Nu[k];
kab(2,2) += G1a[b].x*Az[2][0][k]*Nu[k] + G1a[b].y*Az[2][1][k]*Nu[k] + G1a[b].z*Az[2][2][k]*Nu[k];
}
// multiply by weights
kab *= J0*gw[n]*0.5;
ke.add(3*a, 3*b, kab);
}
// The a-b term
for (int a=0; a<nelna; ++a)
for (int b=0; b<nelnb; ++b)
{
double Ax[3][3][3] = {0};
double Ay[3][3][3] = {0};
double Az[3][3][3] = {0};
for (int i=0; i<3; ++i)
for (int j=0; j<3; ++j)
for (int k=0; k<3; ++k)
{
for (int q=0; q<3; ++q)
for (int r=0; r<3; ++r)
{
Ax[i][j][k] += Ja(i, j, k, 0, q, r)*G2a[a](q ,r);
Ay[i][j][k] += Ja(i, j, k, 1, q, r)*G2a[a](q ,r);
Az[i][j][k] += Ja(i, j, k, 2, q, r)*G2a[a](q ,r);
}
Ax[i][j][k] += Ha(i, j, k, 0, 0)*G1a[a].x + Ha(i, j, k, 0, 1)*G1a[a].y + Ha(i, j, k, 0, 2)*G1a[a].z;
Ay[i][j][k] += Ha(i, j, k, 1, 0)*G1a[a].x + Ha(i, j, k, 1, 1)*G1a[a].y + Ha(i, j, k, 1, 2)*G1a[a].z;
Az[i][j][k] += Ha(i, j, k, 2, 0)*G1a[a].x + Ha(i, j, k, 2, 1)*G1a[a].y + Ha(i, j, k, 2, 2)*G1a[a].z;
}
mat3d kab; kab.zero();
for (int k=0; k<3; ++k)
{
kab(0,0) += G1b[b].x*Ax[0][0][k]*Nu[k] + G1b[b].y*Ax[0][1][k]*Nu[k] + G1b[b].z*Ax[0][2][k]*Nu[k];
kab(0,1) += G1b[b].x*Ax[1][0][k]*Nu[k] + G1b[b].y*Ax[1][1][k]*Nu[k] + G1b[b].z*Ax[1][2][k]*Nu[k];
kab(0,2) += G1b[b].x*Ax[2][0][k]*Nu[k] + G1b[b].y*Ax[2][1][k]*Nu[k] + G1b[b].z*Ax[2][2][k]*Nu[k];
kab(1,0) += G1b[b].x*Ay[0][0][k]*Nu[k] + G1b[b].y*Ay[0][1][k]*Nu[k] + G1b[b].z*Ay[0][2][k]*Nu[k];
kab(1,1) += G1b[b].x*Ay[1][0][k]*Nu[k] + G1b[b].y*Ay[1][1][k]*Nu[k] + G1b[b].z*Ay[1][2][k]*Nu[k];
kab(1,2) += G1b[b].x*Ay[2][0][k]*Nu[k] + G1b[b].y*Ay[2][1][k]*Nu[k] + G1b[b].z*Ay[2][2][k]*Nu[k];
kab(2,0) += G1b[b].x*Az[0][0][k]*Nu[k] + G1b[b].y*Az[0][1][k]*Nu[k] + G1b[b].z*Az[0][2][k]*Nu[k];
kab(2,1) += G1b[b].x*Az[1][0][k]*Nu[k] + G1b[b].y*Az[1][1][k]*Nu[k] + G1b[b].z*Az[1][2][k]*Nu[k];
kab(2,2) += G1b[b].x*Az[2][0][k]*Nu[k] + G1b[b].y*Az[2][1][k]*Nu[k] + G1b[b].z*Az[2][2][k]*Nu[k];
}
// multiply by weights
kab *= J0*gw[n]*0.5;
ke.sub(3*a, 3*(nelna + b), kab);
}
// The b-a term
for (int a=0; a<nelnb; ++a)
for (int b=0; b<nelna; ++b)
{
double Ax[3][3][3] = {0};
double Ay[3][3][3] = {0};
double Az[3][3][3] = {0};
for (int i=0; i<3; ++i)
for (int j=0; j<3; ++j)
for (int k=0; k<3; ++k)
{
for (int q=0; q<3; ++q)
for (int r=0; r<3; ++r)
{
Ax[i][j][k] += Jb(i, j, k, 0, q, r)*G2b[a](q ,r);
Ay[i][j][k] += Jb(i, j, k, 1, q, r)*G2b[a](q ,r);
Az[i][j][k] += Jb(i, j, k, 2, q, r)*G2b[a](q ,r);
}
Ax[i][j][k] += Hb(i, j, k, 0, 0)*G1b[a].x + Hb(i, j, k, 0, 1)*G1b[a].y + Hb(i, j, k, 0, 2)*G1b[a].z;
Ay[i][j][k] += Hb(i, j, k, 1, 0)*G1b[a].x + Hb(i, j, k, 1, 1)*G1b[a].y + Hb(i, j, k, 1, 2)*G1b[a].z;
Az[i][j][k] += Hb(i, j, k, 2, 0)*G1b[a].x + Hb(i, j, k, 2, 1)*G1b[a].y + Hb(i, j, k, 2, 2)*G1b[a].z;
}
mat3d kab; kab.zero();
for (int k=0; k<3; ++k)
{
kab(0,0) += G1a[b].x*Ax[0][0][k]*Nu[k] + G1a[b].y*Ax[0][1][k]*Nu[k] + G1a[b].z*Ax[0][2][k]*Nu[k];
kab(0,1) += G1a[b].x*Ax[1][0][k]*Nu[k] + G1a[b].y*Ax[1][1][k]*Nu[k] + G1a[b].z*Ax[1][2][k]*Nu[k];
kab(0,2) += G1a[b].x*Ax[2][0][k]*Nu[k] + G1a[b].y*Ax[2][1][k]*Nu[k] + G1a[b].z*Ax[2][2][k]*Nu[k];
kab(1,0) += G1a[b].x*Ay[0][0][k]*Nu[k] + G1a[b].y*Ay[0][1][k]*Nu[k] + G1a[b].z*Ay[0][2][k]*Nu[k];
kab(1,1) += G1a[b].x*Ay[1][0][k]*Nu[k] + G1a[b].y*Ay[1][1][k]*Nu[k] + G1a[b].z*Ay[1][2][k]*Nu[k];
kab(1,2) += G1a[b].x*Ay[2][0][k]*Nu[k] + G1a[b].y*Ay[2][1][k]*Nu[k] + G1a[b].z*Ay[2][2][k]*Nu[k];
kab(2,0) += G1a[b].x*Az[0][0][k]*Nu[k] + G1a[b].y*Az[0][1][k]*Nu[k] + G1a[b].z*Az[0][2][k]*Nu[k];
kab(2,1) += G1a[b].x*Az[1][0][k]*Nu[k] + G1a[b].y*Az[1][1][k]*Nu[k] + G1a[b].z*Az[1][2][k]*Nu[k];
kab(2,2) += G1a[b].x*Az[2][0][k]*Nu[k] + G1a[b].y*Az[2][1][k]*Nu[k] + G1a[b].z*Az[2][2][k]*Nu[k];
}
// multiply by weights
kab *= J0*gw[n]*0.5;
ke.add(3*(nelna + a), 3*b, kab);
}
// The b-b term
for (int a=0; a<nelnb; ++a)
for (int b=0; b<nelnb; ++b)
{
double Ax[3][3][3] = {0};
double Ay[3][3][3] = {0};
double Az[3][3][3] = {0};
for (int i=0; i<3; ++i)
for (int j=0; j<3; ++j)
for (int k=0; k<3; ++k)
{
for (int q=0; q<3; ++q)
for (int r=0; r<3; ++r)
{
Ax[i][j][k] += Jb(i, j, k, 0, q, r)*G2b[a](q ,r);
Ay[i][j][k] += Jb(i, j, k, 1, q, r)*G2b[a](q ,r);
Az[i][j][k] += Jb(i, j, k, 2, q, r)*G2b[a](q ,r);
}
Ax[i][j][k] += Hb(i, j, k, 0, 0)*G1b[a].x + Hb(i, j, k, 0, 1)*G1b[a].y + Hb(i, j, k, 0, 2)*G1b[a].z;
Ay[i][j][k] += Hb(i, j, k, 1, 0)*G1b[a].x + Hb(i, j, k, 1, 1)*G1b[a].y + Hb(i, j, k, 1, 2)*G1b[a].z;
Az[i][j][k] += Hb(i, j, k, 2, 0)*G1b[a].x + Hb(i, j, k, 2, 1)*G1b[a].y + Hb(i, j, k, 2, 2)*G1b[a].z;
}
mat3d kab; kab.zero();
for (int k=0; k<3; ++k)
{
kab(0,0) += G1b[b].x*Ax[0][0][k]*Nu[k] + G1b[b].y*Ax[0][1][k]*Nu[k] + G1b[b].z*Ax[0][2][k]*Nu[k];
kab(0,1) += G1b[b].x*Ax[1][0][k]*Nu[k] + G1b[b].y*Ax[1][1][k]*Nu[k] + G1b[b].z*Ax[1][2][k]*Nu[k];
kab(0,2) += G1b[b].x*Ax[2][0][k]*Nu[k] + G1b[b].y*Ax[2][1][k]*Nu[k] + G1b[b].z*Ax[2][2][k]*Nu[k];
kab(1,0) += G1b[b].x*Ay[0][0][k]*Nu[k] + G1b[b].y*Ay[0][1][k]*Nu[k] + G1b[b].z*Ay[0][2][k]*Nu[k];
kab(1,1) += G1b[b].x*Ay[1][0][k]*Nu[k] + G1b[b].y*Ay[1][1][k]*Nu[k] + G1b[b].z*Ay[1][2][k]*Nu[k];
kab(1,2) += G1b[b].x*Ay[2][0][k]*Nu[k] + G1b[b].y*Ay[2][1][k]*Nu[k] + G1b[b].z*Ay[2][2][k]*Nu[k];
kab(2,0) += G1b[b].x*Az[0][0][k]*Nu[k] + G1b[b].y*Az[0][1][k]*Nu[k] + G1b[b].z*Az[0][2][k]*Nu[k];
kab(2,1) += G1b[b].x*Az[1][0][k]*Nu[k] + G1b[b].y*Az[1][1][k]*Nu[k] + G1b[b].z*Az[1][2][k]*Nu[k];
kab(2,2) += G1b[b].x*Az[2][0][k]*Nu[k] + G1b[b].y*Az[2][1][k]*Nu[k] + G1b[b].z*Az[2][2][k]*Nu[k];
}
// multiply by weights
kab *= J0*gw[n]*0.5;
ke.sub(3*(nelna + a), 3*(nelna + b), kab);
}
}
}
//-----------------------------------------------------------------------------
void FEElasticSolidDomain2O::ElementStiffnessMatrixDG3(FESurfaceElement& face, FEInternalSurface2O::Data* pdata, matrix& ke)
{
// get the beta parameter
FEElasticMaterial2O* pmat = dynamic_cast<FEElasticMaterial2O*>(GetMaterial());
double beta = pmat->m_beta;
double h = m_surf.GetElementSize();
// get the surface
FESurface& surf = *m_surf.GetSurface();
// get the solid elements of this interface element
FESolidElement& ela = static_cast<FESolidElement&>(*face.m_elem[1].pe);
FESolidElement& elb = static_cast<FESolidElement&>(*face.m_elem[0].pe);
// get the number nodes of each element
int nelna = ela.Nodes();
int nelnb = elb.Nodes();
// initialize the element stiffness matrix
int ndof = 3*(nelna + nelnb);
// shape function derivatives
vec3d Ga[FEElement::MAX_NODES];
vec3d Gb[FEElement::MAX_NODES];
// loop over all integration points
int nint = face.GaussPoints();
double* gw = face.GaussWeights();
for (int n=0; n<nint; ++n)
{
// get the integration point data
FEInternalSurface2O::Data& data = pdata[n];
// Jacobian and normal at this integration point
vec3d nu;
double J0 = surf.jac0(face, n, nu);
double Nu[3] = {nu.x, nu.y, nu.z};
// shape function gradients at this integration point
ShapeGradient0(ela, data.ksi[1].x, data.ksi[1].y, data.ksi[1].z, Ga);
ShapeGradient0(elb, data.ksi[0].x, data.ksi[0].y, data.ksi[0].z, Gb);
// average stiffness
tens6d& Javg = data.J0avg;
// The ++ term
for (int a=0; a<nelna; ++a)
for (int b=0; b<nelna; b++)
{
mat3d kab;
for (int i=0; i<3; ++i)
for (int p=0; p<3; ++p)
{
double kip = 0.0;
for (int k=0; k<3; ++k)
for (int r=0; r<3; ++r)
{
double NN = Nu[k]*Nu[r];
kip += Ga[a].x*Ga[b].x*Javg(i,0,k,p,0,r)*NN + Ga[a].x*Ga[b].y*Javg(i,0,k,p,1,r)*NN + Ga[a].x*Ga[b].z*Javg(i,0,k,p,2,r)*NN;
kip += Ga[a].y*Ga[b].x*Javg(i,1,k,p,0,r)*NN + Ga[a].y*Ga[b].y*Javg(i,1,k,p,1,r)*NN + Ga[a].y*Ga[b].z*Javg(i,1,k,p,2,r)*NN;
kip += Ga[a].z*Ga[b].x*Javg(i,2,k,p,0,r)*NN + Ga[a].z*Ga[b].y*Javg(i,2,k,p,1,r)*NN + Ga[a].z*Ga[b].z*Javg(i,2,k,p,2,r)*NN;
}
kab[i][p] = kip*(beta/h)*J0*gw[n];
}
ke.add(3*a, 3*b, kab);
}
// The +- term
for (int a=0; a<nelna; ++a)
for (int b=0; b<nelnb; b++)
{
mat3d kab;
for (int i=0; i<3; ++i)
for (int p=0; p<3; ++p)
{
double kip = 0.0;
for (int k=0; k<3; ++k)
for (int r=0; r<3; ++r)
{
double NN = Nu[k]*Nu[r];
kip += Ga[a].x*Gb[b].x*Javg(i,0,k,p,0,r)*NN + Ga[a].x*Gb[b].y*Javg(i,0,k,p,1,r)*NN + Ga[a].x*Gb[b].z*Javg(i,0,k,p,2,r)*NN;
kip += Ga[a].y*Gb[b].x*Javg(i,1,k,p,0,r)*NN + Ga[a].y*Gb[b].y*Javg(i,1,k,p,1,r)*NN + Ga[a].y*Gb[b].z*Javg(i,1,k,p,2,r)*NN;
kip += Ga[a].z*Gb[b].x*Javg(i,2,k,p,0,r)*NN + Ga[a].z*Gb[b].y*Javg(i,2,k,p,1,r)*NN + Ga[a].z*Gb[b].z*Javg(i,2,k,p,2,r)*NN;
}
kab[i][p] = kip*(beta/h)*J0*gw[n];
}
ke.sub(3*a, 3*(nelna + b), kab);
}
// The -+ term
for (int a=0; a<nelnb; ++a)
for (int b=0; b<nelna; b++)
{
mat3d kab;
for (int i=0; i<3; ++i)
for (int p=0; p<3; ++p)
{
double kip = 0.0;
for (int k=0; k<3; ++k)
for (int r=0; r<3; ++r)
{
double NN = Nu[k]*Nu[r];
kip += Gb[a].x*Ga[b].x*Javg(i,0,k,p,0,r)*NN + Gb[a].x*Ga[b].y*Javg(i,0,k,p,1,r)*NN + Gb[a].x*Ga[b].z*Javg(i,0,k,p,2,r)*NN;
kip += Gb[a].y*Ga[b].x*Javg(i,1,k,p,0,r)*NN + Gb[a].y*Ga[b].y*Javg(i,1,k,p,1,r)*NN + Gb[a].y*Ga[b].z*Javg(i,1,k,p,2,r)*NN;
kip += Gb[a].z*Ga[b].x*Javg(i,2,k,p,0,r)*NN + Gb[a].z*Ga[b].y*Javg(i,2,k,p,1,r)*NN + Gb[a].z*Ga[b].z*Javg(i,2,k,p,2,r)*NN;
}
kab[i][p] = kip*(beta/h)*J0*gw[n];
}
ke.sub(3*(a+nelna), 3*b, kab);
}
// The -- term
for (int a=0; a<nelnb; ++a)
for (int b=0; b<nelnb; b++)
{
mat3d kab;
for (int i=0; i<3; ++i)
for (int p=0; p<3; ++p)
{
double kip = 0.0;
for (int k=0; k<3; ++k)
for (int r=0; r<3; ++r)
{
double NN = Nu[k]*Nu[r];
kip += Gb[a].x*Gb[b].x*Javg(i,0,k,p,0,r)*NN + Gb[a].x*Gb[b].y*Javg(i,0,k,p,1,r)*NN + Gb[a].x*Gb[b].z*Javg(i,0,k,p,2,r)*NN;
kip += Gb[a].y*Gb[b].x*Javg(i,1,k,p,0,r)*NN + Gb[a].y*Gb[b].y*Javg(i,1,k,p,1,r)*NN + Gb[a].y*Gb[b].z*Javg(i,1,k,p,2,r)*NN;
kip += Gb[a].z*Gb[b].x*Javg(i,2,k,p,0,r)*NN + Gb[a].z*Gb[b].y*Javg(i,2,k,p,1,r)*NN + Gb[a].z*Gb[b].z*Javg(i,2,k,p,2,r)*NN;
}
kab[i][p] = kip*(beta/h)*J0*gw[n];
}
ke.add(3*(nelna + a), 3*(nelna + b), kab);
}
}
}
//-----------------------------------------------------------------------------
//! Calculates element material stiffness element matrix
void FEElasticSolidDomain2O::ElementStiffness(const FETimeInfo& tp, int iel, matrix& ke)
{
FEElasticMaterial2O* pmat = dynamic_cast<FEElasticMaterial2O*>(m_pMat);
FESolidElement& el = Element(iel);
// Get the current element's data
const int neln = el.Nodes();
const int ndof = 3*neln;
// global derivatives of shape functions
// Gx = dN/dx
vec3d G[FEElement::MAX_NODES];
// H = d2N/dXdX
mat3d G2[FEElement::MAX_NODES];
// get the initial nodal coordinates
vec3d X[FEElement::MAX_NODES];
FEMesh& mesh = *GetMesh();
mesh.GetInitialNodalCoordinates(el, X);
ke.zero();
// calculate element stiffness matrix
const int nint = el.GaussPoints();
const double *gw = el.GaussWeights();
for (int ni=0; ni<nint; ++ni)
{
// get the material point data
// NOTE: deformation gradient and determinant have already been evaluated in the stress routine
FEMaterialPoint& mp = *el.GetMaterialPoint(ni);
FEElasticMaterialPoint2O& pt2O = *(mp.ExtractData<FEElasticMaterialPoint2O>());
// get the material tangents
tens4d C;
tens5d L;
tens5d H;
tens6d J;
pmat->Tangent(mp, C, L, H, J);
// get the first derivative of shape functions
double J0 = ShapeGradient0(el, ni, G);
// second derivative of shape functions
shape_gradient2(el, X, ni, G2);
for (int a=0; a<neln; ++a)
{
double Ga[3] = {G[a].x, G[a].y, G[a].z};
mat3d& Ha = G2[a];
for (int b=0; b<neln; ++b)
{
double Gb[3] = {G[b].x, G[b].y, G[b].z};
mat3d& Hb = G2[b];
// gradN*C*gradN
mat3d Kab; Kab.zero();
for (int j=0; j<3; ++j)
for (int l=0; l<3; ++l)
{
Kab[0][0] += (Ga[j]*C(0,j,0,l)*Gb[l]);
Kab[0][1] += (Ga[j]*C(0,j,1,l)*Gb[l]);
Kab[0][2] += (Ga[j]*C(0,j,2,l)*Gb[l]);
Kab[1][0] += (Ga[j]*C(1,j,0,l)*Gb[l]);
Kab[1][1] += (Ga[j]*C(1,j,1,l)*Gb[l]);
Kab[1][2] += (Ga[j]*C(1,j,2,l)*Gb[l]);
Kab[2][0] += (Ga[j]*C(2,j,0,l)*Gb[l]);
Kab[2][1] += (Ga[j]*C(2,j,1,l)*Gb[l]);
Kab[2][2] += (Ga[j]*C(2,j,2,l)*Gb[l]);
}
// gradN*L*grad2N
for (int j=0; j<3; ++j)
for (int l=0; l<3; ++l)
for (int m=0; m<3; ++m)
{
Kab[0][0] += (Ga[j]*L(0, j, 0, l, m)*Ha(l, m));
Kab[0][1] += (Ga[j]*L(0, j, 1, l, m)*Ha(l, m));
Kab[0][2] += (Ga[j]*L(0, j, 2, l, m)*Ha(l, m));
Kab[1][0] += (Ga[j]*L(1, j, 0, l, m)*Ha(l, m));
Kab[1][1] += (Ga[j]*L(1, j, 1, l, m)*Ha(l, m));
Kab[1][2] += (Ga[j]*L(1, j, 2, l, m)*Ha(l, m));
Kab[2][0] += (Ga[j]*L(2, j, 0, l, m)*Ha(l, m));
Kab[2][1] += (Ga[j]*L(2, j, 1, l, m)*Ha(l, m));
Kab[2][2] += (Ga[j]*L(2, j, 2, l, m)*Ha(l, m));
}
// grad2N*H*gradN
for (int j=0; j<3; ++j)
for (int k=0; k<3; ++k)
for (int m=0; m<3; ++m)
{
Kab[0][0] += (Ha(j,k)*H(0, j, k, 0, m)*Ga[m]);
Kab[0][1] += (Ha(j,k)*H(0, j, k, 1, m)*Ga[m]);
Kab[0][2] += (Ha(j,k)*H(0, j, k, 2, m)*Ga[m]);
Kab[1][0] += (Ha(j,k)*H(1, j, k, 0, m)*Ga[m]);
Kab[1][1] += (Ha(j,k)*H(1, j, k, 1, m)*Ga[m]);
Kab[1][2] += (Ha(j,k)*H(1, j, k, 2, m)*Ga[m]);
Kab[2][0] += (Ha(j,k)*H(2, j, k, 0, m)*Ga[m]);
Kab[2][1] += (Ha(j,k)*H(2, j, k, 1, m)*Ga[m]);
Kab[2][2] += (Ha(j,k)*H(2, j, k, 2, m)*Ga[m]);
}
// grad2N*J*grad2N
for (int j=0; j<3; ++j)
for (int k=0; k<3; ++k)
for (int m=0; m<3; ++m)
for (int n=0; n<3; ++n)
{
Kab[0][0] += (Ha(j,k)*J(0, j, k, 0, m, n)*Hb(m,n));
Kab[0][1] += (Ha(j,k)*J(0, j, k, 1, m, n)*Hb(m,n));
Kab[0][2] += (Ha(j,k)*J(0, j, k, 2, m, n)*Hb(m,n));
Kab[1][0] += (Ha(j,k)*J(1, j, k, 0, m, n)*Hb(m,n));
Kab[1][1] += (Ha(j,k)*J(1, j, k, 1, m, n)*Hb(m,n));
Kab[1][2] += (Ha(j,k)*J(1, j, k, 2, m, n)*Hb(m,n));
Kab[2][0] += (Ha(j,k)*J(2, j, k, 0, m, n)*Hb(m,n));
Kab[2][1] += (Ha(j,k)*J(2, j, k, 1, m, n)*Hb(m,n));
Kab[2][2] += (Ha(j,k)*J(2, j, k, 2, m, n)*Hb(m,n));
}
// multiply by jacobian and integration weight
Kab *= J0*gw[ni];
// add it to the element matrix
ke.add(3*a, 3*b, Kab);
}
}
}
}
//-----------------------------------------------------------------------------
//! Calculate the gradient of deformation gradient of element el at integration point n.
//! The gradient of the deformation gradient is returned in G.
void FEElasticSolidDomain2O::defhess(FESolidElement &el, int n, tens3drs &G)
{
int neln = el.Nodes();
// get the nodal positions
vec3d X[FEElement::MAX_NODES];
vec3d x[FEElement::MAX_NODES];
FEMesh& mesh = *GetMesh();
for (int i=0; i<neln; ++i)
{
X[i] = mesh.Node(el.m_node[i]).m_r0;
x[i] = mesh.Node(el.m_node[i]).m_rt;
}
mat3d H[FEElement::MAX_NODES];
shape_gradient2(el, X, n, H);
// loop over nodes
G.zero();
for (int a=0; a<neln; ++a)
{
const mat3d& Ha = H[a];
// calculate gradient of deformation gradient
// Note that k >= j. Since tensdrs has symmetries this
// prevents overwriting of symmetric components
for (int j=0; j<3; ++j)
for (int k=j; k<3; ++k)
{
G(0,j,k) += Ha(j,k)*x[a].x;
G(1,j,k) += Ha(j,k)*x[a].y;
G(2,j,k) += Ha(j,k)*x[a].z;
}
}
}
//-----------------------------------------------------------------------------
//! Calculate the gradient of deformation gradient of element el at integration point n.
//! The gradient of the deformation gradient is returned in G.
void FEElasticSolidDomain2O::defhess(FESolidElement &el, double r, double s, double t, tens3drs &G)
{
int neln = el.Nodes();
// get the nodal positions
vec3d X[FEElement::MAX_NODES];
vec3d x[FEElement::MAX_NODES];
FEMesh& mesh = *GetMesh();
for (int i=0; i<neln; ++i)
{
X[i] = mesh.Node(el.m_node[i]).m_r0;
x[i] = mesh.Node(el.m_node[i]).m_rt;
}
mat3d H[FEElement::MAX_NODES];
shape_gradient2(el, X, r, s, t, H);
// loop over nodes
G.zero();
for (int a=0; a<neln; ++a)
{
mat3d& Ha = H[a];
// calculate gradient of deformation gradient
// Note that k >= j. Since tensdrs has symmetries this
// prevents overwriting of symmetric components
for (int j=0; j<3; ++j)
for (int k=j; k<3; ++k)
{
G(0,j,k) += Ha[j][k]*x[a].x;
G(1,j,k) += Ha[j][k]*x[a].y;
G(2,j,k) += Ha[j][k]*x[a].z;
}
}
}
//-----------------------------------------------------------------------------
//! Calculates the second derivative of shape function N[node] with respect
//! to the material coordinates.
void FEElasticSolidDomain2O::shape_gradient2(const FESolidElement& el, vec3d* X, int n, mat3d* H)
{
int neln = el.Nodes();
double* gw = el.GaussWeights();
// inverse of Jacobian matrix
double Ji[3][3];
// we'll evaluate this term in the material frame
// so we need the Jacobian with respect to the reference configuration
invjac0(el, Ji, n);
// shape function derivatives
double* Gr = el.Gr(n);
double* Gs = el.Gs(n);
double* Gt = el.Gt(n);
double *Grrn = el.Grr(n); double *Grsn = el.Grs(n); double *Grtn = el.Grt(n);
double *Gsrn = el.Gsr(n); double *Gssn = el.Gss(n); double *Gstn = el.Gst(n);
double *Gtrn = el.Gtr(n); double *Gtsn = el.Gts(n); double *Gttn = el.Gtt(n);
// calculate K = dJ/dr
double K[3][3][3] = {0};
for (int a=0; a<neln; ++a)
{
// second derivatives of shape functions
double G2[3][3];
G2[0][0] = Grrn[a]; G2[0][1] = Grsn[a]; G2[0][2] = Grtn[a];
G2[1][0] = Gsrn[a]; G2[1][1] = Gssn[a]; G2[1][2] = Gstn[a];
G2[2][0] = Gtrn[a]; G2[2][1] = Gtsn[a]; G2[2][2] = Gttn[a];
for (int j=0; j<3; ++j)
for (int k=0; k<3; ++k)
{
K[0][j][k] += G2[j][k]*X[a].x;
K[1][j][k] += G2[j][k]*X[a].y;
K[2][j][k] += G2[j][k]*X[a].z;
}
}
// calculate A = -J^-1*dJ/drJ^-1
double A[3][3][3] = {0};
for (int i=0; i<3; ++i)
for (int j=0; j<3; ++j)
{
for (int p=0; p<3; ++p)
for (int q=0; q<3; ++q)
{
A[i][j][0] -= Ji[j][p]*K[p][q][0]*Ji[q][i];
A[i][j][1] -= Ji[j][p]*K[p][q][1]*Ji[q][i];
A[i][j][2] -= Ji[j][p]*K[p][q][2]*Ji[q][i];
}
}
// first derivative of shape functions
for (int a=0; a<neln; ++a)
{
double G1[3];
G1[0] = Gr[a];
G1[1] = Gs[a];
G1[2] = Gt[a];
// second derivatives of shape functions
double G2[3][3];
G2[0][0] = Grrn[a]; G2[0][1] = Grsn[a]; G2[0][2] = Grtn[a];
G2[1][0] = Gsrn[a]; G2[1][1] = Gssn[a]; G2[1][2] = Gstn[a];
G2[2][0] = Gtrn[a]; G2[2][1] = Gtsn[a]; G2[2][2] = Gttn[a];
// calculate dB/dr
double D[3][3] = {0};
for (int i=0; i<3; ++i)
for (int k=0; k<3; ++k)
{
for (int j=0; j<3; ++j) D[i][k] += A[i][j][k]*G1[j] + Ji[j][i]*G2[j][k];
}
// calculate global gradient of shape functions
mat3d& Ha = H[a];
for (int i=0; i<3; ++i)
for (int j=0; j<3; ++j)
{
Ha[i][j] = D[i][0]*Ji[0][j] + D[i][1]*Ji[1][j] + D[i][2]*Ji[2][j];
}
}
}
//-----------------------------------------------------------------------------
//! Calculates the second derivative of shape function N[node] with respect
//! to the material coordinates.
void FEElasticSolidDomain2O::shape_gradient2(const FESolidElement& el, vec3d* X, double r, double s, double t, mat3d* H)
{
int neln = el.Nodes();
// we need the Jacobian with respect to the reference configuration
double Ji[3][3];
invjac0(el, Ji, r, s, t);
// shape function derivatives
const int M = FEElement::MAX_NODES;
double Gr[M], Gs[M], Gt[M];
el.shape_deriv(Gr, Gs, Gt, r, s, t);
double Grr[M], Gss[M], Gtt[M], Grs[M], Gst[M], Grt[M];
el.shape_deriv2(Grr, Gss, Gtt, Grs, Gst, Grt, r, s, t);
// calculate K = dJ/dr
double K[3][3][3] = {0};
for (int a=0; a<neln; ++a)
{
// second derivatives of shape functions
double G2[3][3];
G2[0][0] = Grr[a]; G2[0][1] = Grs[a]; G2[0][2] = Grt[a];
G2[1][0] = Grs[a]; G2[1][1] = Gss[a]; G2[1][2] = Gst[a];
G2[2][0] = Grt[a]; G2[2][1] = Gst[a]; G2[2][2] = Gtt[a];
for (int j=0; j<3; ++j)
for (int k=0; k<3; ++k)
{
K[0][j][k] += G2[j][k]*X[a].x;
K[1][j][k] += G2[j][k]*X[a].y;
K[2][j][k] += G2[j][k]*X[a].z;
}
}
// calculate A = -J^-1*dJ/drJ^-1
double A[3][3][3] = {0};
for (int i=0; i<3; ++i)
for (int j=0; j<3; ++j)
{
for (int p=0; p<3; ++p)
for (int q=0; q<3; ++q)
{
A[i][j][0] -= Ji[j][p]*K[p][q][0]*Ji[q][i];
A[i][j][1] -= Ji[j][p]*K[p][q][1]*Ji[q][i];
A[i][j][2] -= Ji[j][p]*K[p][q][2]*Ji[q][i];
}
}
// first derivative of shape functions
double G1[3];
double G2[3][3];
for (int a=0; a<neln; ++a)
{
G1[0] = Gr[a];
G1[1] = Gs[a];
G1[2] = Gt[a];
// second derivatives of shape functions
G2[0][0] = Grr[a]; G2[0][1] = Grs[a]; G2[0][2] = Grt[a];
G2[1][0] = Grs[a]; G2[1][1] = Gss[a]; G2[1][2] = Gst[a];
G2[2][0] = Grt[a]; G2[2][1] = Gst[a]; G2[2][2] = Gtt[a];
// calculate dB/dr
double D[3][3] = {0};
for (int i=0; i<3; ++i)
for (int k=0; k<3; ++k)
{
for (int j=0; j<3; ++j) D[i][k] += A[i][j][k]*G1[j] + Ji[j][i]*G2[j][k];
}
// calculate global gradient of shape functions
mat3d& Ha = H[a];
for (int i=0; i<3; ++i)
for (int j=0; j<3; ++j)
{
Ha[i][j] = D[i][0]*Ji[0][j] + D[i][1]*Ji[1][j] + D[i][2]*Ji[2][j];
}
}
}
| C++ |
3D | febiosoftware/FEBio | FEBioRVE/stdafx.h | .h | 13 | 2 | #pragma once
| Unknown |
3D | febiosoftware/FEBio | FEBioRVE/FEElasticMultiscaleDomain1O.h | .h | 1,815 | 43 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#pragma once
#include "FEBioMech/FEElasticSolidDomain.h"
#include "FECore/tens3d.h"
//-----------------------------------------------------------------------------
//! This class implements a domain used in an elastic remodeling problem.
//! It differs from the FEElasticSolidDomain in that it adds a stiffness matrix
//! due to the deformation dependent density.
class FEElasticMultiscaleDomain1O : public FEElasticSolidDomain
{
public:
//! constructor
FEElasticMultiscaleDomain1O(FEModel* pfem);
//! initialize class
bool Init();
};
| Unknown |
3D | febiosoftware/FEBio | FEBioRVE/FERVEProbe.h | .h | 2,609 | 80 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#pragma once
#include <FECore/FECallBack.h>
#include "febiorve_api.h"
//-----------------------------------------------------------------------------
class FEBioPlotFile;
class FEMaterialPoint;
//-----------------------------------------------------------------------------
// Base class for RVE probes
class FEBIORVE_API FERVEProbe : public FECallBack
{
FECORE_BASE_CLASS(FERVEProbe);
public:
// The first FEModel (fem) is the macro-problem, i.e. the model that will generate the callbacks
// The second FEModel (rve) is the micro-problem that needs to be tracked.
FERVEProbe(FEModel* fem);
bool Init() override;
bool Execute(FEModel& fem, int nwhen) override;
void Save();
void SetDebugFlag(bool b) { m_bdebug = b; }
bool GetDebugFlag() const { return m_bdebug; }
void SetRVEModel(FEModel* rve);
protected:
std::string m_file; //!< file name
bool m_bdebug; //!< debug flag
private:
FEBioPlotFile* m_xplt; //!< the actual plot file
FEModel* m_rve; //!< the RVE model
};
//-----------------------------------------------------------------------------
// Probe used by FEMicroMaterial
class FEMicroProbe : public FERVEProbe
{
public:
FEMicroProbe(FEModel* fem);
bool Init() override;
private:
int m_neid; //!< element Id
int m_ngp; //!< Gauss-point (one-based!)
DECLARE_FECORE_CLASS();
};
| Unknown |
3D | febiosoftware/FEBio | FEBioRVE/FERVEModel.h | .h | 3,385 | 111 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#pragma once
#include "FECore/FEModel.h"
#include <FECore/tens4d.h>
#include "febiorve_api.h"
//-----------------------------------------------------------------------------
// Class describing the RVE model.
// This is used by the homogenization code.
class FEBIORVE_API FERVEModel : public FEModel
{
public:
enum RVE_TYPE
{
DISPLACEMENT, // prescribed displacement
PERIODIC_LC, // periodic, linear constraints
PERIODIC_AL // periodic, augmented Lagrangian (NOTE: obsolete, should probably delete)
};
public:
FERVEModel();
~FERVEModel();
//! initialization
bool Init() override;
//! one time initialization (only called on the master RVE)
bool InitRVE(int rveType, const char* szbc);
//! Return the initial volume (calculated in Init)
double InitialVolume() const { return m_V0; }
//! return current volume (calculated each time)
double CurrentVolume();
// scale the geometry
void ScaleGeometry(double scale);
//! see if node is boundary node
bool IsBoundaryNode(int i) const { return (m_BN[i]==1); }
//! Update the RVE (before it is solved)
void Update(const mat3d& F);
// copy from the master RVE
void CopyFrom(FERVEModel& rve);
// set the parent FEModel
void SetParentModel(FEModel* fem);
//! Calculate the stress average
mat3ds StressAverage(mat3d& F, FEMaterialPoint& mp);
mat3ds StressAverage(FEMaterialPoint& mp);
//! Calculate the stiffness average
tens4ds StiffnessAverage(FEMaterialPoint &mp);
protected:
//! Calculate the initial volume
void EvalInitialVolume();
//! find the list of boundary nodes
void FindBoundaryNodes(vector<int>& BN);
//! Center the RVE
void CenterRVE();
bool PrepDisplacementBC(FENodeSet* set);
bool PrepPeriodicBC(const char* szbc);
bool PrepPeriodicLC();
private:
// Hide the solve function so we can't call it.
// (We use the RCI solution method)
bool Solve() override;
protected:
FEModel* m_parentfem; //!< parent FEModel
double m_V0; //!< initial volume
int m_bctype; //!< RVE type
FEBoundingBox m_bb; //!< bounding box of mesh
vector<int> m_BN; //!< boundary node flags
};
| Unknown |
3D | febiosoftware/FEBio | FEBioRVE/FEElasticMaterial2O.cpp | .cpp | 3,722 | 107 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#include "stdafx.h"
#include "FEElasticMaterial2O.h"
#include <FECore/DumpStream.h>
//-----------------------------------------------------------------------------
FEElasticMaterialPoint2O::FEElasticMaterialPoint2O(FEMaterialPointData* pt) : FEMaterialPointData(pt)
{
m_PK1.zero();
m_G.zero();
m_Q.zero();
}
//-----------------------------------------------------------------------------
FEMaterialPointData* FEElasticMaterialPoint2O::Copy()
{
FEElasticMaterialPoint2O* pt = new FEElasticMaterialPoint2O(0);
pt->m_PK1 = m_PK1;
pt->m_G = m_G;
pt->m_Q = m_Q;
if (m_pNext) pt->SetNext(m_pNext->Copy());
return pt;
}
//-----------------------------------------------------------------------------
void FEElasticMaterialPoint2O::Serialize(DumpStream& ar)
{
FEMaterialPointData::Serialize(ar);
ar & m_PK1 & m_G & m_Q;
}
//-----------------------------------------------------------------------------
// define the material parameters
BEGIN_FECORE_CLASS(FEElasticMaterial2O, FEElasticMaterial)
ADD_PARAMETER(m_beta , "beta" );
ADD_PARAMETER(m_bKDG1 , "KDG1" );
ADD_PARAMETER(m_bKDG2 , "KDG2" );
ADD_PARAMETER(m_bKDG3 , "KDG3" );
ADD_PARAMETER(m_buseJ0 , "useJ0" );
END_FECORE_CLASS();
//-----------------------------------------------------------------------------
FEElasticMaterial2O::FEElasticMaterial2O(FEModel* pfem) : FEElasticMaterial(pfem)
{
m_beta = 10.0;
m_bKDG1 = true;
m_bKDG2 = true;
m_bKDG3 = true;
m_buseJ0 = true;
}
//-----------------------------------------------------------------------------
FEMaterialPointData* FEElasticMaterial2O::CreateMaterialPointData()
{
return new FEElasticMaterialPoint2O(new FEElasticMaterialPoint);
}
//-----------------------------------------------------------------------------
// The stiffness is evaluated at the same time the stress is evaluated so we
// can just return it here. Note that this assumes that the stress function
// is always called prior to the tangent function.
// Note that this function is not used in the second-order implemenetation
tens4ds FEElasticMaterial2O::Tangent(FEMaterialPoint &mp)
{
assert(false);
tens4ds c; c.zero();
return c;
}
//-----------------------------------------------------------------------------
// Note that this function is not used in the second-order implemenetation
mat3ds FEElasticMaterial2O::Stress(FEMaterialPoint &mp)
{
assert(false);
mat3ds sa; sa.zero();
return sa;
}
| C++ |
3D | febiosoftware/FEBio | FEBioRVE/FEElasticMultiscaleDomain2O.h | .h | 1,971 | 52 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#pragma once
#include "FEElasticSolidDomain2O.h"
#include <FECore/tens3d.h>
#include <FECore/tens5d.h>
#include <FECore/tens6d.h>
#include <FECore/FESurface.h>
//-----------------------------------------------------------------------------
//! This class implements a domain used in an elastic remodeling problem.
//! It differs from the FEElasticSolidDomain in that it adds a stiffness matrix
//! due to the deformation dependent density.
class FEElasticMultiscaleDomain2O : public FEElasticSolidDomain2O
{
public:
//! constructor
FEElasticMultiscaleDomain2O(FEModel* pfem);
//! initialize class
bool Init() override;
//! Update
void Update(const FETimeInfo& timeInfo) override;
};
| Unknown |
3D | febiosoftware/FEBio | FEBioRVE/FEPeriodicBoundary2O.cpp | .cpp | 16,710 | 688 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#include "stdafx.h"
#include "FEPeriodicBoundary2O.h"
#include "FECore/FEModel.h"
#include "FECore/FENormalProjection.h"
#include "FECore/FEGlobalMatrix.h"
#include <FECore/FELinearSystem.h>
#include "FECore/log.h"
//-----------------------------------------------------------------------------
// Define sliding interface parameters
BEGIN_FECORE_CLASS(FEPeriodicBoundary2O, FEContactInterface)
ADD_PARAMETER(m_laugon , "laugon" )->setLongName("Enforcement method")->setEnums("PENALTY\0AUGLAG\0");
ADD_PARAMETER(m_atol , "tolerance");
ADD_PARAMETER(m_eps , "penalty" );
ADD_PARAMETER(m_btwo_pass, "two_pass" );
ADD_PARAMETER(m_off , "offset" );
ADD_PARAMETER(m_naugmin , "minaug" );
END_FECORE_CLASS();
//-----------------------------------------------------------------------------
// FEPeriodicBoundary
//-----------------------------------------------------------------------------
FEPeriodicBoundary2O::FEPeriodicBoundary2O(FEModel* pfem) : FEContactInterface(pfem), m_ss(pfem), m_ms(pfem)
{
static int count = 1;
SetID(count++);
m_stol = 0.01;
m_srad = 1.0;
m_atol = 0;
m_eps = 0;
m_btwo_pass = false;
m_off = vec3d(0,0,0);
m_naugmin = 0;
// set parents
m_ss.SetContactInterface(this);
m_ms.SetContactInterface(this);
m_ss.SetSibling(&m_ms);
m_ms.SetSibling(&m_ss);
m_Fmacro.zero();
m_Gmacro.zero();
}
//-----------------------------------------------------------------------------
bool FEPeriodicBoundary2O::Init()
{
// create the surfaces
if (m_ss.Init() == false) return false;
if (m_ms.Init() == false) return false;
return true;
}
//-----------------------------------------------------------------------------
void FEPeriodicBoundary2O::Activate()
{
// don't forget to call the base class
FEContactInterface::Activate();
// project primary surface onto secondary surface
ProjectSurface(m_ss, m_ms, false);
ProjectSurface(m_ms, m_ss, false);
}
//-----------------------------------------------------------------------------
void FEPeriodicBoundary2O::CopyFrom(FESurfacePairConstraint* pci)
{
// cast to a periodic boundary
FEPeriodicBoundary2O& pb = dynamic_cast<FEPeriodicBoundary2O&>(*pci);
// copy parameters
GetParameterList() = pb.GetParameterList();
// copy data
m_ss.CopyFrom(pb.m_ss);
m_ms.CopyFrom(pb.m_ms);
}
//-----------------------------------------------------------------------------
//! build the matrix profile for use in the stiffness matrix
// TODO: what if two_pass ??
void FEPeriodicBoundary2O::BuildMatrixProfile(FEGlobalMatrix& K)
{
FEModel& fem = *GetFEModel();
FEMesh& mesh = fem.GetMesh();
// get the DOFS
const int dof_X = fem.GetDOFIndex("x");
const int dof_Y = fem.GetDOFIndex("y");
const int dof_Z = fem.GetDOFIndex("z");
const int dof_RU = fem.GetDOFIndex("Ru");
const int dof_RV = fem.GetDOFIndex("Rv");
const int dof_RW = fem.GetDOFIndex("Rw");
vector<int> lm(6*5);
for (int j=0; j<m_ss.Nodes(); ++j)
{
FESurfaceElement& me = *m_ss.m_data[j].m_pme;
int* en = &me.m_node[0];
int n = me.Nodes();
if (n == 3)
{
lm[6*(3+1) ] = -1;
lm[6*(3+1)+1] = -1;
lm[6*(3+1)+2] = -1;
lm[6*(3+1)+3] = -1;
lm[6*(3+1)+4] = -1;
lm[6*(3+1)+5] = -1;
}
lm[0] = m_ss.Node(j).m_ID[dof_X];
lm[1] = m_ss.Node(j).m_ID[dof_Y];
lm[2] = m_ss.Node(j).m_ID[dof_Z];
lm[3] = m_ss.Node(j).m_ID[dof_RU];
lm[4] = m_ss.Node(j).m_ID[dof_RV];
lm[5] = m_ss.Node(j).m_ID[dof_RW];
for (int k=0; k<n; ++k)
{
vector<int>& id = mesh.Node(en[k]).m_ID;
lm[6*(k+1) ] = id[dof_X];
lm[6*(k+1)+1] = id[dof_Y];
lm[6*(k+1)+2] = id[dof_Z];
lm[6*(k+1)+3] = id[dof_RU];
lm[6*(k+1)+4] = id[dof_RV];
lm[6*(k+1)+5] = id[dof_RW];
}
K.build_add(lm);
}
}
//-----------------------------------------------------------------------------
//! project surface
void FEPeriodicBoundary2O::ProjectSurface(FEPeriodicSurface& ss, FEPeriodicSurface& ms, bool bmove)
{
int i;
double rs[2];
// get the primary's center of mass
vec3d cs = ss.CenterOfMass();
// get the secondary's center of mass
vec3d cm = ms.CenterOfMass();
// get the relative distance
vec3d cr = cs - cm;
// unit vector in direction of cr
// this will serve as the projection distance
vec3d cn(cr); cn.unit();
// initialize projection data
FENormalProjection np(ms);
np.SetTolerance(m_stol);
np.SetSearchRadius(m_srad);
np.Init();
// loop over all primary nodes
for (i=0; i<ss.Nodes(); ++i)
{
FENode& node = ss.Node(i);
// get the nodal position
vec3d r0 = node.m_r0;
// find the intersection with the secondary surface
ss.m_data[i].m_pme = np.Project3(r0, cn, rs);
assert(ss.m_data[i].m_pme);
ss.m_data[i].m_rs[0] = rs[0];
ss.m_data[i].m_rs[1] = rs[1];
}
}
//-----------------------------------------------------------------------------
void FEPeriodicBoundary2O::Update()
{
int i, j, ne;
FESurfaceElement* pme;
FEMesh& mesh = *m_ss.GetMesh();
//vec3d us, um;
vec3d ws, wm;
//vec3d umi[FEElement::MAX_NODES];
vec3d wmi[FEElement::MAX_NODES];
// update gap functions
int npass = (m_btwo_pass?2:1);
for (int np=0; np<npass; ++np)
{
FEPeriodicSurface& ss = (np == 0? m_ss : m_ms);
FEPeriodicSurface& ms = (np == 0? m_ms : m_ss);
// off-set sign
double s = (np==0?1.0:-1.0);
int N = ss.Nodes();
for (i=0; i<N; ++i)
{
// calculate the primary displacement
FENode& node = ss.Node(i);
//us = node.m_rt - node.m_r0;
ws = node.m_rt - m_Fmacro*node.m_r0 - m_Gmacro.contractdyad1(node.m_r0)*0.5;
// get the secondary element
pme = ss.m_data[i].m_pme;
// calculate the secondary displacement
ne = pme->Nodes();
for (j=0; j<ne; ++j)
{
FENode& node = ms.Node(pme->m_lnode[j]);
wmi[j] = node.m_rt - m_Fmacro*node.m_r0 - m_Gmacro.contractdyad1(node.m_r0)*0.5;
}
wm = pme->eval(wmi, ss.m_data[i].m_rs[0], ss.m_data[i].m_rs[1]);
// calculate gap function
//ss.m_gap[i] = ws - wm + m_off*s;
ss.m_data[i].m_gap = ws - wm;
}
}
}
//-----------------------------------------------------------------------------
void FEPeriodicBoundary2O::LoadVector(FEGlobalVector& R, const FETimeInfo& tp)
{
int j, k, l, m, n;
int nseln, nmeln;
double *Gr, *Gs;
// jacobian
double detJ;
vec3d dxr, dxs;
double* w;
// natural coordinates of primary node in secondary element
double r, s;
// contact force
vec3d tc;
// shape function values
double N[FEElement::MAX_NODES];
// element contact force vector
vector<double> fe;
// the lm array for this force vector
vector<int> lm;
// the en array
vector<int> en;
vector<int> sLM;
vector<int> mLM;
vec3d r0[FEElement::MAX_NODES], rt[FEElement::MAX_NODES];
int npass = (m_btwo_pass?2:1);
for (int np=0; np<npass; ++np)
{
FEPeriodicSurface& ss = (np == 0? m_ss : m_ms);
FEPeriodicSurface& ms = (np == 0? m_ms : m_ss);
for (int i=0; i<ss.m_data.size(); ++i) ss.m_data[i].m_Fr = vec3d(0,0,0);
// loop over all primary facets
int ne = ss.Elements();
for (j=0; j<ne; ++j)
{
// get the primary element
FESurfaceElement& sel = ss.Element(j);
// get the elements LM vector
ss.UnpackLM(sel, sLM);
nseln = sel.Nodes();
for (int i=0; i<nseln; ++i)
{
r0[i] = ss.GetMesh()->Node(sel.m_node[i]).m_r0;
rt[i] = ss.GetMesh()->Node(sel.m_node[i]).m_rt;
}
w = sel.GaussWeights();
// loop over primary element nodes (which are the integration points as well)
for (n=0; n<nseln; ++n)
{
Gr = sel.Gr(n);
Gs = sel.Gs(n);
m = sel.m_lnode[n];
// calculate jacobian
dxr = dxs = vec3d(0,0,0);
for (k=0; k<nseln; ++k)
{
dxr.x += Gr[k]*r0[k].x;
dxr.y += Gr[k]*r0[k].y;
dxr.z += Gr[k]*r0[k].z;
dxs.x += Gs[k]*r0[k].x;
dxs.y += Gs[k]*r0[k].y;
dxs.z += Gs[k]*r0[k].z;
}
detJ = (dxr ^ dxs).norm();
// get primary node contact force
tc = ss.m_data[m].m_Lm + ss.m_data[m].m_gap*m_eps;
ss.m_data[m].m_Tn = tc;
// get the secondary element
FESurfaceElement& mel = *ss.m_data[m].m_pme;
ms.UnpackLM(mel, mLM);
nmeln = mel.Nodes();
// isoparametric coordinates of the projected primary node
// onto the secondary element
r = ss.m_data[m].m_rs[0];
s = ss.m_data[m].m_rs[1];
// get the secondary shape function values at this primary node
if (nmeln == 4)
{
// quadrilateral
N[0] = 0.25*(1-r)*(1-s);
N[1] = 0.25*(1+r)*(1-s);
N[2] = 0.25*(1+r)*(1+s);
N[3] = 0.25*(1-r)*(1+s);
}
else if (nmeln == 3)
{
// triangle
N[0] = 1 - r - s;
N[1] = r;
N[2] = s;
}
else
{
assert(false);
}
// calculate force vector
fe.resize(3*(nmeln+1));
fe[0] = -detJ*w[n]*tc.x;
fe[1] = -detJ*w[n]*tc.y;
fe[2] = -detJ*w[n]*tc.z;
for (l=0; l<nmeln; ++l)
{
fe[3*(l+1) ] = detJ*w[n]*tc.x*N[l];
fe[3*(l+1)+1] = detJ*w[n]*tc.y*N[l];
fe[3*(l+1)+2] = detJ*w[n]*tc.z*N[l];
}
// fill the lm array
lm.resize(3*(nmeln+1));
lm[0] = sLM[n*3 ];
lm[1] = sLM[n*3+1];
lm[2] = sLM[n*3+2];
for (l=0; l<nmeln; ++l)
{
lm[3*(l+1) ] = mLM[l*3 ];
lm[3*(l+1)+1] = mLM[l*3+1];
lm[3*(l+1)+2] = mLM[l*3+2];
}
// fill the en array
en.resize(nmeln+1);
en[0] = sel.m_node[n];
for (l=0; l<nmeln; ++l) en[l+1] = mel.m_node[l];
// assemble into global force vector
R.Assemble(en, lm, fe);
// also store in the reaction force vector
vec3d& fr = ss.m_data[m].m_Fr;
fr.x += fe[0];
fr.y += fe[1];
fr.z += fe[2];
}
}
}
}
//-----------------------------------------------------------------------------
void FEPeriodicBoundary2O::StiffnessMatrix(FELinearSystem& LS, const FETimeInfo& tp)
{
int j, k, l, n, m;
int nseln, nmeln, ndof;
FEElementMatrix ke;
const int MN = FEElement::MAX_NODES;
vector<int> lm(3*(MN+1));
vector<int> en(MN+1);
double *Gr, *Gs, *w;
vec3d rt[MN], r0[MN];
vec3d rtm[MN];
double detJ, r, s;
vec3d dxr, dxs;
double H[MN];
vec3d gap, Lm, tc;
// curvature tensor K
double K[2][2] = {0};
// double scale = -0.0035*m_fem.GetMesh().GetBoundingBox().radius();
vector<int> sLM;
vector<int> mLM;
int npass = (m_btwo_pass?2:1);
for (int np=0; np<npass; ++np)
{
FEPeriodicSurface& ss = (np == 0? m_ss : m_ms);
FEPeriodicSurface& ms = (np == 0? m_ms : m_ss);
// loop over all primary elements
int ne = ss.Elements();
for (j=0; j<ne; ++j)
{
FESurfaceElement& se = ss.Element(j);
// get the element's LM vector
ss.UnpackLM(se, sLM);
nseln = se.Nodes();
for (int i=0; i<nseln; ++i)
{
r0[i] = ss.GetMesh()->Node(se.m_node[i]).m_r0;
rt[i] = ss.GetMesh()->Node(se.m_node[i]).m_rt;
}
w = se.GaussWeights();
// loop over all integration points (that is nodes)
for (n=0; n<nseln; ++n)
{
Gr = se.Gr(n);
Gs = se.Gs(n);
m = se.m_lnode[n];
// calculate jacobian
dxr = dxs = vec3d(0,0,0);
for (k=0; k<nseln; ++k)
{
dxr.x += Gr[k]*r0[k].x;
dxr.y += Gr[k]*r0[k].y;
dxr.z += Gr[k]*r0[k].z;
dxs.x += Gs[k]*r0[k].x;
dxs.y += Gs[k]*r0[k].y;
dxs.z += Gs[k]*r0[k].z;
}
detJ = (dxr ^ dxs).norm();
// get the secondary element
FESurfaceElement& me = *ss.m_data[m].m_pme;
ms.UnpackLM(me, mLM);
nmeln = me.Nodes();
// get the secondary element node positions
for (k=0; k<nmeln; ++k) rtm[k] = ms.GetMesh()->Node(me.m_node[k]).m_rt;
// primary node natural coordinates in secondary element
r = ss.m_data[m].m_rs[0];
s = ss.m_data[m].m_rs[1];
// get primary node normal force
tc = ss.m_data[m].m_Lm + ss.m_data[m].m_gap*m_eps; //ss.T[m];
// get the secondary shape function values at this primary node
if (nmeln == 4)
{
// quadrilateral
H[0] = 0.25*(1-r)*(1-s);
H[1] = 0.25*(1+r)*(1-s);
H[2] = 0.25*(1+r)*(1+s);
H[3] = 0.25*(1-r)*(1+s);
}
else if (nmeln == 3)
{
// triangle
H[0] = 1 - r - s;
H[1] = r;
H[2] = s;
}
else
{
assert(false);
}
// number of degrees of freedom
ndof = 3*(1 + nmeln);
// fill stiffness matrix
ke.resize(ndof, ndof); ke.zero();
ke[0][0] = w[n]*detJ*m_eps;
ke[1][1] = w[n]*detJ*m_eps;
ke[2][2] = w[n]*detJ*m_eps;
for (k=0; k<nmeln; ++k)
{
ke[0][3+3*k ] = -w[n]*detJ*m_eps*H[k];
ke[1][3+3*k+1] = -w[n]*detJ*m_eps*H[k];
ke[2][3+3*k+2] = -w[n]*detJ*m_eps*H[k];
ke[3+3*k ][0] = -w[n]*detJ*m_eps*H[k];
ke[3+3*k+1][1] = -w[n]*detJ*m_eps*H[k];
ke[3+3*k+2][2] = -w[n]*detJ*m_eps*H[k];
}
for (k=0; k<nmeln; ++k)
for (l=0; l<nmeln; ++l)
{
ke[3+3*k ][3+3*l ] = w[n]*detJ*m_eps*H[k]*H[l];
ke[3+3*k+1][3+3*l+1] = w[n]*detJ*m_eps*H[k]*H[l];
ke[3+3*k+2][3+3*l+2] = w[n]*detJ*m_eps*H[k]*H[l];
}
// create lm array
lm[0] = sLM[n*3 ];
lm[1] = sLM[n*3+1];
lm[2] = sLM[n*3+2];
for (k=0; k<nmeln; ++k)
{
lm[3*(k+1) ] = mLM[k*3 ];
lm[3*(k+1)+1] = mLM[k*3+1];
lm[3*(k+1)+2] = mLM[k*3+2];
}
// create the en array
en.resize(nmeln+1);
en[0] = se.m_node[n];
for (k=0; k<nmeln; ++k) en[k+1] = me.m_node[k];
// assemble stiffness matrix
ke.SetNodes(en);
ke.SetIndices(lm);
LS.Assemble(ke);
}
}
}
}
//-----------------------------------------------------------------------------
bool FEPeriodicBoundary2O::Augment(int naug, const FETimeInfo& tp)
{
// make sure we need to augment
if (m_laugon != FECore::AUGLAG_METHOD) return true;
int i;
double g;
vec3d lm;
// calculate initial norms
double normL0 = 0;
for (i=0; i<m_ss.Nodes(); ++i)
{
lm = m_ss.m_data[i].m_Lm;
normL0 += lm*lm;
}
for (i=0; i<m_ms.Nodes(); ++i)
{
lm = m_ms.m_data[i].m_Lm;
normL0 += lm*lm;
}
normL0 = sqrt(normL0);
// update Lagrange multipliers and calculate current norms
double normL1 = 0;
double normgc = 0;
int N = 0;
for (i=0; i<m_ss.Nodes(); ++i)
{
lm = m_ss.m_data[i].m_Lm + m_ss.m_data[i].m_gap*m_eps;
normL1 += lm*lm;
g = m_ss.m_data[i].m_gap.norm();
normgc += g*g;
++N;
}
for (i=0; i<m_ms.Nodes(); ++i)
{
lm = m_ms.m_data[i].m_Lm + m_ms.m_data[i].m_gap*m_eps;
normL1 += lm*lm;
g = m_ms.m_data[i].m_gap.norm();
normgc += g*g;
++N;
}
if (N == 0) N=1;
normL1 = sqrt(normL1);
normgc = sqrt(normgc / N);
feLog(" tied interface # %d\n", GetID());
feLog(" CURRENT REQUIRED\n");
double pctn = 0;
if (fabs(normL1) > 1e-10) pctn = fabs((normL1 - normL0)/normL1);
feLog(" normal force : %15le %15le\n", pctn, m_atol);
feLog(" gap function : %15le ***\n", normgc);
// check convergence of constraints
bool bconv = true;
if (pctn >= m_atol) bconv = false;
if (m_naugmin > naug) bconv = false;
// update Lagrange multipliers if we did not converge
if (bconv == false)
{
for (i=0; i<m_ss.Nodes(); ++i)
{
// update Lagrange multipliers
m_ss.m_data[i].m_Lm = m_ss.m_data[i].m_Lm + m_ss.m_data[i].m_gap*m_eps;
}
for (i=0; i<m_ms.Nodes(); ++i)
{
// update Lagrange multipliers
m_ms.m_data[i].m_Lm = m_ms.m_data[i].m_Lm + m_ms.m_data[i].m_gap*m_eps;
}
}
return bconv;
}
//-----------------------------------------------------------------------------
void FEPeriodicBoundary2O::Serialize(DumpStream &ar)
{
// store contact data
FEContactInterface::Serialize(ar);
// store contact surface data
m_ms.Serialize(ar);
m_ss.Serialize(ar);
}
| C++ |
3D | febiosoftware/FEBio | FEBioRVE/FEPeriodicBoundary2O.h | .h | 3,348 | 102 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#pragma once
#include "FEBioMech/FEContactInterface.h"
#include "FEBioMech/FEPeriodicBoundary.h"
#include "FEBioMech/FEContactSurface.h"
#include <FECore/tens3d.h>
//-----------------------------------------------------------------------------
class FEPeriodicBoundary2O : public FEContactInterface
{
public:
//! constructor
FEPeriodicBoundary2O(FEModel* pfem);
//! destructor
virtual ~FEPeriodicBoundary2O(void) {}
//! initialization
bool Init() override;
//! interface activation
void Activate() override;
//! serialize data to archive
void Serialize(DumpStream& ar) override;
//! return the primary and secondary surface
FESurface* GetPrimarySurface() override { return &m_ss; }
FESurface* GetSecondarySurface() override { return &m_ms; }
//! return integration rule class
bool UseNodalIntegration() override { return true; }
//! build the matrix profile for use in the stiffness matrix
void BuildMatrixProfile(FEGlobalMatrix& K) override;
//! create a copy of this interface
void CopyFrom(FESurfacePairConstraint* pci) override;
public:
//! calculate contact forces
void LoadVector(FEGlobalVector& R, const FETimeInfo& tp) override;
//! calculate contact stiffness
void StiffnessMatrix(FELinearSystem& LS, const FETimeInfo& tp) override;
//! calculate Lagrangian augmentations
bool Augment(int naug, const FETimeInfo& tp) override;
//! update
void Update() override;
protected:
void ProjectSurface(FEPeriodicSurface& ss, FEPeriodicSurface& ms, bool bmove);
public:
FEPeriodicSurface m_ss; //!< primary surface
FEPeriodicSurface m_ms; //!< secondary surface
double m_atol; //!< augmentation tolerance
double m_eps; //!< penalty scale factor
double m_stol; //!< search tolerance
double m_srad; //!< search radius (%)
bool m_btwo_pass; //!< two-pass flag
int m_naugmin; //!< minimum number of augmentations
vec3d m_off; //!< relative displacement offset
mat3d m_Fmacro; //!< Macroscopic deformation gradient
tens3drs m_Gmacro; //!< Macroscopic deformation Hessian
DECLARE_FECORE_CLASS();
};
| Unknown |
3D | febiosoftware/FEBio | FEBioRVE/FEMicroMaterial2O.cpp | .cpp | 5,218 | 155 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#include "stdafx.h"
#include "FEMicroMaterial2O.h"
#include "FECore/log.h"
#include "FEBioMech/FESolidSolver2.h"
#include "FEBioMech/FEElasticSolidDomain.h"
#include "FECore/FEAnalysis.h"
//#include "FEBioXML/FEBioImport.h"
//#include "FEBioPlot/FEBioPlotFile.h"
#include "FECore/tens3d.h"
#include "FEPeriodicBoundary2O.h"
#include "FERVEProbe.h"
//-----------------------------------------------------------------------------
FEMicroMaterialPoint2O::FEMicroMaterialPoint2O(FEMaterialPointData* mp) : FEMaterialPointData(mp)
{
m_elem_id = -1;
m_gpt_id = -1;
}
//-----------------------------------------------------------------------------
//! create a shallow copy
FEMaterialPointData* FEMicroMaterialPoint2O::Copy()
{
FEMicroMaterialPoint2O* pt = new FEMicroMaterialPoint2O(m_pNext?m_pNext->Copy():0);
return pt;
}
//-----------------------------------------------------------------------------
//! serialize material point data
void FEMicroMaterialPoint2O::Serialize(DumpStream& ar)
{
FEMaterialPointData::Serialize(ar);
}
//=============================================================================
//-----------------------------------------------------------------------------
// define the material parameters
BEGIN_FECORE_CLASS(FEMicroMaterial2O, FEElasticMaterial2O)
ADD_PARAMETER(m_szrve , "RVE" );
ADD_PARAMETER(m_szbc , "bc_set" );
ADD_PARAMETER(m_rveType , "rve_type" );
ADD_PARAMETER(m_scale , "scale");
ADD_PROPERTY(m_probe, "probe", false);
END_FECORE_CLASS();
//-----------------------------------------------------------------------------
FEMicroMaterial2O::FEMicroMaterial2O(FEModel* pfem) : FEElasticMaterial2O(pfem)
{
// initialize parameters
m_szrve[0] = 0;
m_szbc[0] = 0;
m_rveType = FERVEModel2O::DISPLACEMENT;
m_scale = 1.0;
}
//-----------------------------------------------------------------------------
FEMicroMaterial2O::~FEMicroMaterial2O(void)
{
}
//-----------------------------------------------------------------------------
FEMaterialPointData* FEMicroMaterial2O::CreateMaterialPointData()
{
return new FEMicroMaterialPoint2O(new FEElasticMaterialPoint2O(new FEElasticMaterialPoint()));
}
//-----------------------------------------------------------------------------
bool FEMicroMaterial2O::Init()
{
// initialize base class first
if (FEElasticMaterial::Init() == false) return false;
// load the parent RVE model
/* FEBioImport fim;
if (fim.Load(m_mrve, m_szrve.c_str()) == false)
{
return false;
}
*/
// we don't want to output anything from the RVE
m_mrve.BlockLog();
// scale geometry
m_mrve.ScaleGeometry(m_scale);
// initialize parent RVE
if (m_mrve.InitRVE(m_rveType, m_szbc.c_str()) == false) {
feLogError("An error occurred preparing RVE model"); return false;
}
return true;
}
//-----------------------------------------------------------------------------
void FEMicroMaterial2O::Stress(FEMaterialPoint &mp, mat3d& P, tens3drs& Q)
{
// get the deformation gradient and its gradient
FEElasticMaterialPoint& pt = *mp.ExtractData<FEElasticMaterialPoint>();
FEElasticMaterialPoint2O& pt2 = *mp.ExtractData<FEElasticMaterialPoint2O>();
FEMicroMaterialPoint2O& mmpt2O = *mp.ExtractData<FEMicroMaterialPoint2O>();
// get the deformation gradient and its gradient
const mat3d& F = pt.m_F;
const tens3drs& G = pt2.m_G;
// solve the RVE
bool bret = mmpt2O.m_rve.Solve(F, G);
// make sure it converged
if (bret == false) throw FEMultiScaleException(mmpt2O.m_elem_id, mmpt2O.m_gpt_id);
// calculate the averaged Cauchy stress
mmpt2O.m_rve.AveragedStress2O(P, Q);
}
//-----------------------------------------------------------------------------
void FEMicroMaterial2O::Tangent(FEMaterialPoint& mp, tens4d& C, tens5d& L, tens5d& H, tens6d& J)
{
FEMicroMaterialPoint2O& mmpt2O = *mp.ExtractData<FEMicroMaterialPoint2O>();
// calculate the averaged stiffness here
mmpt2O.m_rve.AveragedStiffness(mp, C, L, H, J);
}
| C++ |
3D | febiosoftware/FEBio | FEBioRVE/FEPeriodicBoundary1O.h | .h | 3,233 | 98 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#pragma once
#include "FEBioMech/FEContactInterface.h"
#include "FEBioMech/FEPeriodicBoundary.h"
#include "FEBioMech/FEContactSurface.h"
#include <FECore/tens3d.h>
//-----------------------------------------------------------------------------
class FEPeriodicBoundary1O : public FEContactInterface
{
public:
//! constructor
FEPeriodicBoundary1O(FEModel* pfem);
//! initialization
bool Init() override;
//! interface activation
void Activate() override;
//! serialize data to archive
void Serialize(DumpStream& ar) override;
//! return the primary and secondary surface
FESurface* GetPrimarySurface() override { return &m_ss; }
FESurface* GetSecondarySurface() override { return &m_ms; }
//! return integration rule class
bool UseNodalIntegration() override { return true; }
//! build the matrix profile for use in the stiffness matrix
void BuildMatrixProfile(FEGlobalMatrix& K) override;
//! create a copy of this interface
void CopyFrom(FESurfacePairConstraint* pci) override;
public:
//! calculate contact forces
void LoadVector(FEGlobalVector& R, const FETimeInfo& tp) override;
//! calculate contact stiffness
void StiffnessMatrix(FELinearSystem& LS, const FETimeInfo& tp) override;
//! calculate Lagrangian augmentations
bool Augment(int naug, const FETimeInfo& tp) override;
//! update
void Update() override;
protected:
void ProjectSurface(FEPeriodicSurface& ss, FEPeriodicSurface& ms, bool bmove);
public:
FEPeriodicSurface m_ss; //!< primary surface
FEPeriodicSurface m_ms; //!< secondary surface
double m_atol; //!< augmentation tolerance
double m_eps; //!< penalty scale factor
double m_stol; //!< search tolerance
double m_srad; //!< search radius (%)
bool m_btwo_pass; //!< two-pass flag
int m_naugmin; //!< minimum number of augmentations
vec3d m_off; //!< relative displacement offset
mat3d m_Fmacro; //!< Macroscopic deformation gradient
DECLARE_FECORE_CLASS();
};
| Unknown |
3D | febiosoftware/FEBio | FEBioRVE/FEPeriodicLinearConstraint2O.h | .h | 2,038 | 65 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#pragma once
#include <FECore/FEMesh.h>
#include "febiorve_api.h"
#include <vector>
class FEModel;
class FEBIORVE_API FEPeriodicLinearConstraint2O
{
class NodeSetSet
{
public:
NodeSetSet();
NodeSetSet(const NodeSetSet& nss);
void operator = (const NodeSetSet& nns);
public:
FENodeList primary;
FENodeList secondary;
};
public:
FEPeriodicLinearConstraint2O();
~FEPeriodicLinearConstraint2O();
void AddNodeSetPair(const FENodeList& ms, const FENodeList& ss, bool push_back = true);
bool GenerateConstraints(FEModel* fem);
private:
int closestNode(FEMesh& mesh, const FENodeList& set, const vec3d& r);
void addLinearConstraint(FEModel& fem, int parent, int child);
private:
std::vector<NodeSetSet> m_set; // list of node set pairs
};
| Unknown |
3D | febiosoftware/FEBio | FEBioRVE/FERVEModel.cpp | .cpp | 18,588 | 707 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#include "stdafx.h"
#include "FERVEModel.h"
#include "FECore/FESolidDomain.h"
#include "FECore/FEElemElemList.h"
#include "FEBioMech/FEElasticMaterial.h"
#include "FEPeriodicBoundary1O.h"
#include "FECore/FEAnalysis.h"
#include "FECore/FELoadCurve.h"
#include "FEBioMech/FEBCPrescribedDeformation.h"
#include "FEBioMech/FESolidSolver2.h"
#include "FEBioMech/FEElasticSolidDomain.h"
#include <FECore/FEPeriodicLinearConstraint.h>
#include <FECore/FECube.h>
#include <FECore/FEPointFunction.h>
#include <FECore/FECoreKernel.h>
//-----------------------------------------------------------------------------
FERVEModel::FERVEModel()
{
m_bctype = DISPLACEMENT;
// Don't collect function timings for RVE models.
CollectTimings(false);
}
//-----------------------------------------------------------------------------
FERVEModel::~FERVEModel()
{
}
//-----------------------------------------------------------------------------
void FERVEModel::SetParentModel(FEModel* fem)
{
m_parentfem = fem;
}
//-----------------------------------------------------------------------------
// copy from the parent RVE
void FERVEModel::CopyFrom(FERVEModel& rve)
{
// base class does most work
FEModel::CopyFrom(rve);
// copy the rest
m_parentfem = rve.m_parentfem;
m_bctype = rve.m_bctype;
m_V0 = rve.m_V0;
m_bb = rve.m_bb;
m_BN = rve.m_BN;
}
//-----------------------------------------------------------------------------
bool FERVEModel::Init()
{
// the RVE should only have one step
if (Steps() != 1) return false;
FEAnalysis* step = GetStep(0);
// match the end time with the parent's
FEAnalysis* parentStep = m_parentfem->GetStep(0);
double tend = parentStep->m_ntime * parentStep->m_dt0;
step->m_ntime = parentStep->m_ntime;
step->m_dt0 = parentStep->m_dt0;
step->m_tend = tend;
return FEModel::Init();
}
//-----------------------------------------------------------------------------
//! Initializes the RVE model and evaluates some useful quantities.
bool FERVEModel::InitRVE(int rveType, const char* szbc)
{
// the RVE should only have one step
if (Steps() != 1) return false;
FEAnalysis* step = GetStep(0);
// make sure the RVE problem doesn't output anything to a plot file
step->SetPlotLevel(FE_PLOT_NEVER);
// Center the RVE about the origin.
// This also calculates the bounding box
CenterRVE();
// generate prescribed BCs
// TODO: Make this part of the RVE definition
m_bctype = rveType;
if (m_bctype == DISPLACEMENT)
{
// find the boundary nodes
if ((szbc) && (szbc[0] != 0))
{
// get the RVE mesh
FEMesh& m = GetMesh();
// find the node set that defines the corner nodes
FENodeSet* pset = m.FindNodeSet(szbc);
if (pset == 0) return false;
// prep displacement BC's
if (PrepDisplacementBC(pset) == false) return false;
// tag all boundary nodes
int NN = m.Nodes();
m_BN.assign(NN, 0);
FENodeSet& ns = *pset;
for (int i=0; i<pset->Size(); ++i) m_BN[ns[i]] = 1;
}
else
{
// find all boundary nodes
FindBoundaryNodes(m_BN);
// create a (temporary) node set from the boundary nodes
FEMesh& mesh = GetMesh();
FENodeSet* set = new FENodeSet(this);
int NN = mesh.Nodes();
for (int i = 0; i<NN; ++i) if (m_BN[i] == 1) set->Add(i);
// prep the displacement BCs
if (PrepDisplacementBC(set) == false) return false;
}
}
else if (m_bctype == PERIODIC_LC)
{
PrepPeriodicLC();
}
/* else if (m_bctype == PERIODIC_AL)
{
// prep periodic BC's
if (PrepPeriodicBC(szbc) == false) return false;
}
*/ else return false;
// initialize base class
if (FEModel::Init() == false) return false;
// calculate intial RVE volume
EvalInitialVolume();
return true;
}
//-----------------------------------------------------------------------------
// This function sets up the periodic linear constraints boundary conditions.
// It assumes that the geometry is a cube.
bool FERVEModel::PrepPeriodicLC()
{
// make sure there no BCs defined
ClearBoundaryConditions();
// user needs to define corner nodes
// get the RVE mesh
FEMesh& m = GetMesh();
// Assuming it's a cube, build the surface, edge, and corner node data
FECube cube;
if (cube.Build(this) == false) return false;
// tag all boundary nodes
int NN = m.Nodes();
const FENodeSet& bs = cube.GetBoundaryNodes();
m_BN.resize(NN, 0);
for (int i = 0; i<bs.Size(); ++i) m_BN[bs[i]] = 1;
// now, build the linear constraints
FEPeriodicLinearConstraint plc(this);
plc.AddNodeSetPair(cube.GetSurface(0)->GetNodeList(), cube.GetSurface(1)->GetNodeList());
plc.AddNodeSetPair(cube.GetSurface(2)->GetNodeList(), cube.GetSurface(3)->GetNodeList());
plc.AddNodeSetPair(cube.GetSurface(4)->GetNodeList(), cube.GetSurface(5)->GetNodeList());
plc.GenerateConstraints(this);
// find the node set that defines the corner nodes
FENodeSet* corners = const_cast<FENodeSet*>(&cube.GetCornerNodes());
if (PrepDisplacementBC(corners) == false) return false;
return true;
}
//-----------------------------------------------------------------------------
//! Evaluates the initial volume of the RVE model.
//! This is called from FERVEModel::Init.
void FERVEModel::EvalInitialVolume()
{
m_V0 = 0;
FEMesh& m = GetMesh();
for (int k=0; k<m.Domains(); ++k)
{
FESolidDomain& dom = static_cast<FESolidDomain&>(m.Domain(k));
for (int i=0; i<dom.Elements(); ++i)
{
FESolidElement& el = dom.Element(i);
int nint = el.GaussPoints();
double* w = el.GaussWeights();
double ve = 0;
for (int n=0; n<nint; ++n)
{
FEElasticMaterialPoint& pt = *el.GetMaterialPoint(n)->ExtractData<FEElasticMaterialPoint>();
double J = dom.detJt(el, n);
ve += J*w[n];
}
m_V0 += ve;
}
}
}
//-----------------------------------------------------------------------------
//! Centers the RVE around the origin.
void FERVEModel::CenterRVE()
{
FEMesh& mesh = GetMesh();
FENode& node = mesh.Node(0);
// setup bounding box
FEBoundingBox box(node.m_r0, node.m_r0);
const int NN = mesh.Nodes();
for (int i=1; i<NN; ++i)
{
FENode& node = mesh.Node(i);
box.add(node.m_r0);
}
// get the center
vec3d c = box.center();
// recenter the RVE about the origin
for (int n = 0; n < NN; ++n)
{
FENode& node = mesh.Node(n);
node.m_r0 -= c;
node.m_rt = node.m_r0;
}
// adjust bounding box
m_bb.translate(-c);
}
//-----------------------------------------------------------------------------
//! Find the boundary nodes of the RVE model
void FERVEModel::FindBoundaryNodes(vector<int>& BN)
{
// first we need to find all the boundary nodes
FEMesh& m = GetMesh();
int NN = m.Nodes();
BN.assign(NN, 0);
// create the element-element list
FEElemElemList EEL;
EEL.Create(&m);
double wx = m_bb.width()*0.5;
double wy = m_bb.height()*0.5;
double wz = m_bb.depth()*0.5;
// use the E-E list to tag all exterior nodes
int fn[FEElement::MAX_NODES], M = 0;
for (int k=0; k<m.Domains(); ++k)
{
FEDomain& dom = m.Domain(k);
for (int i=0; i<dom.Elements(); ++i, ++M)
{
FEElement& el = dom.ElementRef(i);
int nf = el.Faces();
for (int j=0; j<nf; ++j)
{
if (EEL.Neighbor(M, j) == 0)
{
// mark all nodes
int nn = el.GetFace(j, fn);
for (int k=0; k<nn; ++k)
{
FENode& node = m.Node(fn[k]);
if (fabs(node.m_r0.x) >= 0.999*wx) BN[fn[k]] = 1;
if (fabs(node.m_r0.y) >= 0.999*wy) BN[fn[k]] = 1;
if (fabs(node.m_r0.z) >= 0.999*wz) BN[fn[k]] = 1;
}
}
}
}
}
}
//-----------------------------------------------------------------------------
// Setup the displacement boundary conditions.
bool FERVEModel::PrepDisplacementBC(FENodeSet* ns)
{
// create a load curve
FELoadCurve* plc = fecore_alloc(FELoadCurve, this);
plc->Add(0.0, 0.0);
plc->Add(1.0, 1.0);
AddLoadController(plc);
int NLC = LoadControllers() - 1;
// clear all BCs
ClearBoundaryConditions();
// we create the prescribed deformation BC
FEBCPrescribedDeformation* pdc = fecore_new<FEBCPrescribedDeformation>("prescribed deformation", this);
AddBoundaryCondition(pdc);
// assign the boundary nodes
pdc->SetNodeSet(ns);
return true;
}
//-----------------------------------------------------------------------------
bool FERVEModel::PrepPeriodicBC(const char* szbc)
{
// make sure the node set is valid
if ((szbc==0)||(szbc[0]==0)) return false;
// get the RVE mesh
FEMesh& m = GetMesh();
// find the node set that defines the corner nodes
FENodeSet* pset = m.FindNodeSet(szbc);
if (pset == 0) return false;
FENodeSet& ns = *pset;
// check the periodic constraints
int nc = SurfacePairConstraints();
if (nc != 3) return false;
for (int i=0; i<3; ++i)
{
FEPeriodicBoundary1O* pbc = dynamic_cast<FEPeriodicBoundary1O*>(SurfacePairConstraint(i));
if (pbc == 0) return false;
}
// create a load curve
FELoadCurve* plc = fecore_alloc(FELoadCurve, this);
plc->Add(0.0, 0.0);
plc->Add(1.0, 1.0);
AddLoadController(plc);
int NLC = LoadControllers() - 1;
// create the DC's
ClearBoundaryConditions();
FEBCPrescribedDeformation* pdc = fecore_new<FEBCPrescribedDeformation>("prescribed deformation", this);
AddBoundaryCondition(pdc);
// assign nodes to BCs
pdc->SetNodeSet(pset);
// create the boundary node flags
m_BN.assign(m.Nodes(), 0);
int N = ns.Size();
for (int i=0; i<N; ++i) m_BN[ns[i]] = 1;
return true;
}
//-----------------------------------------------------------------------------
void FERVEModel::Update(const mat3d& F)
{
// get the mesh
FEMesh& m = GetMesh();
// assign new DC's for the boundary nodes
FEBCPrescribedDeformation& dc = dynamic_cast<FEBCPrescribedDeformation&>(*BoundaryCondition(0));
dc.SetDeformationGradient(F);
if (m_bctype == FERVEModel::PERIODIC_AL)
{
// loop over periodic boundaries
for (int i = 0; i<3; ++i)
{
FEPeriodicBoundary1O* pc = dynamic_cast<FEPeriodicBoundary1O*>(SurfacePairConstraint(i));
assert(pc);
pc->m_Fmacro = F;
}
}
}
//-----------------------------------------------------------------------------
void FERVEModel::ScaleGeometry(double scale)
{
// get the mesh
FEMesh& mesh = GetMesh();
// calculate the center of mass first
vec3d rc(0,0,0);
for (int i = 0; i<mesh.Nodes(); ++i)
{
rc += mesh.Node(i).m_r0;
}
rc /= (double) mesh.Nodes();
// scale the nodal positions around the center
for (int i = 0; i<mesh.Nodes(); ++i)
{
FENode& node = mesh.Node(i);
vec3d r0 = node.m_r0;
node.m_r0 = rc + (r0 - rc)*scale;
vec3d rt = node.m_rt;
node.m_rt = rc + (rt - rc)*scale;
}
}
//-----------------------------------------------------------------------------
//! return current volume (calculated each time)
double FERVEModel::CurrentVolume()
{
// get the RVE mesh
FEMesh& m = GetMesh();
double V = 0.0;
for (int i = 0; i<m.Domains(); ++i)
{
FESolidDomain& dom = dynamic_cast<FESolidDomain&>(m.Domain(i));
int NE = dom.Elements();
for (int j = 0; j<NE; ++j)
{
FESolidElement& el = dom.Element(j);
int ni = el.GaussPoints();
int ne = el.Nodes();
double* w = el.GaussWeights();
for (int n = 0; n<ni; ++n)
{
FEMaterialPoint& pt = *el.GetMaterialPoint(n);
FEElasticMaterialPoint& ep = *pt.ExtractData<FEElasticMaterialPoint>();
// calculate Jacobian
double Jn = dom.detJt(el, n);
// add it all up
V += w[n] * Jn;
}
}
}
return V;
}
//-----------------------------------------------------------------------------
mat3ds FERVEModel::StressAverage(mat3d& F, FEMaterialPoint& mp)
{
// rewind the RCI
RCI_Rewind();
// update the BC's
Update(F);
// match the time step
FEModel& parent = *m_parentfem;
FETimeInfo& ti = parent.GetTime();
SetCurrentTimeStep(ti.timeIncrement);
// advance the RVE solution
bool bret = RCI_Advance();
assert(ti.currentTime == GetCurrentTime());
// make sure it converged
if (bret == false) throw FEMultiScaleException(-1, -1);
// calculate and return the (Cuachy) stress average
return StressAverage(mp);
}
//-----------------------------------------------------------------------------
//! Calculate the stress average
mat3ds FERVEModel::StressAverage(FEMaterialPoint& mp)
{
// get the RVE mesh
FEMesh& m = GetMesh();
mat3ds T; T.zero();
for (int i=0; i<m.Domains(); ++i)
{
FESolidDomain& dom = dynamic_cast<FESolidDomain&>(m.Domain(i));
int NE = dom.Elements();
for (int j=0; j<NE; ++j)
{
FESolidElement& el = dom.Element(j);
int ni = el.GaussPoints();
int ne = el.Nodes();
double* w = el.GaussWeights();
for (int n=0; n<ni; ++n)
{
FEMaterialPoint& pt = *el.GetMaterialPoint(n);
FEElasticMaterialPoint& ep = *pt.ExtractData<FEElasticMaterialPoint>();
// calculate Jacobian
double Jn = dom.detJt(el, n);
// add it all up
T += ep.m_s*(w[n]*Jn);
}
}
}
/* FEElasticMaterialPoint& pt = *mp.ExtractData<FEElasticMaterialPoint>();
double J = pt.m_J;
double V0 = InitialVolume();
return T / (J*V0);
*/
return T / CurrentVolume();
}
/*
//-----------------------------------------------------------------------------
//! Calculate the stress average
mat3ds FERVEModel::StressAverage(FEMaterialPoint& mp)
{
FEElasticMaterialPoint& pt = *mp.ExtractData<FEElasticMaterialPoint>();
double J = pt.m_J;
// get the RVE mesh
FEMesh& m = GetMesh();
mat3d T; T.zero();
// for periodic BC's we take the reaction forces directly from the periodic constraints
// TODO: Figure out a way to store all the reaction forces on the nodes
// That way, we don't need to do this anymore.
if (m_bctype == FERVEModel::PERIODIC_AL)
{
// get the reaction for from the periodic constraints
for (int i = 0; i<3; ++i)
{
FEPeriodicBoundary1O* pbc = dynamic_cast<FEPeriodicBoundary1O*>(SurfacePairInteraction(i));
assert(pbc);
FEPeriodicSurface& ss = pbc->m_ss;
int N = ss.Nodes();
for (int i = 0; i<N; ++i)
{
FENode& node = ss.Node(i);
vec3d f = ss.m_Fr[i];
// We multiply by two since the reaction forces are only stored at the primary surface
// and we also need to sum over the secondary nodes (NOTE: should I figure out a way to
// store the reaction forces on the secondary nodes as well?)
T += (f & node.m_rt)*2.0;
}
}
}
// get the reaction force vector from the solid solver
// (We also need to do this for the periodic BC, since at the prescribed nodes,
// the contact forces will be zero).
const int dof_X = GetDOFIndex("x");
const int dof_Y = GetDOFIndex("y");
const int dof_Z = GetDOFIndex("z");
FEAnalysis* pstep = GetCurrentStep();
FESolidSolver2* ps = dynamic_cast<FESolidSolver2*>(pstep->GetFESolver());
assert(ps);
vector<double>& R = ps->m_Fr;
if (m_bctype != FERVEModel::PERIODIC_LC)
{
// calculate the averaged stress
// TODO: This might be more efficient if we keep a list of boundary nodes
int NN = m.Nodes();
for (int j = 0; j<NN; ++j)
{
if (IsBoundaryNode(j))
{
FENode& n = m.Node(j);
vec3d f;
f.x = R[-n.m_ID[dof_X] - 2];
f.y = R[-n.m_ID[dof_Y] - 2];
f.z = R[-n.m_ID[dof_Z] - 2];
T += f & n.m_rt;
}
}
}
else
{
for (int i = 0; i<m_FN.size(); ++i)
{
FENode& n = m.Node(m_FN[i]);
vec3d f;
f.x = R[-n.m_ID[dof_X] - 2];
f.y = R[-n.m_ID[dof_Y] - 2];
f.z = R[-n.m_ID[dof_Z] - 2];
T += f & n.m_rt;
}
}
double V0 = InitialVolume();
return T.sym() / (J*V0);
}
*/
//-----------------------------------------------------------------------------
//! Calculate the average stiffness from the RVE solution.
tens4ds FERVEModel::StiffnessAverage(FEMaterialPoint &mp)
{
FEElasticMaterialPoint& pt = *mp.ExtractData<FEElasticMaterialPoint>();
// get the mesh
FEMesh& m = GetMesh();
// the element's stiffness matrix
matrix ke;
// element's residual
vector<double> fe;
// calculate the center point
vec3d rc(0, 0, 0);
for (int k = 0; k<m.Nodes(); ++k) rc += m.Node(k).m_rt;
rc /= (double)m.Nodes();
// elasticity tensor
tens4ds c(0.);
// calculate the stiffness matrix and residual
for (int k = 0; k<m.Domains(); ++k)
{
FEElasticSolidDomain& bd = dynamic_cast<FEElasticSolidDomain&>(m.Domain(k));
int NS = bd.Elements();
for (int n = 0; n<NS; ++n)
{
FESolidElement& e = bd.Element(n);
// create the element's stiffness matrix
int ne = e.Nodes();
int ndof = 3 * ne;
ke.resize(ndof, ndof);
ke.zero();
// calculate the element's stiffness matrix
bd.ElementStiffness(GetTime(), n, ke);
// create the element's residual
fe.assign(ndof, 0);
// calculate the element's residual
bd.ElementInternalForce(e, fe);
// loop over the element's nodes
for (int i = 0; i<ne; ++i)
{
FENode& ni = m.Node(e.m_node[i]);
for (int j = 0; j<ne; ++j)
{
FENode& nj = m.Node(e.m_node[j]);
if (IsBoundaryNode(e.m_node[i]) && IsBoundaryNode(e.m_node[j]))
{
// both nodes are boundary nodes
// so grab the element's submatrix
mat3d K;
ke.get(3 * i, 3 * j, K);
// get the nodal positions
vec3d ri = ni.m_rt - rc;
vec3d rj = nj.m_rt - rc;
// create the elasticity tensor
c += dyad4s(ri, K, rj);
}
}
}
}
}
// divide by volume
/* double V0 = InitialVolume();
double Vi = 1.0 / (pt.m_J * V0);
*/
double Vi = 1.0 / CurrentVolume();
c *= Vi;
return c;
}
// this function is hidden
bool FERVEModel::Solve() { assert(false); return false; }
| C++ |
3D | febiosoftware/FEBio | FEBioRVE/FEPeriodicLinearConstraint2O.cpp | .cpp | 7,057 | 278 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#include "stdafx.h"
#include "FEPeriodicLinearConstraint2O.h"
#include <FECore/FEModel.h>
#include <FECore/FELinearConstraintManager.h>
#include <FECore/FELinearConstraint.h>
#include <FECore/FEMesh.h>
#include <FECore/FESurface.h>
FEPeriodicLinearConstraint2O::NodeSetSet::NodeSetSet()
{
}
FEPeriodicLinearConstraint2O::NodeSetSet::NodeSetSet(const FEPeriodicLinearConstraint2O::NodeSetSet& nss)
{
secondary = nss.secondary;
primary = nss.primary;
}
void FEPeriodicLinearConstraint2O::NodeSetSet::operator = (const FEPeriodicLinearConstraint2O::NodeSetSet& nss)
{
secondary = nss.secondary;
primary = nss.primary;
}
FEPeriodicLinearConstraint2O::FEPeriodicLinearConstraint2O()
{
}
FEPeriodicLinearConstraint2O::~FEPeriodicLinearConstraint2O()
{
}
void FEPeriodicLinearConstraint2O::AddNodeSetPair(const FENodeList& ms, const FENodeList& ss, bool push_back)
{
NodeSetSet sp;
sp.secondary = ms;
sp.primary = ss;
if (push_back) m_set.push_back(sp); else m_set.insert(m_set.begin(), sp);
}
int FEPeriodicLinearConstraint2O::closestNode(FEMesh& mesh, const FENodeList& set, const vec3d& r)
{
int nmin = -1;
double Dmin = 0.0;
for (int i = 0; i<(int)set.Size(); ++i)
{
vec3d& ri = mesh.Node(set[i]).m_r0;
double D = (r - ri)*(r - ri);
if ((D < Dmin) || (nmin == -1))
{
Dmin = D;
nmin = i;
}
}
return nmin;
}
void FEPeriodicLinearConstraint2O::addLinearConstraint(FEModel& fem, int parent, int child)
{
// get the linear constraint manager
FELinearConstraintManager& LCM = fem.GetLinearConstraintManager();
// do one constraint for x, y, z
for (int j = 0; j<3; ++j)
{
FELinearConstraint* lc = fecore_alloc(FELinearConstraint, &fem);
lc->SetParentDof(j, parent);
lc->AddChildDof(j, child, 1.0);
LCM.AddLinearConstraint(lc);
}
}
bool FEPeriodicLinearConstraint2O::GenerateConstraints(FEModel* fem)
{
// get the model's mesh
FEMesh& mesh = fem->GetMesh();
// make sure there is a list of sets
if (m_set.empty()) return true;
// make sure there are three sets
if (m_set.size() != 3) return false;
// tag all nodes to identify what they are (edge, face, corner)
int N = mesh.Nodes();
vector<int> tag(N, 0);
for (size_t i = 0; i<m_set.size(); ++i)
{
FENodeList& ms = m_set[i].secondary;
FENodeList& ss = m_set[i].primary;
for (int j = 0; j<(int)ms.Size(); ++j) tag[ms[j]]++;
for (int j = 0; j<(int)ss.Size(); ++j) tag[ss[j]]++;
}
// flip signs on primary
for (size_t i = 0; i<m_set.size(); ++i)
{
FENodeList& ss = m_set[i].primary;
for (int j = 0; j<(int)ss.Size(); ++j)
{
int ntag = tag[ss[j]];
if (ntag > 0) tag[ss[j]] = -ntag;
}
}
// At this point, the following should hold
// primary nodes: tag < 0, secondary nodes: tag > 0, interior nodes: tag = 0
// only one secondary node should have a value of 3. We make this the reference node
int refNode = -1;
for (int i = 0; i<N; ++i)
{
if (tag[i] == 3)
{
assert(refNode == -1);
refNode = i;
}
}
assert(refNode != -1);
if (refNode == -1) return false;
// extract all 12 edges
vector<FENodeList> surf;
for (int i = 0; i<(int)m_set.size(); ++i)
{
surf.push_back(m_set[i].secondary);
surf.push_back(m_set[i].primary);
}
vector<FENodeList> secondaryEdges;
vector<FENodeList> primaryEdges;
for (int i = 0; i<surf.size(); ++i)
{
FENodeList& s0 = surf[i];
for (int j = i + 1; j<surf.size(); ++j)
{
FENodeList& s1 = surf[j];
vector<int> tmp(N, 0);
for (int k = 0; k<s0.Size(); ++k) tmp[s0[k]]++;
for (int k = 0; k<s1.Size(); ++k) tmp[s1[k]]++;
FENodeList edge(&mesh);
for (int k = 0; k<N; ++k)
{
if (tmp[k] == 2) edge.Add(k);
}
if (edge.Size() != 0)
{
// see if this is a secondary edge or not
// we assume it's a secondary edge if it connects to the refnode
bool bsecondary = false;
for (int k = 0; k<edge.Size(); ++k)
{
if (edge[k] == refNode)
{
bsecondary = true;
break;
}
}
if (bsecondary)
secondaryEdges.push_back(edge);
else
primaryEdges.push_back(edge);
}
}
}
// since it is assumed the geometry is a cube, the following must hold
assert(secondaryEdges.size() == 3);
assert(primaryEdges.size() == 9);
// find the secondary edge vectors
vec3d Em[3];
for (int i = 0; i<3; ++i)
{
FENodeList& edge = secondaryEdges[i];
// get the edge vector
Em[i] = edge.Node(0)->m_r0 - edge.Node(1)->m_r0; assert(edge[0] != edge[1]);
Em[i].unit();
}
// setup the constraints for the surfaces
for (int n=0; n<m_set.size(); ++n)
{
FENodeList& ms = m_set[n].secondary;
FENodeList& ss = m_set[n].primary;
// loop over all primary nodes
for (int i=0; i<ss.Size(); ++i)
{
assert(tag[ss[i]] < 0);
if (tag[ss[i]] == -1)
{
// get the nodal position
vec3d rs = ss.Node(i)->m_r0;
// find the corresponding node on the secondary side
int m = closestNode(mesh, ms, rs);
assert(tag[ms[m]] == 1);
// setup the linear constraint
addLinearConstraint(*fem, ss[i], ms[m]);
}
}
}
// setup the constraint for the edges
for (int n = 0; n<(int)primaryEdges.size(); ++n)
{
FENodeList& edge = primaryEdges[n];
// get the edge vector
vec3d E = edge.Node(0)->m_r0 - edge.Node(1)->m_r0; assert(edge[0] != edge[1]); E.unit();
// find the corresponding secondary edge
bool bfound = true;
for (int m = 0; m<3; ++m)
{
if (fabs(E*Em[m]) > 0.9999)
{
FENodeList& medge = secondaryEdges[m];
for (int i = 0; i<(int)edge.Size(); ++i)
{
assert(tag[edge[i]] < 0);
if (tag[edge[i]] == -2)
{
vec3d ri = edge.Node(i)->m_r0;
int k = closestNode(mesh, medge, ri);
addLinearConstraint(*fem, edge[i], medge[k]);
}
}
bfound = true;
break;
}
}
assert(bfound);
}
return true;
}
| C++ |
3D | febiosoftware/FEBio | FEBioRVE/FEElasticMultiscaleDomain2O.cpp | .cpp | 5,795 | 198 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#include "stdafx.h"
#include "FEElasticMultiscaleDomain2O.h"
#include "FEMicroMaterial2O.h"
#include "FERVEProbe.h"
#include "FECore/mat3d.h"
#include "FECore/tens6d.h"
#include <FECore/log.h>
//-----------------------------------------------------------------------------
//! constructor
FEElasticMultiscaleDomain2O::FEElasticMultiscaleDomain2O(FEModel* pfem) : FEElasticSolidDomain2O(pfem)
{
}
//-----------------------------------------------------------------------------
//! Initialize element data
bool FEElasticMultiscaleDomain2O::Init()
{
// initialize base class first
if (FEElasticSolidDomain2O::Init() == false) return false;
FEModel& fem = *GetFEModel();
// initialze RVEs
const int NE = FEElement::MAX_NODES;
vec3d x0[NE], xt[NE], r0, rt;
FEMesh& m = *GetMesh();
FEMicroMaterial2O* pmat = dynamic_cast<FEMicroMaterial2O*>(m_pMat);
FERVEModel2O& rve = pmat->m_mrve;
for (size_t i=0; i<m_Elem.size(); ++i)
{
FESolidElement& el = m_Elem[i];
int neln = el.Nodes();
for (int j=0; j<neln; ++j)
{
x0[j] = m.Node(el.m_node[j]).m_r0;
xt[j] = m.Node(el.m_node[j]).m_rt;
}
int n = el.GaussPoints();
for (int j=0; j<n; ++j)
{
r0 = el.Evaluate(x0, j);
rt = el.Evaluate(xt, j);
FEMaterialPoint& mp = *el.GetMaterialPoint(j);
FEElasticMaterialPoint& pt = *mp.ExtractData<FEElasticMaterialPoint>();
FEMicroMaterialPoint2O& mmpt2O = *mp.ExtractData<FEMicroMaterialPoint2O>();
mmpt2O.m_elem_id = el.GetID();
mmpt2O.m_gpt_id = j;
// initialize the material point RVE
// This essentially copies the parent RVE to the material point RVE
if (mmpt2O.m_rve.Init(rve) == false) return false;
}
}
// initialize surface RVEs
int nnf = 0;
int NF = m_surf.Elements();
for (int i=0; i<NF; ++i)
{
FESurfaceElement& face = m_surf.Element(i);
int nint = face.GaussPoints();
for (int n=0; n<nint; ++n, ++nnf)
{
for (int k=0; k<2; ++k)
{
FEMaterialPoint& mp = *m_surf.GetData(nnf).m_pt[k];
FEMicroMaterialPoint2O& mmpt2O = *mp.ExtractData<FEMicroMaterialPoint2O>();
mmpt2O.m_elem_id = -i-1;
mmpt2O.m_gpt_id = n + k*nint;
// Initialize the material point RVE
// This essentially copies the parent RVE model to the material points
mmpt2O.m_rve.Init(rve);
}
}
}
// TODO: I need to move the code below to FERVEProbe::Init.
/*
if (p.m_neid > 0)
{
FEElement* pel = FindElementFromID(p.m_neid);
if (pel == 0)
{
feLogError("Invalid Element ID for micro probe %d in material %d (%s)", i + 1, m_pMat->GetID(), m_pMat->GetName().c_str());
return false;
}
int nint = pel->GaussPoints();
int ngp = p.m_ngp - 1;
if ((ngp>=0)&&(ngp<nint))
{
FEMaterialPoint& mp = *pel->GetMaterialPoint(ngp);
FEMicroMaterialPoint2O& mmpt = *mp.ExtractData<FEMicroMaterialPoint2O>();
FERVEProbe* prve = new FERVEProbe(fem, mmpt.m_rve, p.m_szfile.c_str());
p.m_probe = prve;
prve->SetDebugFlag(p.m_bdebug);
}
else
{
feLogError("Invalid gausspt number for micro-probe %d in material %d (%s)", i + 1, m_pMat->GetID(), m_pMat->GetName().c_str());
return false;
}
}
else
{
int fid = -p.m_neid-1;
if ((fid < 0) || (fid >= m_surf.Elements()))
{
feLogError("Invalid surface ID for micro-probe");
return false;
}
int nnf = 0;
for (int j=0; j<fid; ++j)
{
FESurfaceElement& el = m_surf.Element(j);
int nint = el.GaussPoints();
nnf += nint;
}
FESurfaceElement& face = m_surf.Element(fid);
int nint = face.GaussPoints();
int k = (p.m_ngp-1)/nint;
int gpt = (p.m_ngp-1)%nint;
nnf += gpt;
FEMaterialPoint& mp = *m_surf.GetData(nnf).m_pt[k];
FEMicroMaterialPoint2O& mmpt2O = *mp.ExtractData<FEMicroMaterialPoint2O>();
if (mmpt2O.m_elem_id != p.m_neid ) return false;
if (mmpt2O.m_gpt_id != p.m_ngp-1) return false;
FERVEProbe* prve = new FERVEProbe(fem, mmpt2O.m_rve, p.m_szfile.c_str());
p.m_probe = prve;
prve->SetDebugFlag(p.m_bdebug);
}
*/
return true;
}
//-----------------------------------------------------------------------------
void FEElasticMultiscaleDomain2O::Update(const FETimeInfo& timeInfo)
{
try
{
// call base class
FEElasticSolidDomain2O::Update(timeInfo);
}
catch (FEMultiScaleException)
{
// store all the probes
FEMicroMaterial2O* pmat = dynamic_cast<FEMicroMaterial2O*>(m_pMat);
int NP = pmat->Probes();
for (int i=0; i<NP; ++i)
{
FERVEProbe& p = pmat->Probe(i);
if (p.GetDebugFlag()) p.Save();
}
// retrhow
throw;
}
}
| C++ |
3D | febiosoftware/FEBio | FEBioRVE/FEPeriodicBoundary1O.cpp | .cpp | 16,542 | 685 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#include "stdafx.h"
#include "FEPeriodicBoundary1O.h"
#include "FECore/FEModel.h"
#include "FECore/FENormalProjection.h"
#include "FECore/FEGlobalMatrix.h"
#include <FECore/FELinearSystem.h>
#include "FECore/log.h"
//-----------------------------------------------------------------------------
// Define sliding interface parameters
BEGIN_FECORE_CLASS(FEPeriodicBoundary1O, FEContactInterface)
ADD_PARAMETER(m_laugon , "laugon" )->setLongName("Enforcement method")->setEnums("PENALTY\0AUGLAG\0");
ADD_PARAMETER(m_atol , "tolerance");
ADD_PARAMETER(m_eps , "penalty" );
ADD_PARAMETER(m_btwo_pass, "two_pass" );
ADD_PARAMETER(m_off , "offset" );
ADD_PARAMETER(m_naugmin , "minaug" );
END_FECORE_CLASS();
//-----------------------------------------------------------------------------
// FEPeriodicBoundary
//-----------------------------------------------------------------------------
FEPeriodicBoundary1O::FEPeriodicBoundary1O(FEModel* pfem) : FEContactInterface(pfem), m_ss(pfem), m_ms(pfem)
{
static int count = 1;
SetID(count++);
m_stol = 0.01;
m_srad = 1.0;
m_atol = 0;
m_eps = 0;
m_btwo_pass = false;
m_off = vec3d(0,0,0);
m_naugmin = 0;
// set parents
m_ss.SetContactInterface(this);
m_ms.SetContactInterface(this);
m_ss.SetSibling(&m_ms);
m_ms.SetSibling(&m_ss);
m_Fmacro.zero();
}
//-----------------------------------------------------------------------------
bool FEPeriodicBoundary1O::Init()
{
// create the surfaces
if (m_ss.Init() == false) return false;
if (m_ms.Init() == false) return false;
return true;
}
//-----------------------------------------------------------------------------
void FEPeriodicBoundary1O::Activate()
{
// don't forget to call the base class
FEContactInterface::Activate();
// project primary surface onto secondary surface
ProjectSurface(m_ss, m_ms, false);
ProjectSurface(m_ms, m_ss, false);
}
//-----------------------------------------------------------------------------
void FEPeriodicBoundary1O::CopyFrom(FESurfacePairConstraint* pci)
{
// cast to a periodic boundary
FEPeriodicBoundary1O& pb = dynamic_cast<FEPeriodicBoundary1O&>(*pci);
// copy parameters
GetParameterList() = pb.GetParameterList();
// copy nodes
m_ss.CopyFrom(pb.m_ss);
m_ms.CopyFrom(pb.m_ms);
}
//-----------------------------------------------------------------------------
//! build the matrix profile for use in the stiffness matrix
// TODO: what if two_pass ??
void FEPeriodicBoundary1O::BuildMatrixProfile(FEGlobalMatrix& K)
{
FEModel& fem = *GetFEModel();
FEMesh& mesh = fem.GetMesh();
// get the DOFS
const int dof_X = fem.GetDOFIndex("x");
const int dof_Y = fem.GetDOFIndex("y");
const int dof_Z = fem.GetDOFIndex("z");
const int dof_RU = fem.GetDOFIndex("Ru");
const int dof_RV = fem.GetDOFIndex("Rv");
const int dof_RW = fem.GetDOFIndex("Rw");
vector<int> lm(6*5);
for (int j=0; j<m_ss.Nodes(); ++j)
{
FESurfaceElement& me = *m_ss.m_data[j].m_pme;
int* en = &me.m_node[0];
int n = me.Nodes();
if (n == 3)
{
lm[6*(3+1) ] = -1;
lm[6*(3+1)+1] = -1;
lm[6*(3+1)+2] = -1;
lm[6*(3+1)+3] = -1;
lm[6*(3+1)+4] = -1;
lm[6*(3+1)+5] = -1;
}
lm[0] = m_ss.Node(j).m_ID[dof_X];
lm[1] = m_ss.Node(j).m_ID[dof_Y];
lm[2] = m_ss.Node(j).m_ID[dof_Z];
lm[3] = m_ss.Node(j).m_ID[dof_RU];
lm[4] = m_ss.Node(j).m_ID[dof_RV];
lm[5] = m_ss.Node(j).m_ID[dof_RW];
for (int k=0; k<n; ++k)
{
vector<int>& id = mesh.Node(en[k]).m_ID;
lm[6*(k+1) ] = id[dof_X];
lm[6*(k+1)+1] = id[dof_Y];
lm[6*(k+1)+2] = id[dof_Z];
lm[6*(k+1)+3] = id[dof_RU];
lm[6*(k+1)+4] = id[dof_RV];
lm[6*(k+1)+5] = id[dof_RW];
}
K.build_add(lm);
}
}
//-----------------------------------------------------------------------------
//! project surface
void FEPeriodicBoundary1O::ProjectSurface(FEPeriodicSurface& ss, FEPeriodicSurface& ms, bool bmove)
{
int i;
double rs[2];
// get the primary's center of mass
vec3d cs = ss.CenterOfMass();
// get the secondary's center of mass
vec3d cm = ms.CenterOfMass();
// get the relative distance
vec3d cr = cs - cm;
// unit vector in direction of cr
// this will serve as the projection distance
vec3d cn(cr); cn.unit();
// initialize projection data
FENormalProjection np(ms);
np.SetTolerance(m_stol);
np.SetSearchRadius(m_srad);
np.Init();
// loop over all primary nodes
for (i=0; i<ss.Nodes(); ++i)
{
FENode& node = ss.Node(i);
// get the nodal position
vec3d r0 = node.m_r0;
// find the intersection with the secondary surface
ss.m_data[i].m_pme = np.Project3(r0, cn, rs);
assert(ss.m_data[i].m_pme);
ss.m_data[i].m_rs[0] = rs[0];
ss.m_data[i].m_rs[1] = rs[1];
}
}
//-----------------------------------------------------------------------------
void FEPeriodicBoundary1O::Update()
{
int i, j, ne;
FESurfaceElement* pme;
FEMesh& mesh = *m_ss.GetMesh();
//vec3d us, um;
vec3d ws, wm;
//vec3d umi[FEElement::MAX_NODES];
vec3d wmi[FEElement::MAX_NODES];
// update gap functions
int npass = (m_btwo_pass?2:1);
for (int np=0; np<npass; ++np)
{
FEPeriodicSurface& ss = (np == 0? m_ss : m_ms);
FEPeriodicSurface& ms = (np == 0? m_ms : m_ss);
// off-set sign
double s = (np==0?1.0:-1.0);
int N = ss.Nodes();
for (i=0; i<N; ++i)
{
// calculate the primary displacement
FENode& node = ss.Node(i);
ws = node.m_rt - m_Fmacro*node.m_r0;
// get the secondary element
pme = ss.m_data[i].m_pme;
// calculate the secondary displacement
ne = pme->Nodes();
for (j=0; j<ne; ++j)
{
FENode& node = ms.Node(pme->m_lnode[j]);
wmi[j] = node.m_rt - m_Fmacro*node.m_r0;
}
wm = pme->eval(wmi, ss.m_data[i].m_rs[0], ss.m_data[i].m_rs[1]);
// calculate gap function
ss.m_data[i].m_gap = ws - wm;
}
}
}
//-----------------------------------------------------------------------------
void FEPeriodicBoundary1O::LoadVector(FEGlobalVector& R, const FETimeInfo& tp)
{
int j, k, l, m, n;
int nseln, nmeln;
double *Gr, *Gs;
// jacobian
double detJ;
vec3d dxr, dxs;
double* w;
// natural coordinates of primary node in secondary element
double r, s;
// contact force
vec3d tc;
// shape function values
double N[FEElement::MAX_NODES];
// element contact force vector
vector<double> fe;
// the lm array for this force vector
vector<int> lm;
// the en array
vector<int> en;
vector<int> sLM;
vector<int> mLM;
vec3d r0[FEElement::MAX_NODES], rt[FEElement::MAX_NODES];
int npass = (m_btwo_pass?2:1);
for (int np=0; np<npass; ++np)
{
FEPeriodicSurface& ss = (np == 0? m_ss : m_ms);
FEPeriodicSurface& ms = (np == 0? m_ms : m_ss);
for (int i=0; i<ss.m_data.size(); ++i) ss.m_data[i].m_Fr = vec3d(0,0,0);
// loop over all primary facets
int ne = ss.Elements();
for (j=0; j<ne; ++j)
{
// get the primary element
FESurfaceElement& sel = ss.Element(j);
// get the elements LM vector
ss.UnpackLM(sel, sLM);
nseln = sel.Nodes();
for (int i=0; i<nseln; ++i)
{
r0[i] = ss.GetMesh()->Node(sel.m_node[i]).m_r0;
rt[i] = ss.GetMesh()->Node(sel.m_node[i]).m_rt;
}
w = sel.GaussWeights();
// loop over primary element nodes (which are the integration points as well)
for (n=0; n<nseln; ++n)
{
Gr = sel.Gr(n);
Gs = sel.Gs(n);
m = sel.m_lnode[n];
// calculate jacobian
dxr = dxs = vec3d(0,0,0);
for (k=0; k<nseln; ++k)
{
dxr.x += Gr[k]*r0[k].x;
dxr.y += Gr[k]*r0[k].y;
dxr.z += Gr[k]*r0[k].z;
dxs.x += Gs[k]*r0[k].x;
dxs.y += Gs[k]*r0[k].y;
dxs.z += Gs[k]*r0[k].z;
}
detJ = (dxr ^ dxs).norm();
// get primary node contact force
tc = ss.m_data[m].m_Lm + ss.m_data[m].m_gap*m_eps;
ss.m_data[m].m_Tn = tc;
// get the secondary element
FESurfaceElement& mel = *ss.m_data[m].m_pme;
ms.UnpackLM(mel, mLM);
nmeln = mel.Nodes();
// isoparametric coordinates of the projected primary node
// onto the secondary element
r = ss.m_data[m].m_rs[0];
s = ss.m_data[m].m_rs[1];
// get the secondary shape function values at this primary node
if (nmeln == 4)
{
// quadrilateral
N[0] = 0.25*(1-r)*(1-s);
N[1] = 0.25*(1+r)*(1-s);
N[2] = 0.25*(1+r)*(1+s);
N[3] = 0.25*(1-r)*(1+s);
}
else if (nmeln == 3)
{
// triangle
N[0] = 1 - r - s;
N[1] = r;
N[2] = s;
}
else
{
assert(false);
}
// calculate force vector
fe.resize(3*(nmeln+1));
fe[0] = -detJ*w[n]*tc.x;
fe[1] = -detJ*w[n]*tc.y;
fe[2] = -detJ*w[n]*tc.z;
for (l=0; l<nmeln; ++l)
{
fe[3*(l+1) ] = detJ*w[n]*tc.x*N[l];
fe[3*(l+1)+1] = detJ*w[n]*tc.y*N[l];
fe[3*(l+1)+2] = detJ*w[n]*tc.z*N[l];
}
// fill the lm array
lm.resize(3*(nmeln+1));
lm[0] = sLM[n*3 ];
lm[1] = sLM[n*3+1];
lm[2] = sLM[n*3+2];
for (l=0; l<nmeln; ++l)
{
lm[3*(l+1) ] = mLM[l*3 ];
lm[3*(l+1)+1] = mLM[l*3+1];
lm[3*(l+1)+2] = mLM[l*3+2];
}
// fill the en array
en.resize(nmeln+1);
en[0] = sel.m_node[n];
for (l=0; l<nmeln; ++l) en[l+1] = mel.m_node[l];
// assemble into global force vector
R.Assemble(en, lm, fe);
// also store in the reaction force vector
vec3d& fr = ss.m_data[m].m_Fr;
fr.x += fe[0];
fr.y += fe[1];
fr.z += fe[2];
}
}
}
}
//-----------------------------------------------------------------------------
void FEPeriodicBoundary1O::StiffnessMatrix(FELinearSystem& LS, const FETimeInfo& tp)
{
int j, k, l, n, m;
int nseln, nmeln, ndof;
FEElementMatrix ke;
const int MN = FEElement::MAX_NODES;
vector<int> lm(3*(MN+1));
vector<int> en(MN+1);
double *Gr, *Gs, *w;
vec3d rt[MN], r0[MN];
vec3d rtm[MN];
double detJ, r, s;
vec3d dxr, dxs;
double H[MN];
vec3d gap, Lm, tc;
// curvature tensor K
double K[2][2] = {0};
// double scale = -0.0035*m_fem.GetMesh().GetBoundingBox().radius();
vector<int> sLM;
vector<int> mLM;
int npass = (m_btwo_pass?2:1);
for (int np=0; np<npass; ++np)
{
FEPeriodicSurface& ss = (np == 0? m_ss : m_ms);
FEPeriodicSurface& ms = (np == 0? m_ms : m_ss);
// loop over all primary elements
int ne = ss.Elements();
for (j=0; j<ne; ++j)
{
FESurfaceElement& se = ss.Element(j);
// get the element's LM vector
ss.UnpackLM(se, sLM);
nseln = se.Nodes();
for (int i=0; i<nseln; ++i)
{
r0[i] = ss.GetMesh()->Node(se.m_node[i]).m_r0;
rt[i] = ss.GetMesh()->Node(se.m_node[i]).m_rt;
}
w = se.GaussWeights();
// loop over all integration points (that is nodes)
for (n=0; n<nseln; ++n)
{
Gr = se.Gr(n);
Gs = se.Gs(n);
m = se.m_lnode[n];
// calculate jacobian
dxr = dxs = vec3d(0,0,0);
for (k=0; k<nseln; ++k)
{
dxr.x += Gr[k]*r0[k].x;
dxr.y += Gr[k]*r0[k].y;
dxr.z += Gr[k]*r0[k].z;
dxs.x += Gs[k]*r0[k].x;
dxs.y += Gs[k]*r0[k].y;
dxs.z += Gs[k]*r0[k].z;
}
detJ = (dxr ^ dxs).norm();
// get the secondary element
FESurfaceElement& me = *ss.m_data[m].m_pme;
ms.UnpackLM(me, mLM);
nmeln = me.Nodes();
// get the secondary element node positions
for (k=0; k<nmeln; ++k) rtm[k] = ms.GetMesh()->Node(me.m_node[k]).m_rt;
// primary node natural coordinates in secondary element
r = ss.m_data[m].m_rs[0];
s = ss.m_data[m].m_rs[1];
// get primary node normal force
tc = ss.m_data[m].m_Lm + ss.m_data[m].m_gap*m_eps; //ss.T[m];
// get the secondary shape function values at this primary node
if (nmeln == 4)
{
// quadrilateral
H[0] = 0.25*(1-r)*(1-s);
H[1] = 0.25*(1+r)*(1-s);
H[2] = 0.25*(1+r)*(1+s);
H[3] = 0.25*(1-r)*(1+s);
}
else if (nmeln == 3)
{
// triangle
H[0] = 1 - r - s;
H[1] = r;
H[2] = s;
}
else
{
assert(false);
}
// number of degrees of freedom
ndof = 3*(1 + nmeln);
// fill stiffness matrix
ke.resize(ndof, ndof); ke.zero();
ke[0][0] = w[n]*detJ*m_eps;
ke[1][1] = w[n]*detJ*m_eps;
ke[2][2] = w[n]*detJ*m_eps;
for (k=0; k<nmeln; ++k)
{
ke[0][3+3*k ] = -w[n]*detJ*m_eps*H[k];
ke[1][3+3*k+1] = -w[n]*detJ*m_eps*H[k];
ke[2][3+3*k+2] = -w[n]*detJ*m_eps*H[k];
ke[3+3*k ][0] = -w[n]*detJ*m_eps*H[k];
ke[3+3*k+1][1] = -w[n]*detJ*m_eps*H[k];
ke[3+3*k+2][2] = -w[n]*detJ*m_eps*H[k];
}
for (k=0; k<nmeln; ++k)
for (l=0; l<nmeln; ++l)
{
ke[3+3*k ][3+3*l ] = w[n]*detJ*m_eps*H[k]*H[l];
ke[3+3*k+1][3+3*l+1] = w[n]*detJ*m_eps*H[k]*H[l];
ke[3+3*k+2][3+3*l+2] = w[n]*detJ*m_eps*H[k]*H[l];
}
// create lm array
lm[0] = sLM[n*3 ];
lm[1] = sLM[n*3+1];
lm[2] = sLM[n*3+2];
for (k=0; k<nmeln; ++k)
{
lm[3*(k+1) ] = mLM[k*3 ];
lm[3*(k+1)+1] = mLM[k*3+1];
lm[3*(k+1)+2] = mLM[k*3+2];
}
// create the en array
en.resize(nmeln+1);
en[0] = se.m_node[n];
for (k=0; k<nmeln; ++k) en[k+1] = me.m_node[k];
// assemble stiffness matrix
ke.SetNodes(en);
ke.SetIndices(lm);
LS.Assemble(ke);
}
}
}
}
//-----------------------------------------------------------------------------
bool FEPeriodicBoundary1O::Augment(int naug, const FETimeInfo& tp)
{
// make sure we need to augment
if (m_laugon != FECore::AUGLAG_METHOD) return true;
int i;
double g;
vec3d lm;
// calculate initial norms
double normL0 = 0;
for (i=0; i<m_ss.Nodes(); ++i)
{
lm = m_ss.m_data[i].m_Lm;
normL0 += lm*lm;
}
for (i=0; i<m_ms.Nodes(); ++i)
{
lm = m_ms.m_data[i].m_Lm;
normL0 += lm*lm;
}
normL0 = sqrt(normL0);
// update Lagrange multipliers and calculate current norms
double normL1 = 0;
double normgc = 0;
int N = 0;
for (i=0; i<m_ss.Nodes(); ++i)
{
lm = m_ss.m_data[i].m_Lm + m_ss.m_data[i].m_gap*m_eps;
normL1 += lm*lm;
g = m_ss.m_data[i].m_gap.norm();
normgc += g*g;
++N;
}
for (i=0; i<m_ms.Nodes(); ++i)
{
lm = m_ms.m_data[i].m_Lm + m_ms.m_data[i].m_gap*m_eps;
normL1 += lm*lm;
g = m_ms.m_data[i].m_gap.norm();
normgc += g*g;
++N;
}
if (N == 0) N=1;
normL1 = sqrt(normL1);
normgc = sqrt(normgc / N);
feLog(" tied interface # %d\n", GetID());
feLog(" CURRENT REQUIRED\n");
double pctn = 0;
if (fabs(normL1) > 1e-10) pctn = fabs((normL1 - normL0)/normL1);
feLog(" normal force : %15le %15le\n", pctn, m_atol);
feLog(" gap function : %15le ***\n", normgc);
// check convergence of constraints
bool bconv = true;
if (pctn >= m_atol) bconv = false;
if (m_naugmin > naug) bconv = false;
// update Lagrange multipliers if we did not converge
if (bconv == false)
{
for (i=0; i<m_ss.Nodes(); ++i)
{
// update Lagrange multipliers
m_ss.m_data[i].m_Lm = m_ss.m_data[i].m_Lm + m_ss.m_data[i].m_gap*m_eps;
}
for (i=0; i<m_ms.Nodes(); ++i)
{
// update Lagrange multipliers
m_ms.m_data[i].m_Lm = m_ms.m_data[i].m_Lm + m_ms.m_data[i].m_gap*m_eps;
}
}
return bconv;
}
//-----------------------------------------------------------------------------
void FEPeriodicBoundary1O::Serialize(DumpStream &ar)
{
// store contact data
FEContactInterface::Serialize(ar);
// store contact surface data
m_ms.Serialize(ar);
m_ss.Serialize(ar);
}
| C++ |
3D | febiosoftware/FEBio | FEBioRVE/FEBioRVE.cpp | .cpp | 2,913 | 69 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#include "stdafx.h"
#include "FEBioRVE.h"
#include "FEMicroMaterial.h"
#include "FEMicroMaterial2O.h"
#include "FEMindlinElastic2O.h"
#include "FEPeriodicBoundary2O.h"
#include "FE2OMicroConstraint.h"
#include "FEElasticMultiscaleDomain1O.h"
#include "FEElasticMultiscaleDomain2O.h"
#include "FEMultiscaleDomainFactory.h"
#include "FEBioRVEPlot.h"
#include "FERVEProbe.h"
//-----------------------------------------------------------------------------
//! Register all the classes of the FEBioMech module with the FEBio framework.
void FEBioRVE::InitModule()
{
FECoreKernel& febio = FECoreKernel::GetInstance();
febio.RegisterDomain(new FEMultiScaleDomainFactory, true);
// this module extends the solid module
febio.SetActiveModule("solid");
REGISTER_FECORE_CLASS(FEMicroMaterial, "micro-material");
REGISTER_FECORE_CLASS(FEMicroMaterial2O, "micro-material2O");
REGISTER_FECORE_CLASS(FEMindlinElastic2O, "mindlin elastic");
REGISTER_FECORE_CLASS(FEMicroProbe, "probe");
REGISTER_FECORE_CLASS(FEElasticMultiscaleDomain1O, "elastic-mm-solid");
REGISTER_FECORE_CLASS(FEElasticMultiscaleDomain2O, "elastic-mm-solid2O");
REGISTER_FECORE_CLASS(FEElasticSolidDomain2O, "elastic-solid2O");
REGISTER_FECORE_CLASS(FE2OMicroConstraint, "2O microfluc");
REGISTER_FECORE_CLASS(FEPeriodicBoundary1O, "periodic boundary1O");
REGISTER_FECORE_CLASS(FEPeriodicBoundary2O, "periodic boundary2O");
REGISTER_FECORE_CLASS(FEPlotElementGnorm, "G norm");
REGISTER_FECORE_CLASS(FEPlotElementPK1norm, "PK1 norm");
REGISTER_FECORE_CLASS(FEPlotElementQK1norm, "QK1 norm");
REGISTER_FECORE_CLASS(FEPlotElementMicroEnergy, "micro energy");
}
| C++ |
3D | febiosoftware/FEBio | FEBioRVE/FEMindlinElastic2O.cpp | .cpp | 4,306 | 155 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#include "stdafx.h"
#include "FEMindlinElastic2O.h"
BEGIN_FECORE_CLASS(FEMindlinElastic2O, FEElasticMaterial2O)
ADD_PARAMETER(m_lam, "lam");
ADD_PARAMETER(m_mu , "mu" );
ADD_PARAMETER(m_a1 , "a1" );
ADD_PARAMETER(m_a2 , "a2" );
ADD_PARAMETER(m_a3 , "a3" );
ADD_PARAMETER(m_a4 , "a4" );
ADD_PARAMETER(m_a5 , "a5" );
END_FECORE_CLASS();
FEMindlinElastic2O::FEMindlinElastic2O(FEModel* pfem) : FEElasticMaterial2O(pfem)
{
m_mu = 0.0;
m_lam = 0.0;
m_a1 = 0.0;
m_a2 = 0.0;
m_a3 = 0.0;
m_a4 = 0.0;
m_a5 = 0.0;
}
//! Calculate PK1 stress and higher order stress Q
void FEMindlinElastic2O::Stress(FEMaterialPoint& mp, mat3d& P, tens3drs& Q)
{
FEElasticMaterialPoint& pt = *mp.ExtractData<FEElasticMaterialPoint>();
FEElasticMaterialPoint2O& pt2O = *mp.ExtractData<FEElasticMaterialPoint2O>();
// deformation gradient and transpose
const mat3d& F = pt.m_F;
mat3d Ft = F.transpose();
// gradient of deformation gradient
const tens3drs& G = pt2O.m_G;
// identity tensor
mat3dd I(1.0);
// Lagrange strain
mat3d E = (Ft*F - I)*0.5;
double trE = E.trace();
// PK1 stress
P = F*(m_lam*trE) + (F*E)*(2.0*m_mu);
// higher order stress
for (int p=0; p<3; ++p)
for (int q=0; q<3; ++q)
for (int r=0; r<3; ++r)
{
double a = 0.0;
for (int i=0; i<3; ++i)
{
a += m_a1*(I(p,r)*G(i,q,i) + I(p,q)*G(i,i,r));
a += 0.5*m_a2*(2.0*I(q,r)*G(i,p,i) + G(q,i,i)*I(p,r) + G(r,i,i)*I(p,q));
a += 2.0*m_a3*G(p,i,i)*I(q,r);
}
a += 2.0*m_a4*G(p,q,r);
a += m_a5*(G(q,p,r) + G(r,p,q));
Q(p,q,r) = a;
}
}
//! Calculate material tangents
//! C = dP/dF
//! L = dP/dG
//! H = dQ/dF
//! J = dQ/dG
void FEMindlinElastic2O::Tangent(FEMaterialPoint& mp, tens4d& C, tens5d& L, tens5d& H, tens6d& J)
{
FEElasticMaterialPoint& pt = *mp.ExtractData<FEElasticMaterialPoint>();
FEElasticMaterialPoint2O& pt2O = *mp.ExtractData<FEElasticMaterialPoint2O>();
// deformation gradient and transpose
const mat3d& F = pt.m_F;
mat3d Ft = F.transpose();
// identity tensor
mat3dd I(1.0);
// Lagrange strain
mat3d E = (Ft*F - I)*0.5;
double trE = E.trace();
mat3d B = F*Ft;
// C-tensor
for (int i=0; i<3; ++i)
for (int j=0; j<3; ++j)
for (int k=0; k<3; ++k)
for (int l=0; l<3; ++l)
{
double c = 0.0;
c += I(i,k)*(m_lam*trE*I(j,l) + 2.0*m_mu*E(l,j));
c += m_lam*F(i,j)*F(k,l);
c += m_mu*(F(i,l)*F(k,j) + B(k,i)*I(j,l));
C(i,j,k,l) = c;
}
// L and H are zero
L.zero();
H.zero();
// J-tensor
for (int i=0; i<3; ++i)
for (int j=0; j<3; ++j)
for (int k=0; k<3; ++k)
for (int l=0; l<3; ++l)
for (int m=0; m<3; ++m)
for (int n=0; n<3; ++n)
{
double a = 0.0;
a += m_a1*(I(i,k)*I(l,n)*I(j,m) + I(i,j)*I(l,m)*I(k,n));
a += 0.5*m_a2*(2.0*I(j,k)*I(l,n)*I(i,m) + I(j,l)*I(m,n)*I(i,k) + I(i,j)*I(k,l)*I(m,n));
a += 2.0*m_a3*I(i,l)*I(m,n)*I(j,k);
a += 2.0*m_a4*I(i,l)*I(j,m)*I(k,n);
a += m_a5*(I(j,l)*I(i,m)*I(k,n) + I(k,l)*I(i,m)*I(j,n));
J(i,j,k,l,m,n) = a;
}
}
| C++ |
3D | febiosoftware/FEBio | FEBioRVE/FE2OMicroConstraint.h | .h | 3,254 | 103 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#pragma once
#include <FECore/FESurfaceConstraint.h>
#include <FECore/FESurface.h>
#include <FECore/tens3d.h>
#include <FEBioMech/FEElasticMaterial.h>
//-----------------------------------------------------------------------------
class FEMicroFlucSurface : public FESurface
{
public:
//! constructor
FEMicroFlucSurface(FEModel* fem);
//! Initialization
bool Init();
//! copy data
void CopyFrom(FEMicroFlucSurface& s);
public:
vec3d SurfMicrofluc();
public:
vec3d m_Lm; // Lagrange multipler microfluctuation
vec3d m_pv; // "Pressure" vector
vec3d m_c; // Microfluction across surface
mat3d m_Fm; // Macroscopic deformation gradient
tens3drs m_Gm; // Macroscopic deformation Hessian
};
//-----------------------------------------------------------------------------
// This class implements a constraint that tries to maintain the volume of the
// enclosed space using an isochoric pressure.
class FE2OMicroConstraint : public FESurfaceConstraint
{
public:
//! constructor
FE2OMicroConstraint(FEModel* pfem);
void Activate() override;
void LoadVector(FEGlobalVector& R, const FETimeInfo& tp) override;
void StiffnessMatrix(FELinearSystem& LS, const FETimeInfo& tp) override;
bool Augment(int naug, const FETimeInfo& tp) override;
void Serialize(DumpStream& ar) override;
void CopyFrom(FENLConstraint* plc) override;
// update state
void Reset() override;
void Update(const FETimeInfo& tp);
FESurface* GetSurface() override;
//! Unpack surface element data
void UnpackLM(FEElement& el, vector<int>& lm);
//! build connectivity for matrix profile
void BuildMatrixProfile(FEGlobalMatrix& M) override;
public:
FEMicroFlucSurface m_s; //!< the bounding surface
public:
double m_eps; //!< penalty parameter
double m_atol; //!< augmented Lagrangian tolerance
bool m_blaugon; //!< augmentation flag
private:
bool m_binit; //!< flag indicating whether the constraint is initialized
FEDofList m_dofU;
DECLARE_FECORE_CLASS();
};
| Unknown |
3D | febiosoftware/FEBio | FEBioRVE/FEMultiscaleDomainFactory.h | .h | 1,563 | 35 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#pragma once
#include "FECore/FECoreKernel.h"
//-----------------------------------------------------------------------------
class FEMultiScaleDomainFactory : public FEDomainFactory
{
public:
virtual FEDomain* CreateDomain(const FE_Element_Spec& spec, FEMesh* pm, FEMaterial* pmat);
};
| Unknown |
3D | febiosoftware/FEBio | FEBioRVE/FEMicroMaterial.cpp | .cpp | 11,179 | 356 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#include "stdafx.h"
#include "FEMicroMaterial.h"
#include "FECore/FEElemElemList.h"
#include "FECore/log.h"
#include "FEBioMech/FESolidSolver2.h"
#include "FEBioMech/FEElasticSolidDomain.h"
#include "FECore/FEAnalysis.h"
#include "FEBioXML/FEBioImport.h"
#include <FECore/mat6d.h>
#include "FEBioMech/FEBCPrescribedDeformation.h"
#include "FERVEProbe.h"
#include <sstream>
//=============================================================================
FEMicroMaterialPoint::FEMicroMaterialPoint()
{
m_macro_energy = 0.;
m_micro_energy = 0.;
m_energy_diff = 0.;
m_macro_energy_inc = 0.;
m_micro_energy_inc = 0.;
}
//-----------------------------------------------------------------------------
//! Initialize material point data
void FEMicroMaterialPoint::Init()
{
FEElasticMaterialPoint::Init();
m_F_prev.unit();
}
//-----------------------------------------------------------------------------
//! Initialize material point data
void FEMicroMaterialPoint::Update(const FETimeInfo& timeInfo)
{
FEElasticMaterialPoint::Update(timeInfo);
m_F_prev = m_F;
// clear rewind stack so the next rewind won't overwrite current state
m_rve.RCI_ClearRewindStack();
}
//-----------------------------------------------------------------------------
//! create a shallow copy
FEMaterialPointData* FEMicroMaterialPoint::Copy()
{
FEMicroMaterialPoint* pt = new FEMicroMaterialPoint();
if (m_pNext) pt->SetNext(m_pNext->Copy());
return pt;
}
//-----------------------------------------------------------------------------
//! serialize material point data
void FEMicroMaterialPoint::Serialize(DumpStream& ar)
{
FEMaterialPointData::Serialize(ar);
ar & m_S & m_F_prev;
ar & m_macro_energy;
ar & m_micro_energy;
ar & m_energy_diff;
ar & m_macro_energy_inc;
ar & m_micro_energy_inc;
}
//=============================================================================
//-----------------------------------------------------------------------------
// define the material parameters
BEGIN_FECORE_CLASS(FEMicroMaterial, FEElasticMaterial)
ADD_PARAMETER(m_szrve , "RVE" );
ADD_PARAMETER(m_szbc , "bc_set" );
ADD_PARAMETER(m_bctype , "rve_type" );
ADD_PARAMETER(m_scale , "scale" );
ADD_PROPERTY(m_probe, "probe", false);
END_FECORE_CLASS();
//-----------------------------------------------------------------------------
FEMicroMaterial::FEMicroMaterial(FEModel* pfem) : FEElasticMaterial(pfem)
{
// initialize parameters
m_szrve[0] = 0;
m_szbc[0] = 0;
m_bctype = FERVEModel::DISPLACEMENT; // use displacement BCs by default
m_scale = 1.0;
}
//-----------------------------------------------------------------------------
FEMicroMaterial::~FEMicroMaterial(void)
{
}
//-----------------------------------------------------------------------------
FEMaterialPointData* FEMicroMaterial::CreateMaterialPointData()
{
return new FEMicroMaterialPoint;
}
//-----------------------------------------------------------------------------
bool FEMicroMaterial::Init()
{
if (FEElasticMaterial::Init() == false) return false;
// load the RVE model
FEBioImport fim;
if (fim.Load(m_mrve, m_szrve.c_str()) == false)
{
feLogError("Failed to load RVE model.");
return false;
}
// set the parent FEM
m_mrve.SetParentModel(GetFEModel());
// We don't want to output anything from the RVE
m_mrve.BlockLog();
// scale the RVE
if (m_scale != 1.0) m_mrve.ScaleGeometry(m_scale);
// initialize the RVE model
// This also creates the necessary boundary conditions
bool bret = m_mrve.InitRVE(m_bctype, m_szbc.c_str());
if (bret == false) {
feLogError("An error occurred preparing RVE model"); return false;
}
return true;
}
//-----------------------------------------------------------------------------
// Note that this function is not used in the first-order implemenetation
mat3ds FEMicroMaterial::Stress(FEMaterialPoint &mp)
{
// get the deformation gradient
FEMicroMaterialPoint& pt = *mp.ExtractData<FEMicroMaterialPoint>();
mat3d F = pt.m_F;
// calculate the averaged Cauchy stress
mat3ds sa = pt.m_rve.StressAverage(F, mp);
// calculate the difference between the macro and micro energy for Hill-Mandel condition
pt.m_micro_energy = micro_energy(pt.m_rve);
return sa;
}
//-----------------------------------------------------------------------------
// The stiffness is evaluated at the same time the stress is evaluated so we
// can just return it here. Note that this assumes that the stress function
// is always called prior to the tangent function.
tens4ds FEMicroMaterial::Tangent(FEMaterialPoint &mp)
{
FEMicroMaterialPoint& mmpt = *mp.ExtractData<FEMicroMaterialPoint>();
return mmpt.m_rve.StiffnessAverage(mp);
}
//-----------------------------------------------------------------------------
//! Calculate the "energy" of the RVE model, i.e. the volume averaged of PK1:F
double FEMicroMaterial::micro_energy(FEModel& rve)
{
double E_avg = 0.0;
double V0 = 0.0;
FEMesh& m = rve.GetMesh();
for (int k=0; k<m.Domains(); ++k)
{
FESolidDomain& dom = static_cast<FESolidDomain&>(m.Domain(k));
for (int i=0; i<dom.Elements(); ++i)
{
FESolidElement& el = dom.Element(i);
int nint = el.GaussPoints();
double* w = el.GaussWeights();
for (int n=0; n<nint; ++n)
{
FEMaterialPoint& mp = *el.GetMaterialPoint(n);
FEElasticMaterialPoint& pt = *mp.ExtractData<FEElasticMaterialPoint>();
mat3d& F = pt.m_F;
double J = F.det();
mat3ds& s = pt.m_s; // Cauchy stress
// PK1 stress
mat3d Pk1 = J*s*F.transinv();
double energy = Pk1.dotdot(F);
double J0 = dom.detJ0(el, n);
E_avg += energy*w[n]*J0;
V0 += w[n]*J0;
}
}
}
return E_avg/V0;
}
//-----------------------------------------------------------------------------
//! Calculate the average stress from the RVE solution.
mat3d FEMicroMaterial::AveragedStressPK1(FEModel& rve, FEMaterialPoint &mp)
{
FEElasticMaterialPoint& pt = *mp.ExtractData<FEElasticMaterialPoint>();
mat3d F = pt.m_F;
double J = pt.m_J;
// get the RVE mesh
FEMesh& m = rve.GetMesh();
mat3d PK1; PK1.zero();
// for periodic BC's we take the reaction forces directly from the periodic constraints
if (m_bctype == FERVEModel::PERIODIC_AL)
{
// get the reaction for from the periodic constraints
for (int i=0; i<3; ++i)
{
FEPeriodicBoundary1O* pbc = dynamic_cast<FEPeriodicBoundary1O*>(rve.SurfacePairConstraint(i));
assert(pbc);
FEPeriodicSurface& ss = pbc->m_ss;
int N = ss.Nodes();
for (int i=0; i<N; ++i)
{
FENode& node = ss.Node(i);
vec3d f = ss.m_data[i].m_Fr;
// We multiply by two since the reaction forces are only stored at the primary surface
// and we also need to sum over the secondary nodes (NOTE: should I figure out a way to
// store the reaction forces on the secondary nodes as well?)
PK1 += (f & node.m_r0)*2.0;
}
}
}
// get the reaction force vector from the solid solver
// (We also need to do this for the periodic BC, since at the prescribed nodes,
// the contact forces will be zero).
const int dof_X = rve.GetDOFIndex("x");
const int dof_Y = rve.GetDOFIndex("y");
const int dof_Z = rve.GetDOFIndex("z");
FEAnalysis* pstep = rve.GetCurrentStep();
FESolidSolver2* ps = dynamic_cast<FESolidSolver2*>(pstep->GetFESolver());
assert(ps);
vector<double>& R = ps->m_Fr;
FEBCPrescribedDeformation& dc = dynamic_cast<FEBCPrescribedDeformation&>(*rve.BoundaryCondition(0));
const FENodeSet& nset = *dc.GetNodeSet();
int nitems = nset.Size();
for (int i=0; i<nitems; ++i)
{
const FENode& n = *nset.Node(i);
vec3d f;
f.x = R[-n.m_ID[dof_X]-2];
f.y = R[-n.m_ID[dof_Y]-2];
f.z = R[-n.m_ID[dof_Z]-2];
PK1 += f & n.m_r0;
}
double V0 = m_mrve.InitialVolume();
return PK1 / V0;
}
//-----------------------------------------------------------------------------
//! Calculate the average stress from the RVE solution.
mat3ds FEMicroMaterial::AveragedStressPK2(FEModel& rve, FEMaterialPoint &mp)
{
FEElasticMaterialPoint& pt = *mp.ExtractData<FEElasticMaterialPoint>();
mat3d F = pt.m_F;
double J = pt.m_J;
mat3d Finv = F.inverse();
// get the RVE mesh
FEMesh& m = rve.GetMesh();
mat3d S; S.zero();
// for periodic BC's we take the reaction forces directly from the periodic constraints
if (m_bctype == FERVEModel::PERIODIC_AL)
{
// get the reaction for from the periodic constraints
for (int i=0; i<3; ++i)
{
FEPeriodicBoundary1O* pbc = dynamic_cast<FEPeriodicBoundary1O*>(rve.SurfacePairConstraint(i));
assert(pbc);
FEPeriodicSurface& ss = pbc->m_ss;
int N = ss.Nodes();
for (int i=0; i<N; ++i)
{
FENode& node = ss.Node(i);
vec3d f = ss.m_data[i].m_Fr;
vec3d f0 = Finv*f;
// We multiply by two since the reaction forces are only stored at the primary surface
// and we also need to sum over the secondary nodes (NOTE: should I figure out a way to
// store the reaction forces on the secondary nodes as well?)
S += (f0 & node.m_r0)*2.0;
}
}
}
// get the reaction force vector from the solid solver
// (We also need to do this for the periodic BC, since at the prescribed nodes,
// the contact forces will be zero).
const int dof_X = rve.GetDOFIndex("x");
const int dof_Y = rve.GetDOFIndex("y");
const int dof_Z = rve.GetDOFIndex("z");
FEAnalysis* pstep = rve.GetCurrentStep();
FESolidSolver2* ps = dynamic_cast<FESolidSolver2*>(pstep->GetFESolver());
assert(ps);
vector<double>& R = ps->m_Fr;
FEBCPrescribedDeformation& dc = dynamic_cast<FEBCPrescribedDeformation&>(*rve.BoundaryCondition(0));
const FENodeSet& nset = *dc.GetNodeSet();
int nitems = nset.Size();
for (int i=0; i<nitems; ++i)
{
const FENode& n = *nset.Node(i);
vec3d f;
f.x = R[-n.m_ID[dof_X]-2];
f.y = R[-n.m_ID[dof_Y]-2];
f.z = R[-n.m_ID[dof_Z]-2];
vec3d f0 = Finv*f;
S += f0 & n.m_r0;
}
double V0 = m_mrve.InitialVolume();
return S.sym() / V0;
}
| C++ |
3D | febiosoftware/FEBio | FEBioRVE/FERVEModel2O.cpp | .cpp | 25,902 | 963 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#include "stdafx.h"
#include "FERVEModel2O.h"
#include "FECore/FESolidDomain.h"
#include "FECore/FEElemElemList.h"
#include <FECore/FEPrescribedDOF.h>
#include "FEBioMech/FEElasticMaterial.h"
#include "FEPeriodicBoundary2O.h"
#include "FECore/FEAnalysis.h"
#include "FEBioMech/FESolidSolver2.h"
#include "FEBioMech/FEElasticSolidDomain.h"
#include "FEBioMech/FEBCPrescribedDeformation.h"
#include "FEPeriodicLinearConstraint2O.h"
#include <FECore/FELinearConstraintManager.h>
#include <FECore/FECube.h>
#include <FECore/FEPointFunction.h>
#include <FECore/FELoadCurve.h>
//-----------------------------------------------------------------------------
FERVEModel2O::FERVEModel2O()
{
m_rveType = FERVEModel2O::DISPLACEMENT;
}
//-----------------------------------------------------------------------------
FERVEModel2O::~FERVEModel2O()
{
}
//-----------------------------------------------------------------------------
void FERVEModel2O::ScaleGeometry(double scale)
{
// get the mesh
FEMesh& mesh = GetMesh();
// calculate the center of mass first
vec3d rc(0, 0, 0);
for (int i = 0; i<mesh.Nodes(); ++i)
{
rc += mesh.Node(i).m_r0;
}
rc /= (double)mesh.Nodes();
// scale the nodal positions around the center
for (int i = 0; i<mesh.Nodes(); ++i)
{
FENode& node = mesh.Node(i);
vec3d r0 = node.m_r0;
node.m_r0 = rc + (r0 - rc)*scale;
vec3d rt = node.m_rt;
node.m_rt = rc + (rt - rc)*scale;
}
}
//-----------------------------------------------------------------------------
//! Initializes the RVE model and evaluates some useful quantities.
bool FERVEModel2O::InitRVE(int rveType, const char* szbc)
{
// make sure the RVE problem doesn't output anything to a plot file
GetCurrentStep()->SetPlotLevel(FE_PLOT_NEVER);
// Center the RVE about the origin.
// This also calculates the bounding box
CenterRVE();
// generate prescribed BCs
// TODO: Make this part of the RVE definition
m_rveType = rveType;
if (rveType == FERVEModel2O::DISPLACEMENT)
{
// find the boundary nodes
if ((szbc) && (szbc[0] != 0))
{
// get the RVE mesh
FEMesh& m = GetMesh();
// find the node set that defines the corner nodes
FENodeSet* pset = m.FindNodeSet(szbc);
if (pset == 0) return false;
FENodeSet& ns = *pset;
int NN = m.Nodes();
m_BN.assign(NN, 0);
for (int i = 0; i<pset->Size(); ++i) m_BN[ns[i]] = 1;
}
else FindBoundaryNodes(m_BN);
// prep displacement BC's
if (PrepDisplacementBC() == false) return false;
}
else if (rveType == FERVEModel2O::PERIODIC_LC)
{
if (PrepPeriodicLC() == false) return false;
}
/* else if (rveType == FERVEModel2O::PERIODIC_AL)
{
// prep periodic BC's
if (PrepPeriodicBC(szbc) == false) return false;
}
*/ else return false;
// initialize base class
if (FEModel::Init() == false) return false;
// calculate intial RVE volume
EvalInitialVolume();
return true;
}
//-----------------------------------------------------------------------------
//! Evaluates the initial volume of the RVE model.
//! This is called from FERVEModel2O::Init.
void FERVEModel2O::EvalInitialVolume()
{
m_V0 = 0;
FEMesh& m = GetMesh();
for (int k=0; k<m.Domains(); ++k)
{
FESolidDomain& dom = static_cast<FESolidDomain&>(m.Domain(k));
for (int i=0; i<dom.Elements(); ++i)
{
FESolidElement& el = dom.Element(i);
int nint = el.GaussPoints();
double* w = el.GaussWeights();
double ve = 0;
for (int n=0; n<nint; ++n)
{
FEElasticMaterialPoint& pt = *el.GetMaterialPoint(n)->ExtractData<FEElasticMaterialPoint>();
double J = dom.detJ0(el, n);
ve += J*w[n];
}
m_V0 += ve;
}
}
}
//-----------------------------------------------------------------------------
//! Centers the RVE around the origin.
void FERVEModel2O::CenterRVE()
{
FEMesh& mesh = GetMesh();
FENode& node = mesh.Node(0);
// setup bounding box
FEBoundingBox box(node.m_r0);
const int NN = mesh.Nodes();
for (int i=1; i<NN; ++i)
{
FENode& node = mesh.Node(i);
box.add(node.m_r0);
}
// get the center
vec3d c = box.center();
// recenter the RVE about the origin
for (int n=0; n<NN; ++n)
{
FENode& node = mesh.Node(n);
node.m_r0 -= c;
node.m_rt = node.m_r0;
}
// adjust bounding box
m_bb.translate(-c);
}
//-----------------------------------------------------------------------------
//! Find the boundary nodes of the RVE model
void FERVEModel2O::FindBoundaryNodes(vector<int>& BN)
{
// first we need to find all the boundary nodes
FEMesh& m = GetMesh();
int NN = m.Nodes();
BN.assign(NN, 0);
// create the element-element list
FEElemElemList EEL;
EEL.Create(&m);
double wx = m_bb.width()*0.5;
double wy = m_bb.height()*0.5;
double wz = m_bb.depth()*0.5;
// use the E-E list to tag all exterior nodes
int fn[FEElement::MAX_NODES], M = 0;
for (int k=0; k<m.Domains(); ++k)
{
FEDomain& dom = m.Domain(k);
for (int i=0; i<dom.Elements(); ++i, ++M)
{
FEElement& el = dom.ElementRef(i);
int nf = el.Faces();
for (int j=0; j<nf; ++j)
{
if (EEL.Neighbor(M, j) == 0)
{
// mark all nodes
int nn = el.GetFace(j, fn);
for (int k=0; k<nn; ++k)
{
FENode& node = m.Node(fn[k]);
if (fabs(node.m_r0.x) >= 0.999*wx) BN[fn[k]] = 1;
if (fabs(node.m_r0.y) >= 0.999*wy) BN[fn[k]] = 1;
if (fabs(node.m_r0.z) >= 0.999*wz) BN[fn[k]] = 1;
}
}
}
}
}
}
//-----------------------------------------------------------------------------
// Setup the displacement boundary conditions.
bool FERVEModel2O::PrepDisplacementBC()
{
FEMesh& m = GetMesh();
int N = m.Nodes();
// count the nr of exterior nodes
int NN = 0, i;
for (i=0; i<N; ++i) if (m_BN[i] == 1) ++NN;
assert(NN > 0);
// create a load curve
FELoadCurve* plc = new FELoadCurve(this);
plc->Add(0.0, 0.0);
plc->Add(1.0, 1.0);
AddLoadController(plc);
int NLC = LoadControllers() - 1;
// clear all BCs
ClearBoundaryConditions();
// we create the prescribed deformation BC
FEBCPrescribedDeformation2O* pdc = fecore_new<FEBCPrescribedDeformation2O>("prescribed deformation 2O", this);
AddBoundaryCondition(pdc);
// assign the boundary nodes
FENodeSet* nset = new FENodeSet(this);
int c = -1;
for (i = 0; i<N; ++i)
if (m_BN[i] == 1)
{
if (c == -1) { pdc->SetReferenceNode(m_BN[i]); c = 1; }
nset->Add(i);
}
pdc->SetNodeSet(nset);
return true;
}
//-----------------------------------------------------------------------------
bool FERVEModel2O::PrepPeriodicBC(const char* szbc)
{
// make sure the node set is valid
if ((szbc==0)||(szbc[0]==0)) return false;
// get the RVE mesh
FEMesh& m = GetMesh();
// find the node set that defines the corner nodes
FENodeSet* pset = m.FindNodeSet(szbc);
if (pset == 0) return false;
FENodeSet& ns = *pset;
// check the periodic constraints
int nc = SurfacePairConstraints();
if (nc != 3) return false;
for (int i=0; i<3; ++i)
{
FEPeriodicBoundary2O* pbc = dynamic_cast<FEPeriodicBoundary2O*>(SurfacePairConstraint(i));
if (pbc == 0) return false;
}
// create a load curve
FELoadCurve* plc = new FELoadCurve(this);
plc->Add(0.0, 0.0);
plc->Add(1.0, 1.0);
AddLoadController(plc);
int NLC = LoadControllers() - 1;
// create the DC's
ClearBoundaryConditions();
FEPrescribedDOF* pdc[3] = { 0 };
pdc[0] = new FEPrescribedDOF(this); pdc[0]->SetDOF(0); pdc[0]->SetScale(1.0, NLC);
pdc[1] = new FEPrescribedDOF(this); pdc[1]->SetDOF(1); pdc[1]->SetScale(1.0, NLC);
pdc[2] = new FEPrescribedDOF(this); pdc[2]->SetDOF(2); pdc[2]->SetScale(1.0, NLC);
AddBoundaryCondition(pdc[0]);
AddBoundaryCondition(pdc[1]);
AddBoundaryCondition(pdc[2]);
// assign nodes to BCs
pdc[0]->SetNodeSet(pset);
pdc[1]->SetNodeSet(pset);
pdc[2]->SetNodeSet(pset);
// create the boundary node flags
m_BN.assign(m.Nodes(), 0);
int N = ns.Size();
for (int i=0; i<N; ++i) m_BN[ns[i]] = 1;
return true;
}
//-----------------------------------------------------------------------------
bool FERVEModel2O::PrepPeriodicLC()
{
// clear all BCs, just to be sure
ClearBoundaryConditions();
// get the RVE mesh
FEMesh& m = GetMesh();
// Assuming this is a cube, build the cube data
FECube cube;
if (cube.Build(this) == false) return false;
// setup the linear constraints
FEPeriodicLinearConstraint2O lc;
lc.GenerateConstraints(this);
lc.AddNodeSetPair(cube.GetSurface(0)->GetNodeList(), cube.GetSurface(1)->GetNodeList());
lc.AddNodeSetPair(cube.GetSurface(2)->GetNodeList(), cube.GetSurface(3)->GetNodeList());
lc.AddNodeSetPair(cube.GetSurface(4)->GetNodeList(), cube.GetSurface(5)->GetNodeList());
// find the node set that defines the corner nodes
const FENodeSet& set = cube.GetCornerNodes();
// create a load curve
FELoadCurve* plc = new FELoadCurve(this);
plc->Add(0.0, 0.0);
plc->Add(1.0, 1.0);
AddLoadController(plc);
int NLC = LoadControllers() - 1;
// we create the prescribed deformation BC
FEBCPrescribedDeformation2O* pdc = fecore_new<FEBCPrescribedDeformation2O>("prescribed deformation 2O", this);
AddBoundaryCondition(pdc);
// assign nodes to BCs
pdc->SetReferenceNode(set[0]);
pdc->SetNodeSet(const_cast<FENodeSet*>(&set));
pdc->SetScale(1.0, NLC);
// create the boundary node flags
m_BN.assign(m.Nodes(), 0);
int N = set.Size();
for (int i = 0; i<N; ++i) m_BN[set[i]] = 1;
return true;
}
//=============================================================================
FEMicroModel2O::FEMicroModel2O()
{
m_rveType = FERVEModel2O::DISPLACEMENT;
m_V0 = 0.0;
m_BN = 0;
}
//-----------------------------------------------------------------------------
FEMicroModel2O::~FEMicroModel2O()
{
}
//-----------------------------------------------------------------------------
bool FEMicroModel2O::Init(FERVEModel2O& rve)
{
// copy the parent RVE
CopyFrom(rve);
// initialize model
if (FEModel::Init() == false) return false;
// copy some stuff from the parent RVE model
m_V0 = rve.InitialVolume();
m_rveType = rve.RVEType();
m_BN = &rve.BoundaryList();
if (m_BN == 0) return false;
return true;
}
//-----------------------------------------------------------------------------
//! Solve the RVE model
bool FEMicroModel2O::Solve(const mat3d& F, const tens3drs& G)
{
// update boundary conditions
UpdateBC(F, G);
// solve the model
return FEModel::Solve();
}
//-----------------------------------------------------------------------------
//! Assign the prescribed displacement to the boundary nodes.
void FEMicroModel2O::UpdateBC(const mat3d& F, const tens3drs& G)
{
// get the mesh
FEMesh& m = GetMesh();
// assign new DC's for the boundary nodes
FEBCPrescribedDeformation2O& bc = dynamic_cast<FEBCPrescribedDeformation2O&>(*BoundaryCondition(0));
bc.SetDeformationGradient(F);
bc.SetDeformationHessian(G);
if (m_rveType == FERVEModel2O::PERIODIC_AL)
{
// get the "displacement" component of the deformation gradient
mat3d U = F - mat3dd(1);
// set the offset for the periodic BC's
vec3d r[FEElement::MAX_NODES];
// loop over periodic boundaries
for (int i=0; i<3; ++i)
{
FEPeriodicBoundary2O* pc = dynamic_cast<FEPeriodicBoundary2O*>(SurfacePairConstraint(i));
assert(pc);
pc->m_Fmacro = F;
pc->m_Gmacro = G;
}
}
else if (m_rveType == FERVEModel2O::PERIODIC_LC)
{
// get the linear constraint manager
FELinearConstraintManager& LM = GetLinearConstraintManager();
mat3d U = F - mat3dd(1.0);
// loop over all the linear constraints
const int NL = LM.LinearConstraints();
for (int i=0; i<NL; ++i)
{
FELinearConstraint& lc = LM.LinearConstraint(i);
FENode& parentNode = m.Node(lc.GetParentNode());
FENode& childNode = m.Node(lc.GetChildDof(0).node);
vec3d Xp = parentNode.m_r0;
vec3d Xm = childNode.m_r0;
mat3ds XXp = dyad(Xp);
mat3ds XXm = dyad(Xm);
vec3d d = U*(Xp - Xm) + (G.contract2s(XXp - XXm))*0.5;
switch (lc.GetParentDof())
{
case 0: lc.SetOffset(d.x); break;
case 1: lc.SetOffset(d.y); break;
case 2: lc.SetOffset(d.z); break;
}
}
}
}
//-----------------------------------------------------------------------------
//! Calculate the average stress from the RVE solution.
mat3d FEMicroModel2O::AveragedStressPK1(FEMaterialPoint &mp)
{
FEElasticMaterialPoint& pt = *mp.ExtractData<FEElasticMaterialPoint>();
mat3d F = pt.m_F;
double J = pt.m_J;
// get the RVE mesh
FEMesh& m = GetMesh();
mat3d PK1; PK1.zero();
// for periodic BC's we take the reaction forces directly from the periodic constraints
if (m_rveType == FERVEModel2O::PERIODIC_AL)
{
// get the reaction for from the periodic constraints
for (int i=0; i<3; ++i)
{
FEPeriodicBoundary2O* pbc = dynamic_cast<FEPeriodicBoundary2O*>(SurfacePairConstraint(i));
assert(pbc);
FEPeriodicSurface& ss = pbc->m_ss;
int N = ss.Nodes();
for (int i=0; i<N; ++i)
{
FENode& node = ss.Node(i);
vec3d f = ss.m_data[i].m_Fr;
// We multiply by two since the reaction forces are only stored at the primary surface
// and we also need to sum over the secondary nodes (NOTE: should I figure out a way to
// store the reaction forces on the secondary nodes as well?)
PK1 += (f & node.m_r0)*2.0;
}
}
}
// get the reaction force vector from the solid solver
// (We also need to do this for the periodic BC, since at the prescribed nodes,
// the contact forces will be zero).
const int dof_X = GetDOFIndex("x");
const int dof_Y = GetDOFIndex("y");
const int dof_Z = GetDOFIndex("z");
FEAnalysis* pstep = GetCurrentStep();
FESolidSolver2* ps = dynamic_cast<FESolidSolver2*>(pstep->GetFESolver());
assert(ps);
vector<double>& R = ps->m_Fr;
FEBCPrescribedDeformation2O& dc = dynamic_cast<FEBCPrescribedDeformation2O&>(*BoundaryCondition(0));
const FENodeSet& nset = *dc.GetNodeSet();
int nitems = nset.Size();
for (int i=0; i<nitems; ++i)
{
const FENode& n = *nset.Node(i);
vec3d f;
f.x = R[-n.m_ID[dof_X]-2];
f.y = R[-n.m_ID[dof_Y]-2];
f.z = R[-n.m_ID[dof_Z]-2];
PK1 += f & n.m_r0;
}
return PK1 / m_V0;
}
//-----------------------------------------------------------------------------
//! Calculate the average stress from the RVE solution.
void FEMicroModel2O::AveragedStress2O(mat3d& Pa, tens3drs& Qa)
{
// get the RVE mesh
FEMesh& m = GetMesh();
// calculate average PK1 stress
Pa.zero();
Qa.zero();
for (int i = 0; i<m.Domains(); ++i)
{
FESolidDomain& dom = dynamic_cast<FESolidDomain&>(m.Domain(i));
int NE = dom.Elements();
for (int j = 0; j<NE; ++j)
{
FESolidElement& el = dom.Element(j);
int ni = el.GaussPoints();
int ne = el.Nodes();
double* w = el.GaussWeights();
for (int n = 0; n<ni; ++n)
{
FEMaterialPoint& pt = *el.GetMaterialPoint(n);
FEElasticMaterialPoint& ep = *pt.ExtractData<FEElasticMaterialPoint>();
// calculate Jacobian
double Jn = dom.detJt(el, n);
// get the Cauchy stress
mat3ds& s = ep.m_s;
// convert to PK1
mat3d& F = ep.m_F;
double J = ep.m_J;
mat3d Pn = (s*F.transinv())*J;
// add it all up
Pa += Pn*(w[n] * Jn);
// now do the second order stress
vec3d X = pt.m_r0;
tens3drs Qn = dyad3rs(Pn, X);
// add it all up
// Qa += Qn*(w[n] * Jn);
}
}
}
Pa /= m_V0;
Qa /= m_V0;
}
/*
//-----------------------------------------------------------------------------
//! Calculate the average stress from the RVE solution.
void FEMicroModel2O::AveragedStress2O(mat3d& Pa, tens3drs& Qa)
{
// get the RVE mesh
FEMesh& m = GetMesh();
Pa.zero();
Qa.zero();
// for periodic BC's we take the reaction forces directly from the periodic constraints
if (m_rveType == FERVEModel2O::PERIODIC_AL)
{
// get the reaction for from the periodic constraints
for (int i=0; i<3; ++i)
{
FEPeriodicBoundary2O* pbc = dynamic_cast<FEPeriodicBoundary2O*>(SurfacePairInteraction(i));
assert(pbc);
FEPeriodicSurface& ss = pbc->m_ss;
int N = ss.Nodes();
for (int i=0; i<N; ++i)
{
FENode& node = ss.Node(i);
vec3d f = ss.m_Fr[i];
const vec3d& X = node.m_r0;
// We multiply by two since the reaction forces are only stored at the primary surface
// and we also need to sum over the secondary nodes (NOTE: should I figure out a way to
// store the reaction forces on the secondary nodes as well?)
Pa += (f & X)*2.0;
Qa += dyad3rs(f, X)*2.0;
}
}
}
// get the reaction force vector from the solid solver
// (We also need to do this for the periodic BC, since at the prescribed nodes,
// the contact forces will be zero).
FEBCPrescribedDeformation2O& dc = dynamic_cast<FEBCPrescribedDeformation2O&>(*PrescribedBC(0));
const int dof_X = GetDOFIndex("x");
const int dof_Y = GetDOFIndex("y");
const int dof_Z = GetDOFIndex("z");
FEAnalysis* pstep = GetCurrentStep();
FESolidSolver2* ps = dynamic_cast<FESolidSolver2*>(pstep->GetFESolver());
assert(ps);
vector<double>& R = ps->m_Fr;
int N = dc.Items();
for (int i=0; i<N; ++i)
{
FENode& n = m.Node(dc.NodeID(i));
vec3d f;
f.x = R[-n.m_ID[dof_X]-2];
f.y = R[-n.m_ID[dof_Y]-2];
f.z = R[-n.m_ID[dof_Z]-2];
const vec3d& X = n.m_r0;
Pa += (f & X);
Qa += dyad3rs(f, X);
}
// divide by volume
Pa /= m_V0;
Qa /= 2*m_V0;
}
*/
/*
//-----------------------------------------------------------------------------
//! Calculate the average stress from the RVE solution.
void FEMicroModel2O::AveragedStress2OPK1(FEMaterialPoint &mp, mat3d &PK1a, tens3drs &QK1a)
{
FEElasticMaterialPoint& pt = *mp.ExtractData<FEElasticMaterialPoint>();
mat3d F = pt.m_F;
double J = pt.m_J;
// get the RVE mesh
FEMesh& m = GetMesh();
mat3d PK1; PK1.zero();
tens3drs QK1; QK1.zero();
// for periodic BC's we take the reaction forces directly from the periodic constraints
if (m_bperiodic)
{
// get the reaction for from the periodic constraints
for (int i=0; i<3; ++i)
{
FEPeriodicBoundary2O* pbc = dynamic_cast<FEPeriodicBoundary2O*>(SurfacePairInteraction(i));
assert(pbc);
FEPeriodicSurface& ss = pbc->m_ss;
int N = ss.Nodes();
for (int i=0; i<N; ++i)
{
FENode& node = ss.Node(i);
vec3d f = ss.m_Fr[i];
// We multiply by two since the reaction forces are only stored at the primary surface
// and we also need to sum over the secondary nodes (NOTE: should I figure out a way to
// store the reaction forces on the secondary nodes as well?)
PK1 += (f & node.m_r0)*2.0;
vec3d X = node.m_r0;
QK1 += dyad3rs(f, X)*2.0;
}
}
}
// get the reaction force vector from the solid solver
// (We also need to do this for the periodic BC, since at the prescribed nodes,
// the contact forces will be zero).
FEPrescribedBC& dx = *PrescribedBC(0);
FEPrescribedBC& dy = *PrescribedBC(1);
FEPrescribedBC& dz = *PrescribedBC(2);
const int dof_X = GetDOFIndex("x");
const int dof_Y = GetDOFIndex("y");
const int dof_Z = GetDOFIndex("z");
FEAnalysis* pstep = GetCurrentStep();
FESolidSolver2* ps = dynamic_cast<FESolidSolver2*>(pstep->GetFESolver());
assert(ps);
vector<double>& R = ps->m_Fr;
int N = dx.Items();
for (int i=0; i<N; ++i)
{
FENode& n = m.Node(dx.NodeID(i));
vec3d f;
f.x = R[-n.m_ID[dof_X]-2];
f.y = R[-n.m_ID[dof_Y]-2];
f.z = R[-n.m_ID[dof_Z]-2];
PK1 += f & n.m_r0;
vec3d X; X = n.m_r0;
QK1 += dyad3rs(f, X);
}
PK1a = PK1 / m_V0;
QK1a = QK1 / (2*m_V0);
}
//-----------------------------------------------------------------------------
//! Calculate the average stress from the RVE solution.
void FEMicroModel2O::AveragedStress2OPK2(FEMaterialPoint &mp, mat3ds &Sa, tens3ds &Ta)
{
FEElasticMaterialPoint& pt = *mp.ExtractData<FEElasticMaterialPoint>();
mat3d F = pt.m_F;
double J = pt.m_J;
mat3d Finv = F.inverse();
// get the RVE mesh
FEMesh& m = GetMesh();
mat3d S; S.zero();
tens3ds T; T.zero();
// for periodic BC's we take the reaction forces directly from the periodic constraints
if (m_bperiodic)
{
// get the reaction for from the periodic constraints
for (int i=0; i<3; ++i)
{
FEPeriodicBoundary2O* pbc = dynamic_cast<FEPeriodicBoundary2O*>(SurfacePairInteraction(i));
assert(pbc);
FEPeriodicSurface& ss = pbc->m_ss;
int N = ss.Nodes();
for (int i=0; i<N; ++i)
{
FENode& node = ss.Node(i);
vec3d f = ss.m_Fr[i];
vec3d f0 = Finv*f;
// We multiply by two since the reaction forces are only stored at the primary surface
// and we also need to sum over the secondary nodes (NOTE: should I figure out a way to
// store the reaction forces on the secondary nodes as well?)
S += (f0 & node.m_r0)*2.0;
vec3d X = node.m_r0;
T += dyad3s(X, f0, X)*2.0;
}
}
}
// get the reaction force vector from the solid solver
// (We also need to do this for the periodic BC, since at the prescribed nodes,
// the contact forces will be zero).
FEPrescribedBC& dx = *PrescribedBC(0);
FEPrescribedBC& dy = *PrescribedBC(1);
FEPrescribedBC& dz = *PrescribedBC(2);
const int dof_X = GetDOFIndex("x");
const int dof_Y = GetDOFIndex("y");
const int dof_Z = GetDOFIndex("z");
FEAnalysis* pstep = GetCurrentStep();
FESolidSolver2* ps = dynamic_cast<FESolidSolver2*>(pstep->GetFESolver());
assert(ps);
vector<double>& R = ps->m_Fr;
int N = dx.Items();
for (int i=0; i<N; ++i)
{
FENode& n = m.Node(dx.NodeID(i));
vec3d f;
f.x = R[-n.m_ID[dof_X]-2];
f.y = R[-n.m_ID[dof_Y]-2];
f.z = R[-n.m_ID[dof_Z]-2];
vec3d f0 = Finv*f;
S += f0 & n.m_r0;
vec3d X = n.m_r0;
T += dyad3s(X, f0, X);
}
Sa = S.sym() / m_V0;
Ta = T / (2*m_V0);
}
*/
//-----------------------------------------------------------------------------
//! Calculate the average stiffness from the RVE solution.
void FEMicroModel2O::AveragedStiffness(FEMaterialPoint &mp, tens4d& C, tens5d& L, tens5d& H, tens6d& J)
{
FEElasticMaterialPoint& pt = *mp.ExtractData<FEElasticMaterialPoint>();
// get the mesh
FEMesh& m = GetMesh();
// get the solver
FEAnalysis* pstep = GetCurrentStep();
FESolidSolver2* ps = dynamic_cast<FESolidSolver2*>(pstep->GetFESolver());
// the element's stiffness matrix
matrix ke;
// element's residual
vector<double> fe;
// calculate the center point
vec3d rc(0,0,0);
for (int k=0; k<m.Nodes(); ++k) rc += m.Node(k).m_r0;
rc /= (double) m.Nodes();
// zero the stiffness components
C.zero();
L.zero();
H.zero();
J.zero();
// calculate the stiffness matrix and residual
for (int nd=0; nd<m.Domains(); ++nd)
{
FEElasticSolidDomain& bd = dynamic_cast<FEElasticSolidDomain&>(m.Domain(nd));
int NEL = bd.Elements();
for (int ne=0; ne<NEL; ++ne)
{
FESolidElement& el = bd.Element(ne);
// create the element's stiffness matrix
int neln = el.Nodes();
int ndof = 3*neln;
ke.resize(ndof, ndof);
ke.zero();
// calculate the element's stiffness matrix
bd.ElementStiffness(GetTime(), ne, ke);
// create the element's residual
fe.assign(ndof, 0);
// calculate the element's residual
bd.ElementInternalForce(el, fe);
// loop over the element's nodes
for (int a=0; a<neln; ++a)
{
FENode& na = m.Node(el.m_node[a]);
for (int b=0; b<neln; ++b)
{
FENode& nb = m.Node(el.m_node[b]);
if (IsBoundaryNode(el.m_node[a]) && IsBoundaryNode(el.m_node[b]))
{
// both nodes are boundary nodes
// so grab the element's submatrix
mat3d K;
ke.get(3*a, 3*b, K);
// get the nodal positions relative to the center
vec3d ra = na.m_r0 - rc;
vec3d rb = nb.m_r0 - rc;
double Ra[3] = { ra.x, ra.y, ra.z };
double Rb[3] = { rb.x, rb.y, rb.z };
// create the elasticity tensors
for (int i=0; i<3; ++i)
for (int j=0; j<3; ++j)
for (int k=0; k<3; ++k)
for (int l=0; l<3; ++l)
{
C(i, j, k, l) += K[i][k]*Rb[l]*Ra[j];
}
for (int i=0; i<3; ++i)
for (int j=0; j<3; ++j)
for (int k=0; k<3; ++k)
for (int l=0; l<3; ++l)
for (int m=0; m<3; ++m)
{
L(i, j, k, l, m) += K[i][k]*Rb[l]*Rb[m]*Ra[j];
}
for (int i=0; i<3; ++i)
for (int j=0; j<3; ++j)
for (int k=0; k<3; ++k)
for (int l=0; l<3; ++l)
for (int m=0; m<3; ++m)
{
H(i, j, k, l, m) += K[i][l]*Rb[m]*Ra[j]*Ra[k];
}
for (int i=0; i<3; ++i)
for (int j=0; j<3; ++j)
for (int k=0; k<3; ++k)
for (int l=0; l<3; ++l)
for (int m=0; m<3; ++m)
for (int n=0; n<3; ++n)
{
J(i, j, k, l, m, n) += K[i][l]*Rb[m]*Rb[n]*Ra[j]*Ra[k];
}
}
}
}
}
}
// divide by volume
C /= m_V0;
L /= m_V0;
H /= 2.0*m_V0;
J /= 2.0*m_V0;
}
| C++ |
3D | febiosoftware/FEBio | FEBioRVE/FEMicroMaterial2O.h | .h | 3,371 | 101 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#pragma once
#include "FEElasticMaterial2O.h"
#include <FECore/tens3d.h>
#include <FECore/tens4d.h>
#include <FECore/tens5d.h>
#include <FECore/tens6d.h>
#include "FE2OMicroConstraint.h"
#include "FEMicroMaterial.h"
#include "FERVEModel2O.h"
//-----------------------------------------------------------------------------
//! Material point class for the micro-material
class FEMicroMaterialPoint2O : public FEMaterialPointData
{
public:
//! constructor
FEMicroMaterialPoint2O(FEMaterialPointData* mp);
//! create a shallow copy
FEMaterialPointData* Copy();
//! serialize material point data
void Serialize(DumpStream& ar);
public:
FEMicroModel2O m_rve; //!< local copy of the rve
int m_elem_id; //!< element ID
int m_gpt_id; //!< Gauss point index (0-based)
};
//-----------------------------------------------------------------------------
//! The micro-material implements material homogenization. The stress and tangents
//! are calculated by solving a micro-structural RVE problem and return the
//! averaged stress and condensed tangents.
//!
class FEMicroMaterial2O : public FEElasticMaterial2O
{
public:
FEMicroMaterial2O(FEModel* pfem);
~FEMicroMaterial2O(void);
public:
std::string m_szrve; //!< filename for RVE file
std::string m_szbc; //!< name of nodeset defining boundary
int m_rveType; //!< RVE type
double m_scale; //!< geometry scale factor
FERVEModel2O m_mrve; //!< the parent RVE (Representive Volume Element)
public:
//! calculate stress at material point
void Stress(FEMaterialPoint& mp, mat3d& P, tens3drs& Q) override;
//! calculate tangent stiffness at material point
void Tangent(FEMaterialPoint &mp, tens4d& C, tens5d& L, tens5d& H, tens6d& J) override;
//! data initialization
bool Init() override;
//! create material point data
FEMaterialPointData* CreateMaterialPointData() override;
public:
int Probes() { return (int) m_probe.size(); }
FERVEProbe& Probe(int i) { return *m_probe[i]; }
protected:
std::vector<FERVEProbe*> m_probe;
public:
// declare the parameter list
DECLARE_FECORE_CLASS();
};
| Unknown |
3D | febiosoftware/FEBio | FEBioRVE/FEElasticSolidDomain2O.h | .h | 5,373 | 153 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#pragma once
#include "FEBioMech/FEElasticSolidDomain.h"
#include <FECore/tens3d.h>
#include <FECore/tens4d.h>
#include <FECore/tens5d.h>
#include <FECore/tens6d.h>
#include <FECore/FESurface.h>
//-----------------------------------------------------------------------------
// forward declarations
class FEModel;
class FESurface;
//-----------------------------------------------------------------------------
// This class implements a discontinuous-Galerkin formulation for gradient elasticity
class FEElasticSolidDomain2O : public FEElasticSolidDomain
{
protected:
// Helper class for evaluating the discrete-Galerkin contribution
// It stores the data needed for evaluating the integrals over the
// internal surface.
class FEInternalSurface2O
{
public:
struct Data
{
FEMaterialPoint* m_pt[2]; //!< material point data for evaluating stresses
vec3d ksi[2]; //!< local element coordinates
tens3drs Qavg; //!< average stress across interface
tens5d H[2]; //!< H stiffness
tens6d J[2]; //!< J stiffness
tens6d J0[2]; //!< initial J stiffness
tens5d H0[2]; //!< initial H stiffness
tens6d J0avg; //!< average initial higher order stiffess across interface
mat3d DgradU; //!< displacement gradient jump across interface
};
public:
FEInternalSurface2O ();
// initialize the data structure
bool Initialize(FEElasticSolidDomain2O* dom);
int Elements() const { return m_ps->Elements(); }
FESurfaceElement& Element(int i) { return m_ps->Element(i); }
Data& GetData(int i) { return m_data[i]; }
FESurface* GetSurface() { return m_ps; }
double GetElementSize() const { return m_h; }
private:
FESurface* m_ps;
vector<Data> m_data;
double m_h; //!< element size
};
public:
//! constructor
FEElasticSolidDomain2O(FEModel* pfem);
//! initialize class
bool Init() override;
//! initialize elements
void PreSolveUpdate(const FETimeInfo& timeInfo) override;
//! build the matrix profile
//! (overridden from FEDomain)
void BuildMatrixProfile(FEGlobalMatrix& M) override;
//! overridden from FEElasticSolidDomain
void Update(const FETimeInfo& tp) override;
public:
//! internal stress forces
void InternalForces(FEGlobalVector& R) override;
//! evaluate internal element forces
void ElementInternalForce(FESolidElement& el, vector<double>& fe);
//! calculates the global stiffness matrix for this domain
//! (overridden from FEElasticSolidDomain)
void StiffnessMatrix(FELinearSystem& LS) override;
protected:
// discontinuous-Galerkin contribution to residual
void InternalForcesDG1(FEGlobalVector& R);
void InternalForcesDG2(FEGlobalVector& R);
void InternalForcesDG3(FEGlobalVector& R);
void ElementInternalForce_PF(FESolidElement& el, vector<double>& fe);
void ElementInternalForce_QG(FESolidElement& el, vector<double>& fe);
// --- S T I F F N E S S ---
//! calculates the solid element stiffness matrix
void ElementStiffness(const FETimeInfo& tp, int iel, matrix& ke) override;
//! contributions from discontinuous Galerkin formulation
void StiffnessMatrixDG(FELinearSystem& LS);
void ElementStiffnessMatrixDG1(FESurfaceElement& el, FEInternalSurface2O::Data* pdata, matrix& ke);
void ElementStiffnessMatrixDG2(FESurfaceElement& el, FEInternalSurface2O::Data* pdata, matrix& ke);
void ElementStiffnessMatrixDG3(FESurfaceElement& el, FEInternalSurface2O::Data* pdata, matrix& ke);
private:
void UpdateElementStress(int iel, const FETimeInfo& tp) override;
void UpdateInternalSurfaceStresses();
void UpdateKinematics();
public:
// calculate gradient of deformation gradient
void defhess(FESolidElement &el, int n, tens3drs &G);
void defhess(FESolidElement &el, double r, double s, double t, tens3drs &G);
// Calculates second derivative of shape function N[node]
void shape_gradient2(const FESolidElement& el, vec3d* X, int n, mat3d* H);
void shape_gradient2(const FESolidElement& el, vec3d* X, double r, double s, double t, mat3d* H);
protected:
FEInternalSurface2O m_surf;
bool m_binitJ0; //!< flag indicating J0 has been initialized
};
| Unknown |
3D | febiosoftware/FEBio | FEBioRVE/FEMicroMaterial.h | .h | 4,115 | 122 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#pragma once
#include "FEBioMech/FEElasticMaterial.h"
#include "FECore/FEModel.h"
#include "FECore/FEMaterial.h"
#include "FEPeriodicBoundary1O.h"
#include "FECore/FECallBack.h"
#include "FERVEModel.h"
#include "febiorve_api.h"
class FERVEProbe;
//-----------------------------------------------------------------------------
//! Material point class for the micro-material
class FEBIORVE_API FEMicroMaterialPoint : public FEElasticMaterialPoint
{
public:
//! constructor
FEMicroMaterialPoint();
//! Initialize material point data
void Init();
//! Update material point data
void Update(const FETimeInfo& timeInfo);
//! create a shallow copy
FEMaterialPointData* Copy();
//! serialize material point data
void Serialize(DumpStream& ar);
public:
mat3ds m_S; // 2nd Piola-Kirchhoff stress
mat3d m_F_prev; // deformation gradient from last time step
double m_macro_energy; // Macroscopic strain energy
double m_micro_energy; // Volume-average of strain energy throughout the RVE solution
double m_energy_diff; // Difference between macro energy and volume averaged energy of RVE (should be zero)
double m_macro_energy_inc; // Macroscopic strain energy increment
double m_micro_energy_inc; // Microscopic strain energy increment
FERVEModel m_rve; // Local copy of the parent rve
};
//-----------------------------------------------------------------------------
//! The micro-material implements material homogenization. The stress and tangents
//! are calculated by solving a micro-structural RVE problem and return the
//! averaged stress and condensed tangents.
//!
class FEMicroMaterial : public FEElasticMaterial
{
public:
FEMicroMaterial(FEModel* pfem);
~FEMicroMaterial(void);
public:
std::string m_szrve; //!< filename for RVE file
std::string m_szbc; //!< name of nodeset defining boundary
int m_bctype; //!< periodic bc flag
double m_scale; //!< RVE scale factor
FERVEModel m_mrve; //!< the parent RVE (Representive Volume Element)
public:
//! calculate stress at material point
virtual mat3ds Stress(FEMaterialPoint& pt) override;
//! calculate tangent stiffness at material point
virtual tens4ds Tangent(FEMaterialPoint& pt) override;
//! data initialization
bool Init() override;
//! create material point data
FEMaterialPointData* CreateMaterialPointData() override;
// calculate the average PK1 stress
mat3d AveragedStressPK1(FEModel& rve, FEMaterialPoint &mp);
// calculate the average PK2 stress
mat3ds AveragedStressPK2(FEModel& rve, FEMaterialPoint &mp);
// average RVE energy
double micro_energy(FEModel& rve);
public:
int Probes() { return (int) m_probe.size(); }
FERVEProbe& Probe(int i) { return *m_probe[i]; }
protected:
std::vector<FERVEProbe*> m_probe;
public:
// declare the parameter list
DECLARE_FECORE_CLASS();
};
| Unknown |
3D | febiosoftware/FEBio | FEBioRVE/FEMindlinElastic2O.h | .h | 1,864 | 58 | /*This file is part of the FEBio source code and is licensed under the MIT license
listed below.
See Copyright-FEBio.txt for details.
Copyright (c) 2021 University of Utah, The Trustees of Columbia University in
the City of New York, and others.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.*/
#pragma once
#include "FEElasticMaterial2O.h"
class FEMindlinElastic2O : public FEElasticMaterial2O
{
public:
FEMindlinElastic2O(FEModel* pfem);
//! Calculate PK1 stress and higher order stress Q
void Stress(FEMaterialPoint& mp, mat3d& P, tens3drs& Q) override;
//! Calculate material tangents
//! C = dP/dF
//! L = dP/dG
//! H = dQ/dF
//! J = dQ/dG
void Tangent(FEMaterialPoint& mp, tens4d& C, tens5d& L, tens5d& H, tens6d& J) override;
public:
double m_lam;
double m_mu;
double m_a1;
double m_a2;
double m_a3;
double m_a4;
double m_a5;
DECLARE_FECORE_CLASS();
};
| Unknown |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.